WO2018203891A1

WO2018203891A1 - Control system for a class of wind turbines and methods of using the same

Info

Publication number: WO2018203891A1
Application number: PCT/US2017/030817
Authority: WO
Inventors: Alexis Motto; Cristovian BASDEN; Chao Yuan; Sridharan Palanivelu; Bryan GILLENWATER; Noah SCHELLENBERG; Jennifer ZELMANSKI; Akshay PATWAL; Amit Chakraborty; Michael May; Matthew Evans
Original assignee: Siemens Energy, Inc.
Priority date: 2017-05-03
Filing date: 2017-05-03
Publication date: 2018-11-08

Abstract

Systems and methods for maximizing efficiency and output performance for one or more wind turbines (10) similarly situated and positioned in a geographical site (1). The system includes at least a controller (200) operably connected to the wind turbines and sensors (300) positioned in or proximate to the site for monitoring environmental conditions. The controller receives operation and performance information for each wind turbine and based on the received information establishes an operational parameter that is set as a baseline parameter for adjusting any underperforming wind turbines operational parameters.

Description

CONTROL SYSTEM FOR A CLASS OF WIND TURBINES AND

METHODS OF USING THE SAME

TECHNICAL FIELD

The present disclosure relates generally to control and computing systems, and more particularly, to control systems for a class of wind turbine and methods of using the same.

BACKGROUND

Wind turbines generate electrical power using wind energy where an electrical generator is driven by the rotation of a rotor that converts the wind energy into rotational energy. A plurality of wind turbines are traditionally grouped together in a wind farm or wind park at a given geographical site. To maximize efficiency for each wind turbine (turbine), prior art monitoring technologies based on a mapping of wind speed versus real power are generally utilized for adjusting the wind turbine. This mapping is called a power curve. The International Electrotechnical Commission standardized the estimation of the power curve using a nonparametric approach, known as the "binning method." Given the wind speed and real power observation data, the binning method divides the domain of wind speed into a finite number of intervals or bins. The power curve is then obtained as a mapping of sample mean of wind speed versus sample mean of real power, using all observation data falling within each bin. Fig. 5 illustrates prior art filtered power curves for five turbines, i^{! >}^- and ^" °, using observation data over a two-year period, divided into

25 bins.

The wind turbines are on the same geographical site and, for a given wind speed, the corresponding real power is the sample conditional expected value of the real power over the period considered: ^AP j^ s^

Once constructed, the power curve is then used to predict real power at any wind speed, by mapping or interpolation. It should be appreciated that this methodology requires prior construction of power curves, which may be meaningful only after observing data over a sufficiently long period of time.

Other multi-variable methods for maximizing efficiency require prior construction of high-dimensional power curves, which may be meaningful only after observing data over a sufficiently long period of time. In essence, they require more data and are more difficult to understand and to implement than the current standard practice.

Because wind speed always fluctuates, and because the wind speed used for the power curve is not exact, an uncertainty remains in the use of power curves for maximizing the efficiency of the wind turbine operation.

SUMMARY

An object of the present disclosure is to provide an improved dynamic framework to maximize the performance of wind turbines. The inventors have developed a new control system which maximizes the performance of wind turbines by identifying the environmental conditions surrounding the wind turbines, e.g., within a geographical site, and utilizing data related to the identified conditions to adjust one or more control parameters of the wind turbine.

The new framework offers an important tool for improving the performance of utility-scales turbines operating in a defined geographical site. A site may be onshore or offshore. The method requires no construction of any actual or statistical performance curves, support model uncertainties, disturbances, soft operational constraints and hard physical constraints on decision variables. Instead, at least three key input variables are assumed and measured for each monitored turbine: wind speed, ambient temperature, and humidity. One key output variable is also assumed and measured for each monitored turbine: real power output. For a given site, a mathematical mapping or estimator of performance loss of each monitored turbine is derived using the aforementioned measurement data. In one embodiment, a method for adjusting an operation of one or more wind turbines is provided. The method generally includes the step of monitoring a plurality of wind turbines that have a similar configuration and which are similarly situated in an environment. The method further includes the step of establishing a baseline operation based on the output performance of one or more of the monitored wind turbines performance in the environment, and adjusting an operation of one or more of the plurality of wind turbines to or about the baseline operation to maximize performance of the one or more adjusted wind turbines in the environment.

In yet a further exemplary embodiment, a method for adjusting one or more wind turbines may include the step of establishing or identifying an equivalence class of wind turbines. After identifying the equivalence class, a baseline operation parameter may be generated based on at least one of the wind turbines in the equivalence class operating a maximum performance rate. The method further includes adjusting one or more wind turbines in the equivalence class not operating at the maximum performance rate to the baseline operation for maximizing the output performance of the adjusted wind turbines.

