CN107850048B

CN107850048B - Method and system for generating a wind turbine control arrangement

Info

Publication number: CN107850048B
Application number: CN201680038986.9A
Authority: CN
Inventors: C·斯普鲁斯; C·比雷迪
Original assignee: Vestas Wind Systems AS
Current assignee: Vestas Wind Systems AS
Priority date: 2015-06-30
Filing date: 2016-06-21
Publication date: 2020-02-07
Anticipated expiration: 2036-06-21
Also published as: ES2821958T3; CN107850048A; WO2017000951A1

Abstract

There is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the method comprising: determining a value indicative of a current remaining fatigue life of the turbine or one or more turbine components based on the measured wind turbine site data and/or operational data; applying an optimization function that changes an initial control schedule to determine an optimized control schedule by changing a tradeoff between fatigue life consumed by the turbine or one or more turbine components and energy capture until an optimized control schedule is determined, the optimization comprising: estimating a future fatigue life consumed by the turbine or turbine component during the altered control schedule based on the current remaining fatigue life and the altered control schedule; and constraining optimization of the control schedule in accordance with one or more input constraints; wherein the input constraints comprise a maximum allowable number of component replacements for one or more turbine components, and the optimizing further comprises changing an initial value of wind turbine life to determine a target wind turbine life.

Description

Method and system for generating a wind turbine control arrangement

Technical Field

Embodiments of the invention relate to methods and systems for determining a control schedule for a wind turbine power output.

Background

FIG. 1A illustrates a large conventional wind turbine 1 known in the art, which includes a tower 10 and a wind turbine nacelle 20 positioned atop the tower 10. The wind turbine rotor 30 comprises three wind turbine blades 32, each having a length L. The wind turbine rotor 30 may comprise a further number of blades 32, for example one, two, four, five or more. The blades 32 are mounted on a hub 34 at a height H above the base of the tower. The hub 34 is connected to the nacelle 20 by a low speed shaft (not shown) extending from the front of the nacelle 20. The low speed shaft drives a gearbox (not shown) that steps up the rotational speed and in turn drives a generator within the nacelle 20 for converting the energy extracted from the wind by the rotating blades 32 into an electrical power output. The wind turbine blades 32 define a swept area a, which is the area of the circle traced by the rotating blades 32. The swept area indicates how much of a given air mass the wind turbine 1 intercepts and, therefore, affects the power output of the wind turbine 1 as well as the forces and bending moments experienced by the components of the turbine 1 during operation. The turbine may be onshore, as shown, or offshore. In the latter case, the tower would be connected to a mono pile, tripod, lattice or other foundation structure, which may be fixed or floating.

For example, each wind turbine has a wind turbine controller, which may be located at the base or top of the tower. The wind turbine controller processes inputs from the sensors and other control systems and generates output signals for actuators such as pitch actuators, generator torque controllers, generator contactors, switches for activating shaft actuators, yaw motors, and the like.

FIG. 1B schematically shows an example of a conventional wind power plant 100 comprising a plurality of wind turbines 110, the controller of each wind turbine 110 being in communication with a Power Plant Controller (PPC) 130. The PPC 130 may communicate bi-directionally with each turbine. The turbine outputs power to the grid connection point 140 as indicated by the thick line 150. In operation, and assuming wind conditions permit, each wind turbine 110 will output maximum active power up to its rated power as specified by the manufacturer.

FIG. 2 illustrates a conventional power curve 55 for a wind turbine plotting wind speed on the x-axis versus power output on the y-axis. Curve 55 is the normal power curve for the wind turbine and defines the power output of the wind turbine generator as a function of wind speed. As is well known in the art, a wind turbine cuts into a wind speed V_minPower generation is started. The turbine is then operated at part load (also referred to as part load) until at V_RThe point reaches the rated wind speed. At rated wind speed, the rated (or nominal) generator power is reached and the turbine is operating at full load. For example, the cut-in wind speed in a typical wind turbine may be 3m/s and the rated wind speed may be 12 m/s. V_maxThe point is the cut-out wind speed, which is the highest wind speed at which the wind turbine can operate when delivering power. In case the wind speed is equal to or higher than the cut-out wind speed, the wind turbine is shut down for safety reasons, in particular to reduce the loads acting on the wind turbine. Alternatively, the power output may be ramped down to zero power depending on the wind speed.

The power rating of a wind turbine is defined in IEC 61400 as the maximum continuous electrical power output that the wind turbine is designed to achieve under normal operating and external conditions. Large commercial wind turbines are typically designed to have a design life of 20 to 25 years and are designed to operate at rated power so as not to exceed the design loads and fatigue life of the components.

The rate of fatigue damage accumulation for each component in a wind turbine varies greatly under different operating conditions. As the generated power increases, the wear rate or damage accumulation tends to increase. Wind conditions also affect the rate of accumulation of damage. For some mechanical components, operating in very high turbulence can result in many times higher fatigue damage accumulation rates than operating in normal turbulence. For some electrical components, operating at very high temperatures (which may be caused by high ambient temperatures) can result in a fatigue damage accumulation rate (e.g., dielectric breakdown rate) that is many times higher than operating at ambient temperatures. For example, the rule of thumb for a generator winding is that the winding temperature drops by 10 ℃ and the lifetime extends by 100%.

The Annual Energy Production (AEP) of a wind power plant relates to the production rate of the wind turbines forming the wind power plant and is typically dependent on the annual wind speed at the location of the wind power plant. The larger the AEP, the larger the profit for the operator of the wind power plant and the larger the amount of electrical energy provided to the grid for a given wind power plant.

Thus, wind turbine manufacturers and wind power plant operators are constantly trying to increase the AEP of a given wind power plant.

One such method may be to over-rate the wind turbine under certain conditions, in other words, to allow the wind turbine to operate for a period of time to a power level that is higher than the rated or nameplate power level of the wind turbine, as shown by the shaded area 58 of FIG. 2, in order to generate more electrical energy when the wind is high and thus increase the AEP of the wind power plant. In particular, the term "over-rating" is understood to mean that more than rated active power is produced during full load operation by controlling turbine parameters such as rotor speed, torque or generator current. An increase in speed demand, torque demand, and/or generator current demand increases the additional power produced over-rating, while a decrease in speed, torque, and/or generator current demand decreases the additional power produced over-rating. It will be appreciated that over-rating applies to active power, not to reactive power. When the turbine is over-rated, the turbine is running more aggressively than normal and the generator has a power output higher than the rated power at a given wind speed. For example, the excess power level may be 30% above the rated power output. This allows for greater power extraction when this is advantageous to the operator, especially when external conditions such as wind speed, turbulence and electricity prices will allow for more profitable power generation.

Over-rating results in higher wear or fatigue on the components of the wind turbine, which may lead to early failure of one or more components and the need to shut down the turbine for maintenance. Thus, the over-rating is characterized by transient behavior. When the turbine is over-rated, it may be as short as a few seconds, or it may be an extended period of time if wind conditions and fatigue life of the components are favorable for over-rating.

While over-rating allows turbine operators to increase AEP and otherwise modify power generation to accommodate their requirements, there are several problems and disadvantages associated with over-rating wind turbines. Wind turbines are typically designed to operate at a given nominal rated power level or nameplate power level and for a certified number of years (e.g., 20 years or 25 years). Thus, if the wind turbine is over-rated, the life of the wind turbine may be reduced.

The present invention seeks to provide the turbine operator with the flexibility to operate their turbine in a manner suitable to their requirements, for example by returning an optimised AEP.

Disclosure of Invention

The invention is defined in the independent claims to which reference is now made. Preferred features are set out in the dependent claims.

Embodiments of the present invention seek to increase the flexibility available to the turbine operator when employing a control approach that compromises energy capture and fatigue loading. One example of such a control method is the use of over-rating.

According to a first aspect of the invention, there is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the method comprising:

receiving an input indicative of a target minimum wind turbine life;

determining a value indicative of a current remaining fatigue life of the turbine or one or more turbine components based on measured wind turbine site data and/or operational data;

changing a parameter of an initial predefined control schedule indicating how the turbine maximum power level changes over time by:

i) adjusting a parameter of the initial predefined control schedule;

ii) estimating, based on the changed control schedule, a future fatigue life consumed by the wind turbine or the one or more turbine components for a duration of the changed control schedule; and

iii) repeating steps (i) and (ii) until the estimated future fatigue life consumed by the wind turbine or each of the one or more turbine components is sufficient to allow a target minimum wind turbine life to be reached.

The parameters may be varied until the estimated future fatigue life consumed by the heaviest loaded components is sufficient to allow just reaching the target minimum wind turbine life, or in other words, such that the total fatigue life consumed will be substantially the same as the target minimum wind turbine life. This may be achieved based on a predetermined margin of the target minimum wind turbine life (e.g., within 0 to 1,0 to 3, 0 to 6, or 0 to 12 months of the target).

Optionally, step (iii) further requires maximising energy capture over the lifetime of the turbine.

Optionally, the control schedule indicates that the wind turbine may be over-rated to an amount of power above its rated power.

Optionally, the method further comprises: for each of one or more of the turbine components, receiving input indicative of a maximum allowable number of replacements for that turbine component. Step (i) may then further comprise adjusting, for one or more of the turbine components, the number of times that component may be replaced over the remaining life of the turbine. Step (i) may further comprise: when the components can be replaced over the remaining life of the turbine, adjustments are made to one or more of the turbine components. The one or more turbine components may include one or more of: a blade, a pitch bearing, a pitch actuation system, a hub, a main shaft, a main bearing, a gearbox, a generator, a converter, a yaw drive, a yaw bearing or a transformer.

Optionally, the initial predefined control schedule specifies a relative variation over time of the turbine maximum power level.

Optionally, determining a value indicative of a current remaining fatigue life of the turbine or the one or more turbine components comprises: sensor data from one or more turbine sensors is applied to one or more life usage estimation algorithms.

Optionally, determining a value indicative of a current remaining fatigue life of the turbine or the one or more turbine components comprises: data from the condition monitoring system is used.

Optionally, determining a value indicative of a current remaining fatigue life of the turbine or the one or more turbine components comprises: the data obtained from the wind power plant sensors are used in connection with a site check program which determines the loads acting on the turbine components based on the data obtained from the wind power plant sensors and parameters related to the wind power plant and wind turbine design. The sensor data may comprise sensor data collected before commissioning (commission) and/or building the wind turbine or wind power plant.