In another exemplary embodiment, a system for monitoring and adjusting one or more wind turbines is provided. The system may include a plurality of wind turbines similarly configured and situated within a geographical site or class. Each wind turbine may include a competitive agent configured to determine wind speed and/or produce power for the wind turbine based on its mechanical energy. The system may include a controller operably connected to each wind turbine and one or more sensors selectively positioned within the site or the equivalence class. The sensors may be configured to sense environmental conditions surrounding the wind turbines, and to transmit or communicate those conditions to the controller for adjusting operation parameters of the wind turbines. Each wind turbine's competitive agent may submit a bid to the controller, the bid being indicative of the wind turbines performance. The controller may then define a performance index based in part on the bid. The performance index or a value of the performance index may then be submitted to each competitive agent to allow for the agent to adjust the operational parameters of its wind turbine to reduce the value of the performance index.

In yet another exemplary embodiment, a non-transitory computer-readable medium storing therein programming logic(s) that causes a controller to adjust an operation of one or more wind turbines may be provided. In this embodiment, programming logic may be operable to identify the operating parameters for each of the wind turbines, and to determine which turbines are performing at a maximum performance rate and which may be underperforming. Additionally, the programming logic may be operable to generate operating parameters based in part on the current active operating parameters for any wind turbines operating at the maximum performance rate, and to compare the generated parameters to the active operating parameters of any underperforming wind turbines. When comparing both parameters, the medium may include programing logic for adjust the current underperforming wind turbine operating parameters to about the generated parameters to increase performance of the underperforming turbines, and to reduce, e.g., any costs or indices indicative of the wind turbines underperformance.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 illustrates a block diagram of an embodiment of a system for maximizing wind turbine performance in accordance with the disclosure provided herein;

Fig. 2 illustrates an exemplary embodiment of a controller that may be utilized in the system of Fig. 1, in accordance with the disclosure provided herein;

Fig. 3 illustrates a flowchart for an embodiment of a method for maximizing wind turbine performance, in accordance with the disclosure provided herein; Fig. 4 illustrates a second flowchart for an embodiment of a method for maximizing wind turbine performance, in accordance with the disclosure provided herein;

Fig. 5 illustrates a third flowchart for an embodiment of a method for maximizing wind turbine performance, in accordance with the disclosure provided herein; and

Fig. 6 illustrates observation data of filtered power curves for a plurality of wind turbines over a two year period.

DETAILED DESCRIPTION

The components and materials described hereinafter as making up the various embodiments are intended to be illustrative and not restrictive. Many suitable components and materials that would perform the same or a similar function as the materials described herein are intended to be embraced within the scope of embodiments of the present invention.

In general, the computing systems and devices described herein may be assembled by a number of computing components and circuitry such as, for example, one or more processors (e.g., Intel®, AMD®, Samsung®) in communication with memory or other storage medium. The memory may be Random Access Memory (RAM), flashable or non- flashable Read Only Memory (ROM), hard disk drives, flash drives, or any other types of memory known to persons of ordinary skill in the art and having storing capabilities. The computing systems and devices may also utilize cloud computing technologies to facilitate several functions, e.g., storage capabilities, executing program instruction, etc.

The computing systems and devices may further include one or more communication components such as, for example, one or more network interface cards (NIC) or circuitry having analogous functionality, one or more one way or multi-directional ports (e.g., bidirectional auxiliary port, universal serial bus (USB) port, etc.), in addition to other hardware and software necessary to implement wired communication with other devices. The communication components may further include wireless transmitters, a receiver (or an integrated transceiver) that may be coupled to broadcasting hardware of the sorts to implement wireless communication within the system, for example, an infrared transceiver, Bluetooth transceiver, or any other wireless communication know to persons of ordinary skill in the art and useful for facilitating the transfer of information.

Referring now to the drawings wherein the showings are for purposes of illustrating embodiments of the subject matter herein only and not for limiting the same, FIG 1 illustrates a block diagram of an embodiment of a system 100 for maximizing wind turbine performance.

As illustrated in Fig. 1 , the system 100 may include one or more controllers 200 operably connected to one or more of the wind turbines 10 within a geographical site 1 , e.g., a wind turbine park or farm, and one or more sensors 20 via a wired or wireless communication link 15.

The sensors 20 may be positioned in or proximate to the wind turbines and/or the site

1, for sensing conditions at or around the wind turbine and/or the site 1 , and for transmitting the sensed conditions to the controller 200 for adjusting one or more parameters of the wind turbine(s) in response to the conditions. The sensors 20 may be any type of sensor capable of sensing the environmental conditions transmitting those sensed conditions to the controller

200 for adjusting the operational parameters of the wind turbines 10 to improve performance and efficiency.

Examples of the types of environmental conditions may include weather related conditions, e.g., condition related to rain or snow. Additional conditions may include, e.g., humidity, turbulence, wind speed and/or direction, precipitation, air density and temperatures, e.g., ambient temperature. It should be appreciated that other sensors known in the art, e.g., acoustic and optical sensors, and load-balance and displacement sensors may also be provided in the system to assist with adjustment of the operational parameters of the wind turbines, e.g., via the controller 200.

The wind turbines 10 may be positioned randomly within the site 1 or in a predetermined manner to define, e.g., one or more groups or classes of wind turbines 10. In the embodiment illustrated in Fig. 1 , a plurality of wind turbines 10 are arranged in three classes 1 10, 120, and 130. It should be appreciated that the wind turbines may be group based on their positions relative to one another, based on the configurations of the wind turbines, or based on other considerations, e.g., the types of conditions the wind turbines may be exposed to within the site 1.

With continued reference to the figures, the wind turbine 10 may be positioned or similarly situated to one another within the site 1 or group 110, 120, 130 such that the wind turbines 10 may be exposed to identical or similar conditions, e.g., environmental conditions, which may affect performance, e.g., output performance, of each wind turbine 10. Various types of conditions may include, e.g., humidity, turbulence, wind speed and/or direction, air density, temperatures surrounding the wind turbines or within the site 1, weather, or any other conditions known in the art to affect or impact wind turbine 10 performance. Additionally or alternatively, each wind turbine 10 may include similar or the same components and be operationally configured with similar or the same initial operational parameters based on the environmental conditions and the wind turbines location within the geographical site 1.

With continued reference to the figures, and now Fig. 2, the controller 200 may include at least a processor 202 operably connected to a memory 204 for executing one or more instructions or commands of a control application 300 stored in the memory 204, or other data storage component 206 operably connected to the processor 202, e.g., a hard disk drive, solid-state drive etc. The controller 200 may further include a user interface (not shown), which may be any general interface for receiving user input and generating a displayable output on a display (not shown). The controller 200 may also include a network adapter/transceiver 208 to facilitate communication between the controller 200 and other devices of the system 100, e.g., for receiving and transmitting information related to the wind turbine 10 operation and/or site 1 conditions to and from the wind turbines 10, sensors, or a further controller (not shown).

It should be appreciated that the controller 200 may be located within the site 1, e.g., as a standalone entity or within one of the wind turbines 10, or at a remote location, e.g., a location not affected by the environmental conditions. It should also be appreciate that in an embodiment where multiple controllers 20 are utilized, the controllers 200 may be located in a combination of places, e.g., within the site 1 and external to the site 1. This may assist in providing a form of redundancy should one or more of the controller 200 become unavailable, e.g., due to the environmental conditions. Additionally or alternatively, each wind turbine 10 may include its own controller 200 to facilitate the adjusting of that wind turbines operational parameters as described herein.

With continued reference to the figures, the controller 200 may further include a parameter generator 210 for generating operational parameters for one or more of the wind turbines which may be based on the sensed conditions received by the controller 200. It should be appreciated that the generated parameters may be, e.g., the initial operating parameter or the updated parameters for the wind turbine 10. Once any parameters are generated, the parameter may be transmitted to the wind turbine or controller/control unit of the wind turbine, e.g., via the communication link 15 or via any means known to persons of ordinary skill in the art.

The control application 300 may be a series of executable instructions which may be executed, e.g., by the processor 202. The series of instructions may generally include, e.g., instructions for receiving and processing information, e.g., sensed conditions, from one or more sensors situated at or near the wind turbines 10. Additionally or alternatively, the control application may include instructions for generating or updating operational parameters for one or more wind turbines 10 based on the sensed data and current or initial operating parameters of the wind turbines 10 to maximize efficiency and performance.

Consider the following:

NOMENCLATURE

The main notation used throughout the present paper is summarized below.

Acronyms:

PI - Power Performance Index; TB - Turbine

Parameters:

TB

π _ALi Altitude of turbine i;

π _L™ Latitude of turbine i;

π _L™ Longitude of turbine i;

TB

π . Minimum wind speed at rated real power of turbine i;

π _RSi Restart wind speed of turbine i.

Variables:

TB

u _έ Generator speed of turbine i at time t;

TB

u _PAtbk Pitch angle of blade b of turbine i at time t;

TB

u _έ Yaw position of turbine i at time t;

TB

y _Amt Air density at turbine i at time t;

TB

y _Apit Real power output of turbine i at time t;

y _A™_t Ambient temperature at turbine i at time t;

TB

y _ELi Energy loss of turbine i at time t;

TB

y . Humidity at turbine i at time t; y _p T_uB_t Real power loss of turbine i at time t;

y _p T_RB_it Precipitation at turbine i at time t;

y _T T_UB_it Turbulence at turbine i at time t;

y _w T_mB _t Wind direction at turbine i at time t;

y ^ Wind speed at turbine i at time t.