Optionally, adjusting the parameter comprises applying an offset, amplification, de-amplification or gain factor to the control arrangement. The parameters are adjusted until all or substantially all of the fatigue life of the heaviest loaded component is consumed for the scheduled duration. The offset may be adjusted by equating the area of the curve above and below the line showing fatigue damage caused by operation of the individual turbines at a maximum power level set to the site specific capacity of the expected lifetime. The offset may be adjusted until fatigue damage over time due to operating the turbine according to the control schedule equals fatigue damage over time due to operating the turbine according to a constant maximum power level set to the individual turbine maximum power level for the target minimum life.

Optionally, the initial predefined control schedule specifies a gradient of variation of the maximum power level over time. Adjusting the parameter may then comprise adjusting the gradient.

Optionally, the control schedule indicates an amount of fatigue damage that should be caused over time, the method further comprising operating the wind turbine based on the one or more LUEs to cause fatigue damage at a rate indicated by the control schedule.

Optionally, the method further comprises providing the determined control schedule to a wind turbine controller to control the power output of the wind turbine.

The method may be performed only once, or aperiodically as desired. Alternatively, the method may be repeated periodically. In particular, the method may be repeated once a day, month or year.

A corresponding controller for a wind turbine or a wind power plant may be provided, configured for performing the method described herein.

According to a first aspect, there is provided a method for generating a control schedule for a wind power plant comprising two or more wind turbines, the control schedule indicating for each wind turbine how the maximum power level varies over time, the method comprising:

receiving an input indicative of a target minimum expected life for each turbine;

determining a value indicative of a current remaining fatigue life of each wind turbine or one or more turbine components of each wind turbine based on the measured wind turbine site data and/or operational data;

changing a parameter of an initial predefined control schedule specifying how the power plant maximum power level varies over time by:

i) adjusting parameters of an initial predefined control schedule;

ii) estimating future fatigue life consumed by the wind turbine or one or more turbine components over the duration of the varying control schedule, based on the varying control schedule, using a site check program that determines loads acting on the turbine components based on data obtained from wind power plant sensors and parameters related to the wind power plant and wind turbine design, and including interactions between turbines of the wind power plant; and

iii) repeating steps (i) and (ii) until the estimated future fatigue life consumed by the wind turbine or each of the one or more turbine components is sufficient to allow the target minimum wind turbine life to be reached.

Optionally, the sensor data comprises sensor data collected prior to commissioning and/or building the wind turbine or wind power plant.

Optionally, step (iii) is further constrained such that for any given period of time within the schedule, when the power of all turbines is added together, it does not exceed the amount of power that can be carried in the connection from the power plant to the power grid.

According to a second aspect of the present invention there is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the method comprising:

receiving an input indicative of a maximum number of times each of one or more turbine components will be replaced within a remaining life of the turbine;

determining a value indicative of a current remaining fatigue life of the turbine or one or more of the turbine components based on the measured wind turbine site data and/or operational data;

varying a parameter of an initial predefined control schedule indicating how the turbine maximum power level varies over time by:

iv) adjusting parameters of the initial predefined control schedule;

v) estimating a future fatigue life consumed by the wind turbine or one or more turbine components over the duration of the varying control schedule based on the varying control schedule and taking into account the replacement of one or more turbine components; and

vi) repeating steps (i) and (ii) until the estimated future fatigue life consumed by the wind turbine or each of the one or more turbine components is sufficient to allow the target minimum wind turbine life to be reached.

This parameter may be varied until the estimated future fatigue life consumed by the heaviest loaded component is sufficient to allow just the target minimum wind turbine life to be reached, or in other words, such that the total fatigue life consumed will be substantially equal to the target minimum wind turbine life. This may be achieved based on a predetermined margin of the target minimum wind turbine life (e.g., within 0 to 1,0 to 3, 0 to 6, or 0 to 12 months of the target).

Optionally, step (iii) further requires maximizing energy capture over the life of the turbine.

Optionally, step (i) further comprises adjusting for one or more turbine components the number of times a component may be replaced over the remaining life of the turbine. Step (i) may further comprise adjusting for one or more turbine components when a component may be replaced during the remaining life of the turbine.

Optionally, the target minimum wind turbine life is a predetermined target value corresponding to a turbine design life.

Optionally, the method further comprises receiving an input indicative of a user-defined target minimum wind turbine life.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components comprises: sensor data from one or more turbine sensors is applied to one or more life usage estimation algorithms.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components comprises: data from the condition monitoring system is used.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components comprises: the data obtained from the wind power plant sensors are used in connection with a site check program which determines the loads acting on the turbine components based on the data obtained from the wind power plant sensors and parameters related to the wind power plant and wind turbine design. The sensor data may comprise sensor data collected prior to commissioning and/or building the wind turbine or wind power plant.

Optionally, the initial predefined control schedule specifies a gradient of variation of the maximum power level over time. Adjusting the parameter may include adjusting a gradient.

Optionally, the one or more turbine components comprise one or more of: a blade, a pitch bearing, a pitch actuation system, a hub, a main shaft, a main bearing, a gearbox, a generator, a converter, a yaw drive, a yaw bearing or a transformer.

According to a second aspect, there is provided a method for generating a control schedule for a wind power plant comprising two or more wind turbines, the control schedule indicating for each wind turbine how the maximum power level varies over time, the method comprising:

receiving an input indicative of a maximum number of times each of one or more turbine components of each turbine is to be replaced during a remaining life of the turbine;

iv) adjusting parameters of the initial predefined control schedule;

v) estimating future fatigue life consumed by the wind turbine or one or more turbine components for the duration of the varying control schedule based on the varying control schedule and taking into account the replacement of the one or more turbine components using a site check program which determines loads acting on the turbine components based on data obtained from wind power plant sensors and parameters related to the wind power plant and wind turbine design and which includes interactions between the turbines of the wind power plant; and

According to a third aspect of the invention, there is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the method comprising:

determining a value indicative of a current remaining fatigue life of the turbine or one or more turbine components based on the measured wind turbine site data and/or operational data;

applying an optimization function that changes an initial control schedule to determine an optimized control schedule by changing a tradeoff between fatigue life consumed by the turbine or the one or more turbine components and energy capture until an optimized control schedule is determined, the optimization comprising:

estimating a future fatigue life consumed by the turbine or turbine component during the altered control schedule based on the current remaining fatigue life and the altered control schedule; and

constraining optimization of the control schedule according to one or more input constraints;

wherein the input constraints comprise a maximum allowable number of component replacements for one or more turbine components, and the optimizing further comprises changing an initial value of wind turbine life to determine a target wind turbine life.

According to a fourth aspect of the present invention there is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the method comprising:

wherein the input constraints comprise a target minimum wind turbine life, and the optimization further comprises changing an initial value for the number of component replacements to be performed during the schedule for one or more components to determine a maximum number of component replacements.

According to a fifth aspect of the present invention there is provided a method of generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the method comprising:

wherein the optimizing further comprises: the method further includes changing an initial value of wind turbine life, and changing an initial value of a number of component replacements to be performed during the schedule for the one or more components to determine a combination of a target minimum wind turbine life and a number of component replacements for the one or more turbine components.

The following optional features may be applied to the third, fourth or fifth aspect.

The control arrangement may be applicable throughout the lifetime of the turbine.

Optionally, the method further comprises optimizing the control schedule by changing the timing of the component replacements and changing the number of component replacements until a maximum number is reached.

Optionally, the replaceable one or more turbine components comprise one or more of: a blade, a pitch bearing, a pitch actuation system, a hub, a main shaft, a main bearing, a gearbox, a generator, a converter, a yaw drive, a yaw bearing or a transformer.

Optionally, the initial control schedule specifies a relative variation over time of a turbine maximum power level at which the turbine may operate.

Optionally, the input constraints further comprise an upper maximum power output of the turbine and/or a minimum power output of the turbine allowed by the turbine design.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components comprises: data obtained from wind farm sensors is used in conjunction with a site check program that determines the loads acting on turbine components based on the wind farm sensors and parameters related to the wind farm and wind turbine design.

Optionally, the optimization of the control schedule comprises changing the control schedule to minimize a leveled energy cost (LCoE). The LCoE may be determined using an LCoE model that includes parameters for one or more of: a capacity coefficient indicative of the energy generated over a time period divided by the energy that the turbine can generate if it is continuously operating at rated power over the time period; availability indicating a time at which the turbine can be used to generate electricity; and wind farm efficiency, indicating the energy generated over a period of time divided by the energy that the turbine can generate if operated in a wind completely undisturbed by the upstream turbine. The model may further include parameters for one or more of: costs associated with replacing one or more components, including turbine downtime, labor and equipment for component replacement, manufacturing or refurbishment costs for replacement components, and transportation costs of refurbished components or the replacement components to a power plant; and the cost of service associated with replacing worn components.

Optionally, the optimized control schedule is a schedule of maximum power levels at which the turbine may operate, and a maximum power level above the rated power of the wind turbine may be specified. Optionally, the control schedule may specify an amount of fatigue damage that should be caused over time, the method further comprising operating the wind turbine based on the one or more LUEs to cause fatigue damage at a rate indicated by the control schedule.

The control schedule may indicate how the turbine maximum power level varies over the lifetime of the turbine.

Optionally, the method may further comprise providing the optimized control schedule to a wind turbine controller or a wind power plant controller for controlling the power output of the wind turbine.

Optionally, the method is repeated periodically. The method may be repeated daily, monthly or yearly.

A corresponding controller for a wind turbine or wind power plant may be provided configured to perform the method of the third, fourth or fifth aspect described herein.

According to a third aspect, there is provided an optimizer for generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the optimizer comprising:

an optimization module configured to receive: an initial value of a set of variables, the set of variables being operational variables of the wind turbine and comprising an initial control schedule; one or more constraints; and data indicative of the current remaining fatigue life of the turbine or one or more turbine components;

wherein the optimization module is configured to:

optimizing the control schedule by maximizing or minimizing the operating parameters received at the optimization module that depend on the set of variables by changing one or more of the variables from their initial values as a function of the remaining fatigue life of the turbine or the one or more turbine components and the one or more constraints; and is

Outputting the optimized control schedule;

wherein the constraints include a maximum allowable number of component replacements for one or more turbine components, and the optimization module is further configured to change an initial value of wind turbine life to determine a target wind turbine life.

According to a fourth aspect, there is provided an optimizer for generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the optimizer comprising:

wherein the optimization module is configured to:

Outputting the optimized control schedule;

wherein the constraints include a target minimum wind turbine life, and the optimization module is further configured to change an initial value for a number of component replacements to be performed during the schedule for one or more components to determine a maximum number of component replacements.

According to a fifth aspect, there is provided an optimizer for generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the optimizer comprising:

wherein the optimization module is configured to:

Outputting the optimized control schedule;

wherein the optimization module is further configured to: the initial value of the wind turbine life time is changed, and the initial value of the number of component replacements to be performed during the schedule is changed for one or more components to determine a combination of a target minimum wind turbine life time and the number of component replacements for the one or more turbine components.

The following optional features may be applicable to the optimiser of the third, fourth or fifth aspect.

Optionally, the optimizer further comprises an initialization module configured to receive the sensor data and initial values of the set of variables, the initialization module configured to calculate initial values of the operating parameters.

Optionally, the one or more turbine components are one or more of: a blade, a pitch bearing, a pitch actuation system, a hub, a main shaft, a main bearing, a gearbox, a generator, a converter, a yaw drive, a yaw bearing or a transformer.

Optionally, the operating parameter is a levelled energy cost (LCoE) of the turbine, and optimizing the control arrangement includes minimizing the levelled energy cost (LCoE). The LCoE may be determined using an LCoE model that includes parameters for one or more of: a capacity coefficient indicative of the energy generated over a time period divided by the energy that the turbine can generate if it is continuously operating at rated power over the time period; availability indicating a time at which the turbine can be used to generate electricity; and wind farm efficiency, indicating the energy generated over a period of time divided by the energy that the turbine can generate if operated in a wind completely undisturbed by the upstream turbine. The model may further include parameters for one or more of: costs associated with replacing one or more components, including turbine downtime, labor and equipment for component replacement, manufacturing or refurbishment costs for replacement components, and transportation costs of refurbished components or the replacement components to a power plant; and the cost of service associated with replacing worn components.

A controller may be provided comprising an optimizer according to any of the third, fourth or fifth aspects.

According to a third aspect, there is provided a method of generating a control schedule for a wind power plant comprising a plurality of wind turbines, the control schedule indicating for each wind turbine how the turbine maximum power level varies over time, the method comprising:

determining a value indicative of a current remaining fatigue life of each turbine or one or more turbine components of each turbine based on measured wind turbine site data and/or operational data;

applying an optimization function that changes an initial control schedule for each turbine to determine an optimized control schedule by changing a tradeoff between fatigue life consumed and energy capture for each turbine or for one or more turbine components of each turbine until an optimized control schedule is determined, the optimization comprising:

estimating future fatigue life consumed by the turbine or turbine component during the altered control schedule based on the current remaining fatigue life and the altered control schedule using a site check program that determines loads acting on the turbine components based on data obtained from wind power plant sensors and parameters related to the wind power plant and wind turbine design and includes interactions between the turbines of the wind power plant; and

constraining optimization of the control schedule in accordance with one or more input constraints;

wherein the constraints include a maximum number of allowable component replacements for each of the one or more turbine components of each of the wind turbines, and the optimization module is further configured to change the initial value of wind turbine life to determine a target wind turbine life.

According to a fourth aspect, there is provided a method of generating a control schedule for a wind power plant comprising a plurality of wind turbines, the control schedule indicating for each wind turbine how the turbine maximum power level varies over time, the method comprising:

wherein the constraints include a target minimum wind turbine life for each of the wind turbines, and the optimization module is further configured to change an initial value for a number of component replacements to be performed during the schedule for one or more components of each wind turbine to determine a maximum number of component replacements.

According to a fifth aspect, there is provided a method of generating a control schedule for a wind power plant comprising a plurality of wind turbines, the control schedule indicating for each wind turbine how the turbine maximum power level varies over time, the method comprising:

wherein the optimizing further comprises changing an initial value for the lifetime of each wind turbine and changing an initial value for the number of component replacements to be performed during the schedule for one or more components of each wind turbine to determine a combination of a target minimum wind turbine lifetime for each wind turbine and the number of component replacements for one or more turbine components of each wind turbine.

The following optional features may be applicable to the power plant level method of the third, fourth or fifth aspect.

Optionally, the initial control schedule specifies for each turbine the relative variation over time of the turbine maximum power level at which the turbine may operate.

For one or more turbine components, optionally, the optimization function changes the number of times components can be replaced over the remaining life of the turbine. The optimization function makes changes to one or more of the turbine components as to when the components can be replaced during the remaining life of the turbine.

Optionally, the method is further constrained such that for any given period of time within the schedule, when the power of all turbines is added together, it does not exceed the amount of power that can be carried in the connection from the power plant to the power grid.

A corresponding wind power plant controller may be provided which is configured to perform the above-described method of the third, fourth or fifth aspect.

Any of the methods described herein may be embodied in software that when executed on a processor of a controller causes it to perform the relevant method.

Reference herein to site inspection software includes site inspection tools known to those skilled in the art for simulating operation of wind turbines based on pre-construction and/or pre-commissioning sensor data and other site information (e.g., terrain, etc.) to determine operational characteristics of the wind turbines and wind power plants. The site inspection tool may also use operational data from a turbine or power plant, or may use operational data from a similar turbine or power plant (where such data is available). Examples include the vestas (tm) site review tool. DNV GL provides an alternative site check software package. It consists of three linked programs: "WindFarmer", "WindFarmer blanked Link", and "blanked", which allow the user to perform comprehensive performance and load calculations.

Drawings

The invention will now be further described, by way of example only, with reference to the accompanying drawings, in which:

FIG. 1A is a schematic front view of a conventional wind turbine;

FIG. 1B is a schematic view of a conventional wind power plant comprising a plurality of wind turbines;

FIG. 2 is a graph illustrating a conventional power curve for a wind turbine;

FIG. 3 is a graph illustrating how the power produced by a wind turbine over time varies with the target life of the turbine;

FIG. 4 is a graph showing different power schedules for wind turbines where individual maximum wind turbine power levels vary over the life of the turbine to control power output;

FIG. 5 is a graph illustrating exemplary variations in cumulative total life fatigue between different turbine components;

FIG. 6 is an example of a simplified normalized energy cost model of a wind power plant;

FIG. 7 is a block diagram of an exemplary optimizer for optimizing a wind turbine control strategy;

FIG. 8 is an example of a method for determining a maximum power level for a wind turbine type; and

FIG. 9 is a schematic view of a wind turbine controller apparatus.

Detailed Description

Embodiments of the present invention seek to increase the flexibility that a turbine operator may have when employing a control approach that compromises energy capture and fatigue loading. Specifically, embodiments provide an optimization method to allow turbine operators to optimize turbine performance, such as AEP, according to their requirements.

To optimize performance, the three parameters may be varied throughout the wind turbine control strategy. These are: (i) power scheduling of the wind turbine; (ii) remaining life of the wind turbine; (iii) number of component replacements allowed during the remaining life of the wind turbine. One or more of these parameters may be varied relative to one or more of the other parameters to achieve an optimized control strategy. The parameters may also be constrained.

For example, optimization may be performed to improve the AEP of the turbine over its lifetime and to improve profitability. The turbine operator may specify one or more constraints and may then perform the optimization. The operator may request a minimum wind turbine life (e.g., 19 years), a maximum number of replacements of individual components (e.g., one gearbox replacement), and/or one or more of a particular power schedule, schedule curve or shape, or schedule gradient.

Power scheduling is the scheduling of variables that the wind turbine controller uses to trade off energy capture and fatigue loading during the remaining turbine life, for example when overburdening the turbine. The additional power generated by over-rating a given turbine may be controlled by specifying the value of a variable such as the individual wind turbine maximum power level. This maximum power level specifies power above the rated power, up to the power at which the turbine can operate at the time of the over-rating. The power schedule may specify a constant maximum power level over the life of the turbine. Alternatively, the power schedule may specify a maximum power level that varies over the life of the wind turbine, such that the amount of additional power that may be generated by over-rating varies over time. For example, a power plant operator may wish to generate more power during the early years of the wind turbine life at the expense of increased fatigue life consumption of the turbine components, as the financial value of the power generation during the early years of the project is disproportionately high.

For a given turbine type, the individual wind turbine maximum power level is constrained by the maximum load limits of the wind turbine mechanical components as well as the design limits of the electrical components, since the maximum power cannot be safely increased beyond a level that causes the turbine to experience electrical or mechanical load values above its maximum design load limit. The upper maximum power level that an individual wind turbine maximum power level cannot exceed may be referred to as a "wind turbine type maximum power level" and specifies a maximum power level at which the determined load does not exceed the design load of that type of wind turbine. An example of the way in which the wind turbine type maximum power level may be calculated is given in the "maximum power level calculation" section below.

The individual wind turbine maximum power level is the power level specified in the schedule according to embodiments of the invention and may be referred to simply as the maximum power level. The individual wind turbine maximum power level may be refined for each individual turbine, calculated based on the fatigue load value of each turbine based on one or more of the conditions each turbine is facing at its particular location or position in the wind power plant, wherein the individual wind turbine maximum power level is determined for each turbine at a given site. The individual wind turbine maximum power level may then be set such that the rate of consumption of fatigue life of the turbine or individual turbine components gives a fatigue life that corresponds to or exceeds a particular target life.

The remaining life of the wind turbine specifies the amount of operating life that the operator is willing to accept in order to optimize the AEP. The remaining life will depend on the point in time at which the first activation of the AEP optimization method is carried out, since the available remaining life decreases as the turbine operates.

The number of component replacements allowed during the remaining life of the wind turbine may also be used to optimize the AEP. Due to the different rates of turbine component fatigue under different conditions, the actual life of some components may well exceed the 20 year expected life of the wind turbine, or as such, these components can be over-rated by a higher amount during a given life cycle. Components with longer life do not drive the entire turbine life and have excess production capacity. However, components with shorter life may have a limiting effect on over-rating, and the AEP can be improved by replacing one or more of these components during the life of the turbine. In particular, the over-rating achieved by increasing torque has a particularly significant impact on fatigue life of the gearbox, generator and electrical output components. Conversely, in the event that over-rating is achieved by increasing the rotor speed, then the fatigue life of the blades and structural components is more affected.

Replaceable components in the context of embodiments of the present invention are considered to be the primary components, such as those that each account for 5% or more of the total cost of the wind turbine and can be replaced in the field. Typical wear parts, which account for only a small fraction of the total cost of the wind turbine, need not be considered. In particular, the component considered for replacement may comprise one or more of a blade, a pitch bearing, a pitch actuation system, a hub, a main shaft, a main bearing, a gearbox, a generator, a converter, a yaw drive, a yaw bearing or a transformer.