Other Mathematical Symbols: If x is a variable, then— and -^i; denote its lower bound and upper bound, respectively. If XY denotes the two-letter mnemonic of some object ,

the index set of the corresponding object instances. For example, TB is the two-letter mnemonic for the class of wind turbines and ^ "denotes the index set of wind turbines. As described herein, ^'rtX Y is a shorthand for the cardinality of the index set

. If x denote a vector, the symbol ^' denotes the dimension of x. ^s is shorthand for the expected value of x, and ^:- ^"■ '-' ^J as the conditional expectation of x given y. Let ^!···^{>ί 1} be a sequence of random vectors. The symbol ί denotes the Hilbert space spanned by the vectors I* *_' " - > Vt that is, = ^apim ' ' The symbol denotes the Hilbert space spanned by ^ⁱ (i.e. at time t only): ^{! t} = span *^■■" '· ·' . If is a vector space, dim(K) denotes the dimension of V. Other symbols are defined herein in the subsections where they are used.

CONSIDER '"' ^{r !} ' ^{ί ϊ Β} ^ ^*' identical utility-scale wind turbines 10 commissioned in a given geographical site 1 to convert mechanical energy from wind speed into electrical energy. Barring any binding operational or design constraints, each turbine may operate insomuch as to efficiently harvest and convert the maximum amount of prevailing mechanical energy into electrical power. Any turbine whose operation may deviate from the maximum-power-harvesting engineering design principle is considered as under-performing. Performance monitoring aims at detecting under-performing wind turbines 10, and performance maximization aims at improving the performance of any under-performing turbines 10. In general, wind turbines 10 may operate automatically, i.e., self-starting when the

,, ,ΤΒ

'if

wind speed is above the cut-in speed, here denoted — ^WSif for turbine i at time t. During

.. . ·

operation below the rated power, here denoted — for some turbine i at time t, the pitch angle and rotor speed may be continuously adjusted to maximize the aerodynamic efficiency. Rated power is reached at a wind speed between the cut-in and maximum operational limit

,„TB ,„TB values, here denoted by " *?*, for some turbine i at time t. Wind speeds above the real power output is regulated at rated power. Speed compliance during power regulation minimizes the dynamic loads on the transmission system. If the average wind speed exceeds ΐ/^Τ . ,

the maximum operational limit, here denoted— for some turbine i at time t, the turbine is shut down by feathering of the blades. When the wind drops back below the restart speed,

.... ^'Hi

here denoted "κ-s-ii for some turbine i at time t, the safety systems reset automatically. Monitoring the performance of utility-scale wind turbines is of paramount practical energy relevance.

With continued reference to the figures, and now the performance index, the new data analytics framework may require a new index for each turbine measuring under-performance. For reference and a self-contained exposition, at a given time t > 0, we define the new index as the energy per unit time (or real power) below some adapted threshold. Mathematically, we express the performance index as follows:

Vviit - -ruit - _^ j #ΑΜτ ^Ί - V ⁱ 2 , v f / . { I ) where ^{!' τ;: ; ί} is an auxiliary variable, used here for notational convenience, and defined as:

v*€Γ^Β, ¾ r. (2)

Note that, by definition, ^{TH¾i —} ' - ^{¾iA! r} . Hence, ^'#vi» — " . Integrating over the period [0, t] yields the corresponding performance energy index:

. T8 / . -TS V,' ■- - -7^* .'^'--J \

. a

For computational efficiency, it may be helpful to recast the indices in vector form, for all turbines. Let ¾ si, for all * fc / , denote a permutation matrix such that the following condition holds:

··> = Ci/L l < x-^<2 ≤ -■ - < . ,_ri; (4) where x denotes an ^f?TB -dimensional vector whose components are * ^{1 s} ' ^{' " !} .

DTK

The condition (4) states that applying the permutation matrix ¾to the wind speed vector, observed at time t, for all ^ T _; yields a new vector of the observed wind speed values sorted in ascending order. Using the permutation matrix, the performance power index can be recast in a compact vector form as follows:

(5)

Integrating component-wise over the period [0, 7] yields the associated performance energy index in vector form: With reference now to the performance ranking score for ranking wind turbines 10 over a given time period, the new data analytics framework may require a new performance ranking score for each turbine, measuring some degree of underperformance with respect to comparable wind turbines 10 similarly situated. For reference and a self-contained exposition, we outline the main mathematical formulation as follows:

Given n wind turbines, n > 1, and a time period -^{s >} we define an n x n matrix S, here called the performance score matrix, as a convex combination of ^! ', v -^■ r^■'^<r^■ lin 1k ma ₊tri ·ces - · * ^ { ^s 0.^■ 1 2} ³ : :

i /c >

17_Ί =—^■ — ψ , ¾, j { f&.