FIG. 3 shows a first example of optimization in which the power schedule is varied with respect to the target life of the turbine. In this example, the design life of the turbine is 20 years, and the power level is fixed during the life of the turbine. It can be seen that as the life of a wind turbine decreases, the amount of electricity produced in a given year increases. As turbine life decreases, the fatigue life consumption rate of the turbine or turbine components may increase, allowing additional power to be generated by over-rating. The optimization may be applied according to the preferences of the turbine operator. For example, a life that maximizes the AEP, Net Present Value (NPV), or net present value (NPW) of the turbine may be determined and selected. NPV/NPW can be calculated using known methods.

FIG. 4 shows another example of an optimization where the power schedule is again varied with respect to the target life of the turbine. In this example, the maximum power level specified by the schedule is variable over the life of the turbine. An initial schedule may be specified, for example, a turbine operator may have a desired schedule shape to use. The schedule defines how the individual wind turbine maximum power levels vary over time, but may be done in a relative rather than an absolute manner. In this example, the desired schedule 401 is from the wind turbine type maximum power level P over a 20 year turbine life_maxNominal or rated power level P to turbine type_nomIs arranged linearly. For a typical example site with an average annual wind speed below the turbine design wind speed, dashed line A represents the site specific capacity of the individual turbine over a 20 year lifetime. For a particular turbine, it may not be possible to meet the desired schedule 401 without exceeding the fatigue life of the turbine or certain turbine components over the life of the turbine. The schedule is adjusted accordingly until the total fatigue occurring according to the power schedule does not exceed the design fatigue life of the heaviest loaded component.

This may be accomplished by estimating the fatigue damage that occurs in compliance with the schedule over its duration (e.g., until the turbine design life, or a user-specified turbine life). Using the site check function, the fatigue damage that occurs can be estimated and the LUE data can be supplemented, both taking into account fatigue damage due to loading in view of given micro-site conditions. The schedule may be adjusted until the resulting fatigue life of the heaviest loaded component is equal to the design fatigue life of that component. In other words, the schedule is adjusted until all or most of the fatigue life of the heaviest loaded component is exhausted during the scheduled duration.

The schedule may be adjusted by adjusting one or more of its parameters. This may include:

-applying an offset to the schedule by adding or subtracting a value in the whole schedule;

-applying a gain greater or less than 1 to the schedule;

-non-linearly increasing or decreasing any other suitable function of the control arrangement by adjusting the relevant parameter to otherwise expand/contract or increase/contract the arrangement as required to change the arrangement power level value.

In one example, adjusting the schedule may be accomplished by: based on an equivalent graph of occurring fatigue damage versus time or an equivalent graph of remaining fatigue life versus time for the most fatigued components as determined from the power schedule map, and site inspection software is used to determine the fatigue damage to components that may occur at a particular turbine location (also referred to as a turbine micro-site) within the power plant at a given power level. The curve is adjusted until the regions defined by each of the arrangements above and below the corresponding force line on the equivalent fatigue curve for the desired turbine life are equal. This may be achieved, for example, by equating the area of the curve above and below the line showing fatigue damage caused by individual turbines operating at a constant maximum power level set to a site specific capacity over the expected lifetime. For example, this would be a line equivalent to dashed line a of fig. 3, but illustrating fatigue damage caused by the maximum power of the individual wind turbines over time. Zone equality may be achieved by moving the power schedule curve up or down by adding or subtracting an offset to the curve until the zones are equal, or by zooming in or out on the curve by adjusting one or more parameters of the curve. The total fatigue life consumed by the turbine or turbine components will be up to 20 years of operation. Line 402 shows an exemplary arrangement, the line terminating in a black square i.

For the same exemplary site, the site specific capability of the turbine over the 19 year life is shown as dashed line B. It can be seen that the 19 year life span capacity exceeds the 20 year life span capacity. Thus, the resulting 19-year schedule (an example of which is given by line 403) may have an initial maximum power level value P 'that is greater than the 20-year schedule 402'_20yrsHigher initial maximum power level value P'_19yrs. The schedule 403 ends at 19 years, represented by the black square ii.

In the example of fig. 4, schedule adjustments are subject to the additional constraint that the slope or gradient of the schedule should equal the slope or gradient of the initial schedule 401 for a 20 year lifetime. As used in the example of fig. 4, other constraints may also be applied whereby the slope of the schedule equals the slope of the initial schedule 401 until a nominal power level is reached, which may be the rated power of the turbine, from which point the maximum power level is maintained at the nominal power level. Alternatively, embodiments may employ a derating of the turbine such that the maximum power level specified by the schedule may be set to a level below the rated power of the turbine.

Scheduling adjustments in a stepwise manner, or from P_maxDecrease, or from P_nomIncrease, or increase from the power value of line a, until a suitable schedule is reached for which there is sufficient fatigue life in the heaviest loaded turbine components to reach the target turbine life. For example, the initial maximum power level P' may be at P_nomThe 1% steps are increased or decreased until a proper schedule is reached.

There are other possibilities to optimize the power schedule according to the age of the turbine life. For example, the schedules may all be from the same initial value (e.g., P)_max) Starting and graduating until the areas defined by each of the arrangements above and below the corresponding lines of capability on the equivalent fatigue curve for the expected turbine life are equal.

Another line 404 shows an example of a schedule that a turbine may achieve over a 20 year life if one or more component replacements are considered. The schedule 404 terminates with a black box i. One or more components may be particularly susceptible to over-rated induced fatigue damage. For example, as shown in FIG. 5, over a 20 year operation, one component may reach a 20 year life fatigue limit while other components still have some life remaining. In such a case, replacing one or more components that cause a higher rate of fatigue damage allows the AEP to be increased. This may still increase profitability of the turbine when calculating the NPV during the life of the turbine, including and taking into account the total cost of replacement.

As an alternative to the arrangement of specifying the maximum power level value, it is also possible to specify the arrangement of fatigue damage or the remaining fatigue life, since the rate of fatigue damage caused is related to the maximum power level setting of the turbine. The turbine power output is then controlled so that the remaining fatigue life remains the remaining fatigue life specified by the schedule, such as by using LUEs to track the fatigue life of the turbine controller. As a further alternative, an energy schedule may also be used, as this still indicates how the turbine maximum power level varies over time. The energy schedule may be yearly or every calendar month, etc.

For the avoidance of doubt, the arrangement may also have a non-linear shape, for example a shape that follows a polynomial curve.

Although the schedules are shown to vary continuously over their duration, they may vary in steps, specifying a given maximum power level during a particular time period (e.g., a month, a season, or a year). The schedule may be, for example, a series of age values over the life of the turbine.

The schedule may be calculated once or may be repeated at intervals. For example, the schedule may be monthly or yearly. For schedules that specify maximum power levels annually, it may be advantageous to calculate the schedule (e.g., monthly or weekly) because changes in the schedule may alert the user to parameters that change more quickly than expected.

If the calculation is scheduled once, the calculation may be performed before commissioning of the wind power plant or may be performed at any time after commissioning. For calculations that are repeated at intervals, the first calculation may be performed before commissioning of the wind power plant or may be performed at any time after commissioning.

First example

According to a first example, a control arrangement is generated which can be used for controlling the wind turbine. A relative arrangement may be defined and one or more of a minimum wind turbine life or a maximum number of replacements of a major component may be defined. The schedule is then adjusted to ensure that the fatigue life of the turbine reaches the target life while maximizing the AEP.

The wind turbine operates according to one of the over-rating control techniques described herein using an over-rating controller, which may be implemented by a wind turbine controller.

A Life Usage Estimator (LUE) may be used to determine and monitor the life usage of the component. The life usage estimator may be used to ensure that the fatigue load limit of all turbine components remains within their design life. The load (e.g., their bending moment, temperature, force, or motion) experienced by a given component may be measured and the amount of component fatigue life consumed calculated, for example, using techniques such as rain flow counting and Miner's law or chemical decay equations. Based on the lifetime usage estimator, the individual turbines may then be operated in a manner that does not exceed their design limits. The device, module, software component or logic for measuring the fatigue life consumed by a given turbine component may also be referred to as its life usage estimator, and the same acronym (LUE) will be used to refer to the algorithm for determining the life usage estimate and the corresponding device, module or software or logic. The LUE will be described in more detail below.

According to the default mode of operation, the over-rating controller will control the amount of over-rating applied based on a function or schedule over the expected or certified life of the wind turbine. Typically this is 20 or 25 years.

The controller is configured to receive input parameters, for example from a site operator, which define a new target life for the wind turbine or one or more specific turbine components. The LUE is used to determine the lifetime usage of the turbine or associated turbine components to date. This has constraints on the amount of remaining component life of the wind turbine and therefore on the control arrangement. In addition, the revised target life has a constraint on the amount of time that the remaining component life must be extended.

The future available fatigue life may be calculated off-line or on-line using site inspection software and used to specify a revised control schedule. The site review function may include calculations or one or more simulations to determine an expected fatigue damage rate using site-based historical data (including site climate data measured before and/or after construction and/or data from LUEs). Site climate data typically includes data from a wind mast or base lidar and may include wind speed, turbulence intensity, wind direction, air density, vertical wind shear, and temperature. Site check calculations may be performed remotely or by the turbine/plant level controller as the case may be.

The site review software may have information or parameters related to a given WPP site terrain, wind conditions, etc. Terrain and topographical information may be provided by site surveys and/or from knowledge of the WPP site, which may include slopes, cliffs, inflow angles of each turbine in the WPP, and so forth. Wind conditions, such as wind speed (season, year, etc.), turbulence intensity (season, year, etc.), air density (season, year, etc.), temperature (season, year, etc.), etc., may be provided from the wind mast data and/or wind conditions experienced and recorded by the wind turbine and/or the WPPC at the WPP.

The site review tool may include one or more memories, databases, or other data structures to store and maintain fatigue load values for each type of wind turbine, a wind turbine type maximum power level for each type of wind turbine, and information and/or parameters related to WPP site conditions.

A revised control schedule is thus generated whereby the over-rated generated additional power is adjusted such that the turbine is exposed to a higher or lower rate of accumulation of fatigue damage depending on whether the new target end-of-life date is earlier or later than the previous target date, which may be the certified life.

The ability to modify the turbine control schedule allows the operator to change its priority over time. For example, a main generator on a local grid may be shut down for mid-term service, or may be completely out of service, and the grid may require additional support. This may be reflected in a much higher long term cost, so it is advantageous for the operator to increase the energy production in the short term. Thus, the operator may decide to reduce the life of the turbine, or to reduce the life of affected components such as the gearbox and generator, and to generate additional power by overbooking while accepting shorter wind turbine or turbine component life.

Methods other than LUE may be used to determine the lifetime usage of the wind turbine or turbine component. Alternatively, the operating date of the turbine may be checked and the fatigue damage that occurred so far may be calculated. This may be particularly useful when the over-rated control is updated to the wind turbine, and the future available fatigue life is again calculated off-line using site check software, and this is used to specify the maximum power level. The site check function may again include offline or online calculations, or one or more simulations, to determine the expected fatigue damage rate using measured site data as of installation or based on historical data of the site, although in this case the calculations are made without LUE data being available.

Site review software may be used to review the operation of the wind turbine up to the date the over-rated controller using the functionality described herein is installed to calculate fatigue loads on turbine components based on input parameters specifying site terrain, site geography, site meteorological conditions, etc., using measurements related to the precise location of the turbine within the wind power plant site, such as one or more of energy output, wind speed, wind direction, turbulence intensity, wind shear, air density, turbine mechanical load measurements (e.g., from blade load sensors), turbine electrical component temperatures and loads, icing events, component temperatures, and condition monitoring system outputs. These values may be used to calculate an estimate of the fatigue damage that has occurred to date on the turbine component. The future available life of the turbine or turbine component may be calculated by applying the measurements to a site check function wind turbine model or simulation that provides as output an estimated fatigue damage and/or remaining fatigue life based on the value of the turbine's wind turbine type maximum power level and one or more of these measurements. The simulation or model may provide fatigue damage and/or remaining fatigue life at the component level or for the turbine as a whole. The fatigue load calculation may be performed according to various calculation programs. Various examples of these site check programs will be known to those skilled in the art and will not be described in detail.

The resulting estimate of the fatigue life of the turbine or the consumption of the turbine component may be used to determine an over-rating strategy to be applied by the controller. This estimate may be used once at the initiation of over-rating control, and if updated, may be performed part way through the life of the turbine. Alternatively, the estimation may be performed periodically over the life of the turbine, such that the overbooking strategy is updated periodically according to how the life fatigue consumption changes over the life of the turbine.

The over-rating strategy is determined based on the remaining fatigue life of the wind turbine or wind turbine component, which is itself based on the operational life of the wind turbine. The amount of overbooking applied is controlled so that the turbine or turbine component causes fatigue damage at a rate low enough to ensure that the fatigue life of the turbine is exhausted only at the end, and preferably only at the end of a predetermined turbine life.

The determination of the component fatigue life estimate may be further extended or replaced by using data from one or more condition monitoring systems. Condition Monitoring Systems (CMSs) include a number of sensors at strategic points in the drive train, in the turbine gearbox, generator, or other critical components. The condition monitoring system provides an early warning of component failure before the component actually fails. Thus, the output from the condition monitoring system may be provided to the controller and used as an indication of the fatigue life consumed by the monitored component, and may in particular provide an indication of when the fatigue life of the component has reached its end. This provides an additional method of estimating the lifetime of use.

Second example

A second example is provided to perform a more general optimization procedure that can be used to perform similar optimizations to those described above, as well as other more general optimizations. The optimization process of the second example may be performed by a controller applying an optimization scheme.

An overall financial cost or normalized energy cost (LCoE) model of the turbine is included and may be used in offline calculations prior to installation of the excess control system or online as part of the wind turbine controller or wind power plant controller. The use of the LCoE model allows for optimization of the overbooking strategy and may also include factors to replace major components based on the cost of doing so. As used herein, the term "leveled energy cost" refers to a measure of energy cost from a turbine that is calculated by dividing the life cost of the turbine by the life energy output of the turbine.

FIG. 6 illustrates an example of a simplified LCoE model in which various costs associated with building and operating a wind turbine and wind turbine plant are considered.

Wind Turbine Generator (WTG) costs take into account the overall cost of manufacturing a wind turbine. Transportation costs take into account the cost of transporting the turbine components to a site for installation. The operating and maintenance (O & M) costs take into account turbine operating costs and may be updated as operation and maintenance occurs. Such information may be provided by service technicians to the local turbine controllers, to the wind farm controllers, or otherwise. The capacity coefficient represents the energy generated over a given period of time (e.g., one year) divided by the energy that would be generated over that period of time if the turbine were continuously operating at rated power. Availability represents the time that the turbine is available to generate electricity. The farm efficiency represents the efficiency of extracting energy from the wind and is affected by the spacing of the turbines within the farm.

Only those elements of the LCoE that are affected by the control and component replacement strategy need to be included in the LCoE model, since many of the parameters that may be included in the LCoE model are fixed when the turbine or wind farm is built. The affected elements are:

cost of operation and maintenance (O & M)

If more parts are replaced, this will increase

Capacity factor

If a more aggressive over-rating is used, it will increase and, therefore, more MWh will be generated

● availability

If more major components are replaced, the downtime required for the replacement process may be somewhat reduced

If a more aggressive over-rating results in increased preventive replacement or unplanned failure of worn parts, it will be somewhat reduced

● life span

Decreasing or increasing depending on the constraint selection.

Having a financial cost (LCoE) model of the turbine contained in the turbine or WPP controller allows for a more flexible and efficient control strategy to be determined. For example, if conditions at a particular site are found to be particularly harsh on the gearbox, such conditions will be identified and the operator may choose whether to over-rate the turbine and take into account the gearbox replacement a certain number of times. The turbine controller may then determine when the gearbox should be replaced, run the turbine accordingly, and optionally also provide an indication of when to replace the gearbox.

FIG. 7 illustrates a block diagram of an exemplary optimizer for optimizing a wind turbine control strategy, which may be incorporated into a controller and used to implement various embodiments of the present invention.

When the algorithm starts, the block marked "initialize" will run once. This provides initial conditions for the optimization cycle. The loop marked as "optimized" is performed periodically, for example, once per day, month or year. When executed, the loop may be run as many times as necessary to achieve good enough convergence of the optimization process. After convergence, the new set of outputs is sent to the wind turbine controller (x1) and the operator (other outputs) to implement the determined control strategy. These two blocks "calculate the estimate of LCoE" contain the same calculation method. They include all elements not yet fixed in fig. 6, i.e., O & M cost, capacity factor, availability, and lifetime. For example, the tower CAPEX is already fixed, so it need not be included. But the operating and maintenance (O & M) costs are not fixed, which is included because the gearbox can work harder and be replaced once during the lifetime of the turbine.

In case there are many similar connections, e.g. between the optimization algorithm block and the block "calculate estimate of LCoE", not all connections in fig. 7 are shown. The following terms are used in or with reference to FIG. 7:

● N number of time periods of remaining life (e.g., years). If desired, the user may change it to suit his desired operating strategy.

● x1 for example, for a 3MW turbine [3.5MW, 3.49MW, 3.48MW, 3.47MW. ], a one-dimensional array of individual wind turbine maximum power levels in the year 1.. N,

● x2 one-dimensional array of gearbox replacement times in 1.. N years, e.g., [0,0,0,0,0,0,0, 1,0,0,0,0,0, 0,0]

● x3 one-dimensional array of generator replacement times in 1

● x4 one-dimensional array of main bearing replacement times in 1.. N years

● x5 one-dimensional array of blade group replacement times in 1.. N years and optionally:

● x6 one-dimensional array of converter replacement times in 1.. N years

● x7 one-dimensional array of pitch bearing replacement times in 1

● x8 one-dimensional array of pitch actuator (hydraulic or electrical) replacement times in the year 1.. N

● x9 one-dimensional array of yaw drive replacement times in 1.. N years

● x10 one-dimensional array of yaw bearing replacement times in 1

● x11 one-dimensional array of transformer replacement times in 1

● "_ 0" represents an initial condition, e.g., an initial condition where x1_0 is x1

Referring to FIG. 7, the optimization process entails determining a plurality of constants for a given turbine and calculating the initial conditions for optimization using the values of a plurality of physical and control parameters. Once the initial conditions are calculated, the optimization process applies a function defining the relationship between the flattening energy costs and the input values of the physical and control parameters to determine the combination of input values that minimizes the flattening energy costs without exceeding certain optimization constraints.

To calculate the optimal initial conditions, a number of parameter values for a given turbine are determined and input into an "initialization" block. These values are constant for any given periodic optimization (e.g., monthly). They are parameters entered by the operator and can be changed at any time, but if changed, will be applied the next time the optimization is run. These parameters may include one or more of the following: the life of the turbine/individual turbine components; the cost of gear box replacement; cost of bearing replacement; generator replacement cost; blade replacement cost; the replacement cost of the pitch system; and replacement costs to replace any other components as needed.

For example, using a site check function and/or one or more LUEs to determine the life of the turbine and/or the life of one or more components, or may be provided as constraints to be met. The exchangeable part comprises a blade, a pitch bearing, a pitch actuation system, a hub, a main shaft, a main bearing, a gearbox, a generator, a converter, a yaw drive, a yaw bearing or a transformer.

The total cost of replacing each component is determined. For example, for replacing a gearbox, cost considerations will be whether to install a new gearbox or a refurbished gearbox, transportation costs, and crane and labor costs. The turbine downtime cost is also included under the availability portion in FIG. 6.

Other costs may be included, such as financial costs (including Weighted Average Capital Cost (WACC), etc.), as well as other elements needed to calculate the impact of future wind turbine operating strategies on LCoE.

The lifetime parameters may be set by the operator according to its operating policy for the site, or may be determined as part of the optimization. Other constants are based on best knowledge so they may be updated occasionally, but such updates will be quite rare. In particular, the O & M cost can only be estimated in advance, and over time these estimates will be replaced by actual data, making a more accurate estimate of the future O & M cost.

The "initialization" block and optimization algorithm uses the following variables:

x1 is a one-dimensional array of maximum power levels in the year 1.. N, for example for a 3MW turbine [3.5MW, 3.49MW, 3.48MW, 3.47MW.. ],

one-dimensional array of gearbox replacement times in N.N.1. for x2, e.g., [0,0,0,0,0,0,0,0,1,0,0,0,0,0]

One-dimensional array of generator replacement times in N years of x3

One-dimensional array of main bearing replacement times in N years of 1.. x4

A one-dimensional array of blade group replacement times in N years of x5 and optionally:

one-dimensional array of converter replacement times in N years of x6

One-dimensional array of pitch bearing replacement times in N years of x7

One-dimensional array of pitch actuator (hydraulic or electrical) replacement times in N years of x8

One-dimensional array of yaw drive replacement times in N years of x9

One-dimensional array of yaw bearing replacement times in N years of x10

One-dimensional array of transformer replacement times in 1.. N years for x11

The initial calculation of the estimate of LCoE uses the initial operator estimate of the initial conditions, x1_0, x2_0, x3_0, etc.