EL; & f_t(f Pi'j (t)dt ^' where ^it?.^; is a binary variable that is equal to 1 if there is a link from node i to j, or 0, otherwise; denotes the performance index of i with respect to j. Here, we use the convention o ^"~ ' .

Dynamic game model:

We define a stochastic dynamic game for a large number of the wind turbines 10 operating in a given geographical site 1. Following the game theory framework, assume that each turbine 10 is managed by a competitive agent that seeks to maximize some utility or performance index function. At every time t, each competitive agent submits, e.g., to a central process, e.g., controller 200, a bid that consists of the measured wind speed and power produced by the agent. The central process computes the performance indices and communicates the value to each associated turbine 10 agent. The agents react by adjusting their local control strategies in order to reduce their respective performance index. The produced power process of each agent thus depend on the performance index process, which may be determined, at each time instant, by all agents' bids, which themselves depend on the local actions of the respective agents. Given a set ^'of agents, define the multi-variable state process xi, for all ^{? fc} ^ ,

^'ΠΪ . T8 ,,,^'TS , .T8 B TB H TB ΤΪ3. with components «AI>« ^» ¾^ρίί- y.\^jit- J/EL*^'* - m\sU-> ift u ^ ifmi y-mit > # 5 a- i*w$U

Define the multivariable control process ^{ί >}· , for all ^{2 fe} , with components and - YS'ts Assume that the weather variables can be modeled as Ito drift-diffusion processes. This assumption is common in various fields dealing with weather factors. A drift-diffusion process may be used to model temperature dynamics with seasonal stochastic volatility. The model may be then used, e.g., to compute futures prices on indices like cooling and heating degree days and cumulative average temperatures, as well as option prices on them. The synoptic variability of mid-latitude sea surface winds (obtained from 6 hourly scatter ometter observations) can be well described by a univariate Ito drift-diffusion stochastic differential equation.

The dynamics of the turbines can be stated as Ito drift diffusion processes: d& = (™_{H i} i + σ™^ν _Η , V» E l^Tn , ⁽€ [0, *] (Sa)

¾l¾_f = ÷ ^ -¾ - Vi ε X^TS (8b >

<¾i_t = ÷ Vm_Htdw™ , vi E 2™ (8c)

€i = ÷ ¾ -cto™* , V, E 2 ^{! K} (8d»

%W « = t* >it<& ÷ ^Vmiitdwrnm * ^V* ^e (^8I>

A¾k = ÷ ¾<*u¾_t, Vi E ™ <8g)

= μ%*&, i J™ (8h) where

are standard

Wiener processes [11]. The first and second terms on the right-hand side of are the drift and diffusion components, respectively. Note that ^{f "ΑΡΐί} is a function of the real power, · ^{Ρϊ ί} , the performance index, &inAt, and the weather variables ^^Α£ϊΐί, ^** ' and

ΪΒ , .TiS .. ?£ ,^'5'B i i:

Define the cost function for an agent i, ' , as follows:

With continue reference to the figures and now Fig. 3, a method 1000 for maximizing wind turbine output performance is provided. Assuming that a plurality of wind turbines 10 are similarly situated within a site 1 and exposed to similar environmental conditions, the method 1000 may include monitoring the plurality of wind turbines (1010). In this step, the output performance information for each wind turbine may be observed, e.g., via the controller 200. Additionally or alternatively, the conditions sensed by the sensors 20 distributed throughout and proximate to the site 1 may also be observed and/or transmitted to the controller.

In an exemplary embodiment, during the operation of a plurality of wind turbine 10 within the site 1 , the controller 200, under the control of the control application 300, may receive the performance information from any one or more of the wind turbines or each of the plurality of wind turbines 10. Current or Active operational parameters for each wind turbine 10 may also be provided with the performance information or any time prior to establishing a baseline operation. The performance and/or the operation parameters may also include, for example, output parameters indicative of the wind turbine 10 output products generated during a period of time, e.g., a predetermined period, given the current environmental conditions at the site 1.

Once the controller 200 observes and/or receives the performance information and/or sensed conditions, a baseline operation may be established, e.g., via the control application 300 or other operation generator (1020).