The signal labeled "measurement data" in fig. 7 contains data from the sensors and data determined according to the O & M process. The measurement data from the sensor may be from a turbine or wind power plant and may include one or more of the following:

LUE values of one or more of the turbine components such as gearbox, generator, main bearing, blades, converter, pitch bearing, pitch actuator (hydraulic or electric), yaw drive, yaw bearing, transformer;

-wind speed and environmental data, or other data obtained from site inspection procedures;

-CMS data of one or more of the turbine components.

The measurement data from the operation and maintenance (O & M) activities contains the O & M cost, which may include an estimate based on the cost (if any) so far. This is used together with future scheduled service patterns, experience of other turbines of the same design from the same or other wind power plants and experience of certain parts of other turbines of different designs using the same parts to give an estimate of future O & M costs in LCoE calculations.

The optimization process uses inputs and constraints to minimize the flattening energy cost (LCoE) by calculating the LCoE directly or by calculating certain LCoE variables, depending on the initial conditions. Only the portion of the LCoE that changes after the turbine is built, i.e., the portion affected by O & M cost, capacity factor, availability, and life, needs to be calculated. The optimization is run until the LCoE is minimized, for example, until the calculated LCoE gradually changes within a given tolerance.

The constraint on the optimization is the area that the optimization algorithm cannot enter when searching for the minimum of LCoE. The constraints may include one or more of the following: wind turbine type maximum power level; minimum power output for the turbine type; maximum active power capacity of the wind power plant in connection with the grid, i.e. the maximum sum of the active power outputs of the turbines; and any other suitable constraints.

The constraints may also include one or more of the following, which may be defined by the user:

-a minimum or target expected wind turbine life;

-maximum number of component replacements of all components or of one or more given components;

-a predefined maximum power level schedule, or a predefined relative maximum power schedule defining the shape of the maximum power schedule.

The number of inputs per one-dimensional array may be selected to make the runtime of the optimization algorithm easier to manage. One-dimensional arrays x1, x2, etc. are described above as being provided for yearly operations. While input may be provided for operation each month or each season, this would provide 12 or 4 times the input. Thus, the age value can be used. Of course, different time periods may be used as appropriate depending on the desired computation time or the optimized interval.

Also, in order to make the runtime easier to manage, the wind turbine components may be selected such that only the most relevant components are used in the optimization. The components to be included, in particular the gearbox, generator, main bearings and blades, may be selected according to whether their lifetime is significantly affected by the active power output above the rated wind speed.

Additionally or alternatively, the components used in the optimization may be selected based on their values. For example, components having only values of 5% or more of the total cost of the turbine may be included.

The optimizer algorithm generates multiple outputs each time it runs to converge. The one-dimensional array x1 representing the schedule of maximum power levels of the turbine in 1.. N years can be used for closed-loop control by automatically transmitting data to the wind turbine controller for use in accordance with the turbine power requirements until the next operational optimization cycle (e.g. 1 month later). Alternatively, the maximum power level may be used without an automatic control loop at the recommended capacity, for example by sending maximum power level data to a computer system to be output on a display for viewing by a service department.

The other one-dimensional arrays x2, x3, x4 represent the arrangement for component replacement. This scheduling data may be output to another computer system to allow action to be taken. The data may be provided directly to the component replacement scheduling software. Alternatively, the component replacement data including the suggested replacement date may be used as a suggested output that is sent to a display for viewing by a service department to decide on manual implementation of the component replacement plan.

It should be noted that the above-described one-dimensional array of maximum power levels (x1) may be provided as only over-rated, over-rated or under-rated, or only under-rated, so that the maximum power level variable need only specify an amount above (or below) the rated power. The power demand may alternatively be a speed demand and/or a torque demand per cycle, or fatigue life consumption in the case where power is controlled by a life usage control function as described below. A disadvantage of using both the speed demand and the torque demand is that the calculation time to calculate the optimum configuration will be longer.

Although the optimizer is described above as being performed periodically, it may be used occasionally, or even once. For example, the optimization may be performed offline at the point where the over-rating controller is installed. Alternatively, the optimizer may be embodied in a controller of the wind turbine, the wind power plant or elsewhere, in which case it will be executed in specific time steps.

As described above, optimization may be performed with or without LUEs, as the site data may be used to determine component fatigue and thus give an indication of the remaining life available for the turbine or turbine component.

Although the optimization algorithm is primarily described as involving use with an over-rated controller, this is not a requirement. The optimization may be applied with any control action that compromises energy capture for turbine fatigue loads. This may include one or more of the following: changing power requirements, such as by derating; thrust limits that limit power output to prevent high thrust loads by reducing rotor thrust at the "knee" of the power curve at the expense of power output; or any other control feature that compromises energy capture and fatigue loading.

Although the required calculations may be performed anywhere, in practice such policy actions may be better performed in a wind power plant controller such as a SCADA server. This allows service data to be entered directly on site, avoiding communication problems from the site to the control center. However, the calculations may also be performed at the control center. Other methods described herein, including the method of the first example, are equally applicable.

Maximum power level calculation

Exemplary techniques for determining a maximum power level applicable to a turbine are now described next.

A method for determining a wind turbine type maximum power level for a type of wind turbine may comprise: simulating a load spectrum for two or more test power levels to determine a load on the type of wind turbine for each of the two or more test power levels; comparing the determined load for each test power level with a design load for the type of wind turbine; and setting a wind turbine type maximum power level for the type of wind turbine to a maximum test power level at which the determined load does not exceed a design load for the type of wind turbine.

Thus, a wind turbine type maximum power level may be determined for one or more types of wind turbines.

FIG. 8 shows a flow chart detailing an example of setting a turbine maximum power level that may be used with any embodiment. In step 301, a check is performed to determine wind turbine mechanical component design limits for one or more types of wind turbines. In this example, an offline computer system is used to determine the design limits. However, as will be appreciated, this functionality may be implemented by an online computer system or any other software and/or hardware associated with the wind turbine and/or the WPP.

Wind turbine type maximum power level is the maximum power level that a given type of wind turbine is allowed to produce when the wind is suitably high if the wind turbine of the given type is operating at the limits of the design loads of the components of the wind turbine. The wind turbine type maximum power level is effectively adapted to the design lifetime of the turbine. Thus, the wind turbine type maximum power level will typically be higher than the nominal nameplate rating of that type of wind turbine, as the nominal nameplate rating is generally a more conservative value.

One type of wind turbine used in the following examples and embodiments may be understood as a wind turbine having the same electrical system, mechanical system, generator, gearbox, turbine blades, turbine blade length, hub height, etc. Thus, any difference from the main structure or components of the wind turbine effectively creates a new type of wind turbine for the purposes of embodiments of the present invention. For example, the same wind turbine except for the difference in hub height (e.g., tower height) would be two different types of wind turbines. Similarly, the same wind turbine except for the different length of the turbine blades will also be considered as two different types of wind turbines. Additionally, 50Hz and 60Hz wind turbines are considered to be different types of wind turbines, being cold climate and hot climate designed wind turbines.

This type of wind turbine therefore does not necessarily correspond to an International Electrotechnical Commission (IEC) class of wind turbine, as different types of turbines may belong to the same IEC class of wind turbines, wherein each type of wind turbine may have a different wind turbine type maximum power level, based on the design and components of the wind turbine.

In the following example, the wind turbine is rated at a nominal nameplate rated power level of 1.65MW (1650KW) with a hub height of 78 meters and is designed for service under specific IEC wind class conditions.

Design limits for wind turbine type mechanical components may then be determined for this type of wind turbine by simulating a load spectrum for a first test over-rated power level to identify loads on the type of wind turbine for the first power level. The load may be a mechanical load, a fatigue load, any other load that the wind turbine may experience, or any combination of different loads. However, as will be appreciated, in this example, the mechanical load is considered to be other loads, such as fatigue loads may also be considered. The process of simulating the load spectrum may also include or be an inference or other form of analysis that may be performed to determine the load on that type of wind turbine.

The load spectrum typically includes a series of different test cases that may be run in a computer simulation of the wind turbine. For example, the load spectrum may include test cases with wind speeds of 8m/s for 10 minutes, 10m/s for 10 minutes, different wind directions, different wind turbulences, wind turbine start-up, wind turbine shut-down, etc. It should be appreciated that there are many different wind speeds, wind conditions, wind turbine operating conditions, and/or fault conditions for which there are test cases to be run in a wind turbine simulation of the load spectrum. The test case may include real, actual data or man-made data (e.g., 50 year high winds as defined in standards related to wind turbines). The simulation of the load spectrum may determine forces and loads affecting the wind turbine for all test cases in the load spectrum. The simulation may also estimate or determine the number of times a test case event may occur, e.g. a 10m/s wind, 10 minutes duration test case may be expected to occur 2000 times during the 20 year life of the wind turbine, and thus the fatigue of the wind turbine during the life of the wind turbine may be calculated. The simulation may also calculate or determine fatigue damage or loads that may be caused by various components in the wind turbine based on the determined loads affecting the wind turbine.

In this example, the first test power level may be 1700KW, as this is higher than the nominal nameplate rated power level for the type of wind turbine considered in this example. A load spectrum may then be simulated for a given type of wind turbine in order to determine whether the type of wind turbine can operate at the first test power level without exceeding the limit design loads of the mechanical components of the type of wind turbine. If the simulation determines that a wind turbine of this type can be operated at a first test power level, the same process can be repeated for a second test power level. For example, in this example, the second test power level may be 1725 KW. A load spectrum is then simulated for a given type of wind turbine to determine whether the type of wind turbine is capable of operating at the second test power level without exceeding the limit design loads for the mechanical components.