The established baseline operation may be based on the received output information and operational parameters of one or more of the monitored wind turbines, given the sensed conditions. The controller 200, e.g., under the control of the control application 300, may identify which wind turbine(s) is operating at a highest efficiency rate based on the received information and operational parameters. It should be appreciated that the baseline operation may be similar or identical to the identified operational parameters of any wind turbine 10 operating at the highest efficiency rate, i.e., the most efficient, or the wind turbine 10 operating at a maximum performance, e.g., a highest output of energy, e.g., mechanical energy. Once the baseline operation is established, the method 1000 may include adjusting the operational parameters of each wind turbine 10 not operating at the established baseline operational parameter to about the established baseline operation parameter to maximize performance of the adjusted wind turbine(s) 10 (1030).

In one exemplary embodiment to adjust the operational parameters of the wind turbine 10, the current or active operational parameter for each wind turbine 10 may be compared to the established baseline operation parameter to determine what differences in parameters exist, if any, between the current operational parameter and the established baseline operation parameter. If it is determined that differences in parameters exist, the operational parameters may be adjusted to the baseline operation parameter or at least close to the baseline operation parameter.

Additionally or alternatively, the current operational parameters may be adjusted to emulate, i.e., equal or surpass, the power output or efficiency of the wind turbine(s) 10 whose parameters were used to established by the baseline parameters. This adjustment may be based on the environmental conditions being sensed and/or anticipated in or around the site 1, e.g., for a given period, to account for output reductions or other changes effecting output which may result from the conditions in and around the site 1.

It should be appreciated that the monitoring of the wind turbines 10 in the method 1000 may be continuous. If any changes in output or efficiency are identified during the continuous monitoring, the method 1000 may include establishing an updated baseline operation for updating any, e.g., underperforming turbines 10, not operating at the updated baseline operation. Similar to the above steps, e.g., the controller 200 receives the output performance information and operational parameters for each wind turbine to determine which wind turbines are operating at the highest efficiency and/or performance rate. The updated operational parameters may then be established, e.g., generated via the control application 300, and based on the received information. Thereafter, the current/active operational parameters may be adjusted to the updated baseline operation parameter or to about the updated baseline operation parameter to maximize output and efficiency.

In yet a further exemplary embodiment, additionally or alternatively, the method 1000 may include establishing a maximum operating efficiency (MOE) rate (production rate) for a wind turbine 10. The MOE is representative of an efficiency value which may not be exceeded based on the mechanical and operational configuration of the wind turbine(s) 10 in a given environment. In this embodiment, similar to the established baseline and updated baseline parameters, the MOE rate may also be utilized for adjusting the wind turbine operation in order to maximize efficiency and output performance.

With continued reference to the figures, and now Fig. 4, a second method 2000 for maximizing wind turbine performance and efficiency is provided. Similar to the method 1000, the method 2000 includes continuously monitoring a plurality of wind turbines 10 (2010), and identifying an equivalence class within the plurality of wind turbines 10 (2020).

In this embodiment, the plurality of wind turbines may be grouped in a manner similar to the wind turbines 10 in method 1000, or additionally or alternatively, in a manner to establish a class of wind turbines 10, e.g., equivalence classes 110, 120, and 130. It should be appreciated that the equivalence class may be based on several factors, which may be based on the configuration or construction of the wind turbines 10, the conditions surrounding the wind turbines 10, and/or be based on consumer needs or output goals for a given class. It should be appreciated that the configuration and/or construction may be identical across the turbines 10 making up the equivalence class, or they may be similar enough such that each wind turbine may achieve the same maximum output performance goals.

Upon identifying the equivalence class, the method 2000 includes determining which wind turbines 10 within the class are operating to deliver a maximum power output based on the wind turbine's 10 mechanical energy and given the environmental conditions surround the wind turbines 10 within the class (2030). It should be appreciated that any wind turbines 10 operating at a highest performance rate based on mechanical energy and given the monitored environmental conditions are identified along with operational information for each wind turbine operating at the highest performance rate. Upon identifying the highest performing wind turbines 10, the method 2000 includes generating a baseline operation (2040). Similar to the baseline operation in method 1000, the generated baseline operation of method 2000 may be based on the operating parameters of those wind turbines 10 operating at maximum power output and/or efficiency, in addition to conditions sensed or anticipated. After generating the baseline operation, any wind turbines 10 not operating at the generated baseline operation may have its operation parameters adjusted to the generated baseline operation or to about the generated baseline operation (2050).

With reference now to Fig. 5, a further exemplary embodiment of a method 3000 for maximizing wind turbine performance is provided. In this embodiment, a power index is established or identified (3010). It should be appreciated that the power index may be representative of a maximum output mechanical energy for a wind turbine. The method 3000 may further include monitoring a plurality of wind turbines, and receiving operational information identifying operational parameters and output performance for each wind turbine (3020). Step 3020 may be similar to step 1010 and 2010 in that the monitoring of the wind turbines may be continuous throughout the wind turbines 10 operation. The method 3000 may further include receiving operational information identifying the operational parameters, e.g., current and/or active parameters, and/or output performance information for each wind turbine 10. Upon receiving the operational and performance information, any wind turbines 10 not operating to produce a maximum output mechanical energy may be identified (3030). To identify which wind turbines are not producing at maximum rate, the current production rate of each wind turbine may be compared to the power index to determine which wind turbines 10 are underperforming. Upon identifying the underperforming wind turbines, a cost indicative of a difference between a respective wind turbine's actual output performance and the power index may be generated (3040). In this step, the cost may be generated, e.g., via the control application 300, and subsequently assigned to its respective wind turbine 10, which has been identified as underperforming. Upon assigning the cost to each wind turbine 10, the operational parameters of each wind turbine 10 incurring a cost may be adjusted to reduce the difference between the respective wind turbine's actual output performance and the power index to reduce or eliminate the incurred cost. It should be appreciated that in this embodiment, the existence of any cost in any of the wind turbines may be representative of a wind turbine 10 not performing at a maximum output and efficiency rate. Where a cost exists, adjustment of the operational parameters may continue until the cost is eliminated, which may be indicative of maximum performance.

While specific embodiments have been described in detail, those with ordinary skill in the art will appreciate that various modifications and alternative to those details could be developed in light of the overall teachings of the disclosure. For example, elements described in association with different embodiments may be combined. Accordingly, the particular arrangements disclosed are meant to be illustrative only and should not be construed as limiting the scope of the claims or disclosure, which are to be given the full breadth of the appended claims, and any and all equivalents thereof. It should be noted that the terms "comprising", "including", and "having", are open-ended and does not exclude other elements or steps; and the use of articles "a" or "an" does not exclude a plurality.

Claims

CLAIMS We Claim:

1. A method comprising:

a) monitoring a plurality of wind turbines 10 in a wind park and exposed to similar environmental conditions;

b) receiving output performance information from each of the wind turbines and establishing a baseline operation parameter based on the received output information and operational parameters of one or more of the monitored plurality of wind turbines; and

c) adjusting the operational parameters of each wind turbine not operating at the established baseline operational parameter to about the established baseline operation parameter to maximize performance of the adjusted wind turbines.

2. The method of claim 1, wherein the establishing step comprises:

identifying one or more wind turbines operating at a highest efficiency rate based on the received output performance information and operational parameters, and identifying the operational parameter corresponding to the highest efficiency rate wind turbines; and

generating the baseline operation parameter based on the operational parameters corresponding to the highest outputting wind turbine(s).

3. The method of claim 1, wherein the establishing step comprises:

identifying one or more wind turbines demonstrating a highest output based on the output performance information and identifying the operational parameters corresponding to the highest outputting wind turbine(s); and

The method of claim 1, wherein the adjusting step comprises:

identifying one or more wind turbines not operating at a highest efficiency rate based on the received output performance information and operational parameters, and identifying the operational parameters corresponding to the wind turbines not operating at the highest efficiency rate;

comparing the established baseline operation with the corresponding operational parameters of the wind turbines not operating at the highest efficiency rate and generating updated operational parameters indicative of the difference in parameters between the corresponding parameters of each of the wind turbines not operating at the highest efficiency rate and the established baseline operation parameter; and

adjusting the operational parameters of each wind turbine not operating at the highest efficiency rate based on the updated operational parameters corresponding to each wind turbine to about the established baseline parameter.

The method of claim 1, wherein the adjusting step comprises:

identifying one or more wind turbines not demonstrating a highest output based on the output performance information and identifying the operational parameters corresponding to the wind turbine(s) not demonstrating a highest output; comparing the established baseline operation with the corresponding operational parameters of the wind turbine(s) not demonstrating a highest output and generating updated operational parameters indicative of the difference in parameters between the corresponding parameters of the wind turbine(s) not demonstrating a highest output and the established baseline operation parameter; and adjusting the operational parameters of each the wind turbine(s) not demonstrating a highest output based on the updated operational parameters corresponding to each wind turbine to about the established baseline parameter.

The method of claim 1, wherein each of the plurality of wind turbines includes a similar configuration and is similarly situated in the environment.

The method of claims 4 or 5, wherein the adjusting step comprises:

continuously adjusting the operational parameters operation of each wind turbine not operating at the highest efficiency rate to emulate a maximum power output of the each wind turbine operating at the established baseline parameter.

The method of claim 7 further comprising:

upon one or more of the wind turbines emulating the maximum power output, establishing an updated baseline operation parameter based on the operational parameters of the one or more wind turbines emulating the maximum power output; and

adjusting the operational parameters of any wind turbines not operating at the updated baseline operation parameter to about the updated baseline operation parameter.