The process of simulating the load spectrum for additional test power levels may be performed iteratively if the limit design load of the mechanical component is not exceeded. In this example, the test power level is incremented in steps of 25KW, however, it should be understood that the incremental steps may be any suitable steps for the purpose of determining the maximum power level of the wind turbine type, e.g. 5KW, 10KW, 15KW, 20KW, 30KW, 50KW or the like, or the test power level may be increased by a percentage of the test power level, e.g. 1% increments, 2% increments, 5% increments or the like. Alternatively, the process starts with a high first test power level and for each iteration the test power level is decremented by a suitable amount until the wind turbine type maximum power level is determined, i.e. the first test power level at which the type of wind turbine can be operated without exceeding the limit design limits.

In this example, a wind turbine of a given type is determined to be capable of operating at additional test power levels of 1750kW, 1775kW and 1800kW before exceeding the design limits of one or more mechanical components at 1825 kW. Thus, the process determines that the wind turbine type maximum power level for this type of turbine is 1800 KW.

In further embodiments, since wind turbines of this type do not exceed the mechanical component's ultimate design load at 1800KW, but exceed the mechanical component's ultimate design load at 1825KW, the process may also iteratively increment the test power level in small increments, e.g., 5KW, to determine whether the wind turbine is capable of operating at power levels between 1800KW and 1825KW without exceeding the mechanical ultimate design load. However, in the present example, a power level of 1800KW is considered to be a wind turbine type mechanical component design limit for this type of wind turbine.

The process of determining the wind turbine type maximum power level may then be performed for any other type of wind turbine to be analyzed. In step 302 of FIG. 8, the design constraints of the electrical components in this type of wind turbine may be considered or evaluated for previously determined wind turbine mechanical component design constraints.

In step 302, the primary electrical components may be considered to ensure that the determined wind turbine type power level for the mechanical component design limit does not exceed the design limits of the primary electrical components of the type of wind turbine being analyzed. The main electrical components may include, for example, a generator, a transformer, an internal cable, a contactor, or any other electrical component in a wind turbine of this type.

Based on the simulation and/or calculation, it is then determined whether the main electrical component is capable of operating at the wind turbine type maximum power level previously determined for the mechanical component design limits. For example, operation at the mechanical component design limit power level may cause the temperature of one or more cables inside the wind turbine to increase and thus reduce the cable current carrying capacity, as determined by the size of the cable conductors and the heat dissipation conditions. Thus, the current carrying capacity will be calculated for the new temperature conditions in order to determine whether the cable can be operated at a power level up to the maximum power level of the wind turbine type. Similar reasons may be considered for other electrical components, such as temperature of the component, capacity of the component, and so forth, to determine whether the electrical component is capable of operating at power levels up to the mechanical component design limit.

If it is determined or determined that the primary electrical component is capable of operating at the previously determined mechanical component design limit, then in step 303 of FIG. 8, for a given type of wind turbine, the wind turbine type maximum power level is set or recorded as the maximum power level for the given type of wind turbine according to the mechanical component design limit. However, if it is determined or determined that the primary electrical component is not capable of operating at the previously determined mechanical component design limits, further investigation or further action may be taken to derive a turbine type maximum power level that accommodates both the mechanical and electrical components.

Once the wind turbine type maximum power level has been determined for each type of wind turbine, this parameter may be used as a constraint within the method described above to derive an arrangement of individual maximum power levels, e.g. maximum over-rated power levels, for each wind turbine in the WPP.

The different individual maximum power levels for each wind turbine in the WPP is advantageous because conditions in the WPP may vary throughout the site of the WPP. Thus, it may be the case that a wind turbine at one location in a WPP may face different conditions than another wind turbine of the same type at a different location in the WPP. Thus, two wind turbines of the same type may require different individual maximum power levels, or depending on the preferred embodiment, the lowest individual maximum power level may be applied to all wind turbines of that type in the WPP. As described herein, individual wind turbine-specific individual maximum power levels are determined as part of the determination schedule.

Over-rating control

Embodiments of the invention may be applicable to wind turbines or wind power plants that operate by applying an over-rating control to determine an over-rating amount to apply.

The over-rating control signal is generated by an over-rating controller and used by the wind turbine controller to over-rate the turbine. The control arrangement described above may be used within or with such an over-rating controller to set an upper limit on the amount of power that can be generated by over-rating. The specific manner in which the over-rating control signal is generated is not important to embodiments of the present invention, but examples will be given for ease of understanding.

Each wind turbine may comprise an over-rating controller as part of the wind turbine controller. The over-rating controller calculates an over-rating request signal indicating that the turbine is configured to over-rating to an amount of power output above the rated output. The controller receives data from the turbine sensors, such as pitch angle, rotor speed, power output, etc., and may send commands, such as set points for pitch angle, rotor speed, power output, etc. The controller may also receive commands from the grid, for example, from a grid operator to boost or reduce active or reactive power output in response to a demand or fault in the grid.

FIG. 9 shows a schematic example of a turbine controller arrangement in which an over-rating controller 901 generates an over-rating control signal that may be used by a wind turbine controller to apply an over-rating to a turbine. The over-rating control signal may be generated depending on the output of one or more sensors 902/904 detecting operating parameters of the turbine and/or local conditions such as wind speed and direction. The excess amount controller 901 includes one or more functional control modules that may be used for aspects of excess control. Additional functional modules may be provided, the functionality of the modules may be combined, and certain modules may be omitted.

The optimizer 907 provides values of the individual turbine maximum power levels according to the determined schedule as described herein. This provides a maximum power level at which the turbine can operate, according to the arrangement.

The additional functionality module generates a power demand and is typically used to reduce the final power demand acted on by the turbine controller. A specific example of an additional functionality module is an operational constraints module 906. Overrating utilizes the gap typically existing between component design loads and the loads experienced by each turbine in operation, which is generally better than the IEC standard simulation conditions under which the design loads are calculated. Over-rating causes the power demand of the wind turbine to increase in strong winds until an operating limit specified by operating constraints (temperature, etc.) is reached, or until an upper power limit set to prevent exceeding component design loads is reached. The operating constraints implemented by the operating constraint control module 906 limit the possible over-rated power requirements based on various operating parameters. For example, in the event that the protection function properly initiates shutdown when the gearbox oil temperature exceeds 65 ℃, the operating constraints may indicate a linear drop in the maximum possible over-rating set point signal based on the gearbox oil temperature exceeding 60 ℃ reaching "no over-rating" (i.e., the power set point signal equals the rated power) at 65 ℃.

The minimum function block 908 is provided with the maximum power level and power requirements of the functional module and selects the minimum value. An additional minimum block 909 may be provided that selects the minimum power demand from the over-rating controller 901 and any other turbine power demand, such as that specified by the grid operator for generating the final power demand imposed by the wind turbine controller.

Alternatively, for example, the over-rating controller may be part of the PPC controller 130 of FIG. 1B. The PPC controller is in communication with each of the turbines, and may receive data from the turbines, such as pitch angle, rotor speed, power output, etc., and may send commands, such as set points for pitch angle, rotor speed, power output, etc., to the individual turbines. The PPC 130 also receives commands from the grid, such as commands from a grid operator to boost or reduce the active or reactive power output in response to a demand or fault of the grid. The controller of each wind turbine is in communication with the PPC 130.

The PPC controller 130 receives power output data from each of the turbines and therefore knows the power output by each turbine and the power plant as a whole at the grid connection point 140. If desired, the PPC controller 130 may receive an operating set point for the power output of the power plant as a whole and divide it among each turbine so that the output does not exceed an operator specified set point. The power plant set point may be any value from 0 to the rated power output of the power plant. The "rated power" output of the power plant is the sum of the rated power outputs of the individual turbines within the power plant. The power plant set point may be higher than the rated power output of the power plant, i.e. the entire power plant is over-rated.

The PPC may receive input directly from the grid connection or may receive a signal that is a measure of the difference between the total power plant output and the nominal or rated power plant output. This difference may be used to provide a basis for over-rating of each turbine. In theory, only a single turbine may be over-rated, but it is preferred to over-rate a plurality of turbines, and most preferred to send an over-rating signal to all turbines. The over-rating signal sent to each turbine may not be a fixed control but may be an indication of the maximum amount of over-rating that each turbine may perform. Each turbine may have an associated controller, which may be implemented within the turbine controller or, for example, centrally at the PPC, which will implement one or more of the functions shown in fig. 9 to determine whether the turbine is able to respond to the over-rating signal and, if so, the amount of the over-rating signal. For example, where a controller within the turbine controller determines that conditions at a given turbine are favorable and above rated wind speed, it may respond positively and the given turbine is over-rated. Since the controller implements the over-rating signal, the output of the power plant will rise.

Thus, an over-rating signal is generated centrally or at each individual turbine, which signal is indicative of the amount of over-rating that can be performed by one or more turbines or turbines of the power plant as a whole.

Lifetime usage estimator

As described above, embodiments of the present invention utilize a Lifetime Usage Estimator (LUE). The lifetime usage estimator will now be described in more detail. The algorithms required to estimate life usage vary from component to component, and LUEs may comprise a library of LUE algorithms including some or all of the following: load duration, load rotation profile, rain flow count, stress cycling damage, temperature cycling damage, generator thermal reaction rate, transformer thermal reaction rate, and bearing wear. Other algorithms may alternatively be used. As described above, life usage estimation is only available for selected critical components, and the use of a library of algorithms enables the selection of a new component for an LUE and the selection of an appropriate algorithm from the library and the specific parameters set for that component.

In one embodiment, the LUE is implemented for all major components of the turbine, including the blades; a pitch bearing; a pitch actuator or drive; a hub; a main shaft; a main bearing housing; a main bearing; a gearbox bearing; gear teeth; a generator; a generator bearing; a converter; a generator junction box cable; a yaw drive; a yaw bearing; a tower; an offshore support structure, if present; a foundation; and a transformer winding. Alternatively, one or more of these LUEs may be selected.

As an example of a suitable algorithm, rain flow counting may be used in blade structures, blade bolts, pitch systems, main shaft systems, converters, yaw systems, towers and foundation estimators. In the blade structure algorithm, the rain flow counts are applied to the blade root bending wing direction (flapwise) and flange direction (edgewise) moments to determine the stress cycle range and average, and the output is sent to the stress cycle damage algorithm. For the blade bolts, the rain flow counts are applied to the bolt bending moments to determine the stress cycle range and average, and the output is sent to the stress cycle damage algorithm. In the pitch system, the main shaft system, the tower and the foundation estimator, a rain flow counting algorithm is also used to determine the stress cycle range and average and send the output to the stress cycle damage algorithm. Parameters for applying the rain flow algorithm may include:

-pitch system-pitch force;

-main shaft system-main shaft torque;

-tower stress;

-base stress.