The method of claim 1 further comprising:

d) monitoring the environmental conditions surrounding the plurality of wind turbines for changes in the environmental conditions; and

e) upon determining a change in the environmental condition, receiving updated output performance information from each of the wind turbines and establishing an updated baseline operation parameter based on the updated output information and operational parameters of one or more of the monitored plurality of wind turbines; and f) adjusting the operational parameters of each wind turbine not operating at the established updated baseline operational parameter to about the established updated baseline operation parameter to maximize performance of the adjusted wind turbines.

10. A method comprising:

establishing an equivalence class of wind turbines;

monitoring the wind turbines in the equivalence class and gathering output performance and operational information for each wind turbine;

identifying one or more wind turbines operating at a highest performance rate based on mechanical energy given the monitored environmental conditions and identifying the operational information for the wind turbines operating at the highest performance rate;

generating a baseline operation parameter based on the operation parameters of the wind turbines operating at the highest performance rate; and

adjusting the operation parameters of each wind turbine not operating at the highest performance rate to about the baseline operation parameter.

11. The method of claim 10, wherein each of the wind turbines in the equivalence class are similar configured and exposed to similar environmental conditions.

12. The method of claim 11 further comprising:

monitoring the environmental conditions surrounding the wind turbines in the equivalence class, and upon identifying a change in the environmental conditions, identifying one or more wind turbines operating at the highest performance rate and gathering the operation parameters of the wind turbines operating at the highest performance rate; generating an updated baseline operation parameter based on operational parameters operating at the highest performance rate given the change in the environmental conditions; and

updating the operational parameters of any wind turbines not operating at the updated baseline operation parameter to about the updated baseline operation parameter.

The method of claim 10,

wherein the step of generating a baseline parameter comprises:

defining a performance index indicative of measured speed and generated mechanical energy for each wind turbine over a predetermined period of time as:

14. The method of claim 13 further comprising: establishing a permutation matrix ¾ for all t T such that:

applying the permutation matrix to a wind speed vector observed at time t, and recasting the performance power index as:

15. A method comprising: establishing a power index representative of a maximum output mechanical energy for a wind turbine;

monitoring a plurality of wind turbines and receiving operational information identifying operational parameters and output performance for each wind turbine; identifying one or more wind turbines not producing the maximum output mechanical energy and generating a cost indicative of a difference between a respective wind turbine's actual output performance and the power index and assigning the cost to each wind turbines not producing at the maximum output mechanical energy; and

adjusting the operational parameters of each wind turbine incurring the cost to reduce the difference between the respective wind turbine's actual output performance and the power index to reduce or eliminate the incurred cost.

16. The method of claim 15, wherein the plurality of wind turbines are comprised in an equivalence class and exposed to similar environmental conditions.

17. The method of claim 16 further comprising:

identifying a change in the environmental conditions;

monitoring the output performance of each wind turbine and establishing an updated power index representative of the actual output performance of one or more wind turbines exhibiting a highest output of mechanical energy given the change in environmental condition;

generating a revised cost indicative of a difference between a respective wind turbine's actual output performance and the updated power index; and assigning the revised cost to each respective wind turbine outputting less mechanical energy than the mechanical energy represented by the updated power index; and adjusting the operational parameters of each wind turbine incurring the revised cost to reduce the difference between the respective wind turbine's actual output performance given the change in environmental conditions and the updated power index to reduce or eliminate the incurred revised cost.

A method in a control unit of a wind turbine comprising:

identifying a power index representative of a maximum output mechanical energy of the wind turbine;

identifying the wind turbine's operational parameters and actual output mechanical energy over a predetermined period of time;

comparing the actual output mechanical energy with the power index and generating an incurred cost for the wind turbine that indicative of a difference between the maximum output mechanical energy and the actual output mechanical energy; and

adjusting the operation parameters of the wind turbine to reduce the incurred cost. The method of claim 18, wherein the power index is representative of output mechanical energy from a second wind turbine similarly configured to the first wind turbine and situated in a equivalence class with the first wind turbine.

A system comprising:

a plurality of wind turbines arranged in an equivalence class, each wind turbine being exposed to similar environmental conditions within the equivalence class and includes a competitive agent configured to determine wind speed and produced power;

a controller operably connected to each wind turbines and one or more sensors selectively positioned within the equivalence class and configured to sense environmental conditions surrounding the wind turbines; wherein each competitive agent submits a bid comprising determined wind speed and produced power for each respective wind turbine to the controller, and wherein the controller defines a performance index based in part on the bid as:

, and wherein the controller transmits the value of performance index to each competitive agent; and wherein each competitive agent adjusts operational parameters for its respective wind turbine to reduce the value of the performance index.