In a yaw system, a rain flow algorithm is applied to the tower top torque to identify load duration and this output is sent to a stress cycle damage algorithm. In the converter, the generator power and RPM are used to infer the temperature, and rain flow counting is used over that temperature to identify the temperature cycle and average.

The life usage of the blade bearing may be monitored by inputting the blade airfoil directional load and pitch rate as inputs to a load duration algorithm or a bearing wear algorithm. For the gearbox, the load rotation duration is applied to the main shaft torque to calculate the lifetime used. For the generator, the generator RPM is used to infer the generator temperature, which is used as an input to the generator thermal reaction rate algorithm. For transformers, transformer temperature is inferred from power and ambient temperature to provide input to the transformer thermal reaction rate algorithm.

Where possible, it is preferred to use existing sensors to provide inputs on which the algorithm operates. Thus, for example, wind turbines typically directly measure the blade root flange direction and wing moment required for blade structure, blade bearings, and blade bolt estimators. For a pitch system, the pressure in the first chamber of the cylinder may be measured and the pressure in the second chamber inferred, enabling the pitch force to be calculated. These are examples only, and other parameters required as inputs may be measured directly or inferred from other available sensor outputs. For some parameters, it may be advantageous to use additional sensors if values cannot be inferred with sufficient accuracy.

Algorithms for various types of fatigue assessment are known and can be found in the following standards and texts:

load rotation profile and load duration:

wind turbine certification guidelines, Germainischer Lloyd, section 7.4.3.2 fatigue loads

Rain flow:

IEC 61400-1 wind turbine-part 1: design requirements, Accessories G

Miners' summation:

IEC 61400-1 wind turbine-part 1: design requirements, Accessories G

Power law (chemical decay):

IEC 60076-12 "power transformer-part 12: loading guide for dry power transformers ", section 5.

Power plant level control

Any of the methods described herein may be performed at the wind power plant level, thereby generating a plant control arrangement comprising an individual control arrangement for each wind turbine. This has the benefit of allowing the interaction between turbines in a given power plant to be taken into account.

Changes in the power demand/power level of the one or more upstream turbines affect the power output and the rate of fatigue damage accumulation for any turbine following the one or more upstream turbines. The site inspection software includes information about the positioning of the turbines within the wind power plant and takes into account the relative positions of the turbines within the wind power plant with respect to each other. The wake effect of the upstream turbine is therefore taken into account in the calculation by the site inspection software.

In the case of some wind power plants, the power carrying capacity of the connection from the plant to the utility grid is less than the sum of the power generated by each turbine in the case when all turbines generate power at the wind turbine type maximum power level. In this case, the control schedule of the wind turbines or wind power plant is also constrained such that for any given period of time within the schedule, when the power of all the turbines is added together, it does not exceed the amount of power that can be carried in the connection from the power plant to the grid.

The embodiments described herein rely on analysis of turbine characteristics and turbine site characteristics to determine a control schedule for the turbine. Various calculations, including those performed by site inspection software, may be performed offline at one or more different computing systems, and the resulting control arrangement provided to the wind turbine or power plant controller. Alternatively, the calculations may be performed online at the wind turbine controller or the power plant controller.

The embodiments described above are not exclusive and one or more of the features may be combined or may cooperate to achieve improved over-rating control by setting a maximum power level for each wind turbine in the wind power plant, taking into account environmental and site conditions to which the wind turbine is exposed or affected.

It should be noted that embodiments of the present invention may be applied to both constant and variable speed turbines. Turbines may employ active pitch control, whereby power limits above rated wind speed are achieved by feathering, which involves rotating all or part of each blade to reduce the angle of attack. Alternatively, the turbine may employ active stall control, which achieves power limits above rated wind speed by pitching the blades to stall (in the opposite direction to that of active pitch control).

While embodiments of the present invention have been shown and described, it will be understood that they have been described by way of example only. Numerous variations, changes and substitutions will occur to those skilled in the art without departing from the scope of the invention as defined by the appended claims.

Claims

1. A method of generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the method comprising:

estimating a future fatigue life consumed by the turbine or the turbine component for a duration of the altered control schedule based on the current remaining fatigue life and the altered control schedule; and

2. The method of claim 1, further comprising:

the control schedule is optimized by changing the timing of the component replacements and changing the number of component replacements until a maximum number is reached.

3. The method of claim 1 or 2, wherein the one or more turbine components that can be replaced comprise one or more of: a blade, a pitch bearing, a pitch actuation system, a hub, a main shaft, a main bearing, a gearbox, a generator, a converter, a yaw drive, a yaw bearing or a transformer.

4. A method according to claim 1 or claim 2, wherein the initial control schedule specifies a relative variation over time of the turbine maximum power level at which the turbine can operate.

5. A method according to claim 1 or 2, wherein the input constraints further comprise an upper maximum power output of the turbine and/or a minimum power output of the turbine as allowed by the turbine design.

6. The method of claim 1 or 2, wherein determining a value indicative of the current remaining fatigue life of the turbine or the one or more turbine components comprises: sensor data from one or more turbine sensors is applied to one or more life usage estimation algorithms.

7. The method of claim 1 or 2, wherein determining a value indicative of the current remaining fatigue life of the turbine or the one or more turbine components comprises: data from the condition monitoring system is used.

8. The method of claim 1 or 2, wherein determining a value indicative of the current remaining fatigue life of the turbine or the one or more turbine components comprises: data obtained from wind farm sensors is used in conjunction with a site check program that determines loads acting on turbine components based on the wind farm sensors and parameters related to the wind farm and the wind turbine design.

9. The method of claim 1 or 2, wherein the optimization of the control schedule comprises:

the control arrangement is changed to minimize the flattening energy cost.

10. The method of claim 9, wherein the flattened energy cost is determined using a flattened energy cost model, the model including parameters for one or more of:

a capacity coefficient indicative of the energy generated over a time period divided by the energy that could be generated if the turbine were to continue operating at rated power over the time period;

availability indicating a time at which the turbine can be used to generate electricity; and

a field efficiency indicating the energy generated over a period of time divided by the energy that can be generated if the turbine is operating in a wind completely undisturbed by the upstream turbine.

11. The method of claim 10, wherein the model further comprises parameters of one or more of:

costs associated with replacing one or more components, including turbine downtime, labor and equipment for component replacement, manufacturing or refurbishment costs for replacement components, and transportation costs of refurbished components or the replacement components to a power plant; and

the cost of service associated with replacing worn components.

12. A method according to claim 1 or 2, wherein the optimised control schedule is a schedule of maximum power levels to which the turbine can operate.

13. The method of claim 1 or 2, wherein the control schedule indicates an amount of fatigue damage that should be caused over time, the method further comprising: operating the wind turbine based on one or more lifetime usage estimators to cause fatigue damage at a rate indicated by the control schedule.

14. A method according to claim 1 or 2, wherein the control schedule specifies a maximum power level above the rated power of the wind turbine.

15. A method according to claim 1 or 2, wherein the control schedule indicates how the turbine maximum power level varies over the lifetime of the turbine.

16. A method according to claim 1 or 2, further comprising providing the optimized control arrangement to a wind turbine controller or a wind power plant controller for controlling the power output of a wind turbine.

17. The method of claim 1 or 2, wherein the method is repeated periodically.

18. The method of claim 17, wherein the method is repeated once a day, month or year.

19. A controller for a wind turbine or wind power plant, the controller being configured to perform the method of any of claims 1 to 18.

20. An optimizer for generating a control schedule for a wind turbine, the control schedule indicating how the turbine maximum power level varies over time, the optimizer comprising:

wherein the optimization module is configured to:

Outputting the optimized control schedule;

21. The optimizer of claim 20, further comprising an initialization module configured to receive sensor data and initial values of the set of variables, the initialization module configured to calculate initial values of the operating parameters.

22. The optimizer of claim 20 or 21, wherein the one or more turbine components are one or more of: a blade, a pitch bearing, a pitch actuation system, a hub, a main shaft, a main bearing, a gearbox, a generator, a converter, a yaw drive, a yaw bearing or a transformer.

23. The optimizer of claim 20 or 21, wherein the operating parameter is a flattened energy cost of the turbine and optimizing the control arrangement comprises minimizing the flattened energy cost.

24. The optimizer of claim 23, wherein the flattened energy cost is determined using a flattened energy cost model comprising parameters of one or more of:

25. The optimizer of claim 24, wherein the model further comprises parameters of one or more of:

the cost of service associated with replacing worn components.

26. A controller comprising an optimizer as claimed in any of claims 20 to 25.

27. A wind turbine comprising a controller according to claim 26.

28. A wind power plant comprising a controller according to claim 26.

29. A method of generating a control schedule for a wind power plant comprising a plurality of wind turbines, the control schedule indicating for each wind turbine how the maximum power level varies over time, the method comprising:

determining a value indicative of a current remaining fatigue life of each of the plurality of turbines or one or more components of each of the plurality of turbines based on measured wind turbine site data and/or operational data;

applying an optimization function that changes an initial control schedule for each of the plurality of turbines to determine an optimized control schedule by changing a tradeoff between fatigue life consumed by each of the plurality of turbines or the one or more turbine components of each of the plurality of turbines and energy capture until an optimized control schedule is determined, the optimization comprising:

estimating future fatigue life consumed by the turbine or the turbine component for the duration of the altered control schedule based on the current remaining fatigue life and the altered control schedule using a site check program that determines loads acting on turbine components based on data obtained from wind power plant sensors and parameters related to the wind power plant and wind turbine design and includes interactions between the plurality of turbines of the wind power plant; and

wherein the constraints include a maximum number of allowable component replacements for each of the one or more turbine components of each of the plurality of wind turbines, and the optimization function is further applied to change an initial value of wind turbine life to determine a target wind turbine life.

30. A method according to claim 29, wherein the data obtained from wind power plant sensors comprises sensor data collected prior to commissioning and/or building the wind turbine or the wind power plant.

31. A method according to claim 29 or 30, wherein the optimisation function varies, for one or more of the turbine components, the number of times that component can be replaced during the remaining life of the turbine.

32. A method according to claim 31, wherein the optimisation function makes changes to one or more of the turbine components as to when the components can be replaced during the remaining life of the turbine.

33. A method according to claim 29 or 30, wherein the method is further constrained such that for any given period of time within the schedule, when the power of all the turbines is added together, the sum of the power does not exceed the amount of power that can be carried in the connection from the power plant to the power grid.

34. A wind power plant controller configured to perform the method of any of claims 29 to 33.