Classifications

• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
  • F03D—WIND MOTORS
  • F03D7/00—Controlling wind motors 
  • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
  • F03D7/04—Automatic control; Regulation
  • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
  • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
  • F03D7/046—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
  • F05B2260/00—Function
  • F05B2260/82—Forecasts
  • F05B2260/821—Parameter estimation or prediction
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
  • F05B2270/00—Control
  • F05B2270/70—Type of control algorithm
  • F05B2270/709—Type of control algorithm with neural networks
• • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
  • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
  • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
  • F05B2270/00—Control
  • F05B2270/80—Devices generating input signals, e.g. transducers, sensors, cameras or strain gauges
  • F05B2270/804—Optical devices
  • F05B2270/8042—Lidar systems
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
  • Y02E10/00—Energy generation through renewable energy sources
  • Y02E10/70—Wind energy
  • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

• the present invention relates to a method and system for controlling a wind energy installation and a computer program product for carrying out the method.
• Wind turbines with rotors and generators coupled to them can be adapted to changing environmental conditions, in particular varying wind speeds, by controlling the generator and various actuators which, for example, rotate rotor blades about their longitudinal axes or nacelles supporting the rotor about a yaw axis.
• the object of the present invention is to improve the operation, in particular the performance, of wind energy plants.
• a wind power plant comprises a rotor that can be rotated (mounted) about a rotor axis and has one or more rotor blades, in one embodiment at least two and / or at most five, and one rotor coupled to the rotor, in one embodiment via a transmission Generator.
• the rotor is (rotatably) mounted, in particular, in a nacelle, which in turn is mounted in a further development, in particular rotatably, on, in particular on, a tower.
• the rotor axis encloses an angle with the vertical or gravitational direction, which is at least 60 ° and / or at most 120 °, in one
• the rotor or nacelle are rotatable about a yaw axis, in particular mounted on the tower, the yaw axis in one embodiment including an angle with the rotor axis which is at least 60 ° and / or at most 120 ° in one
• Continuing education is, at least essentially, vertical.
• the present invention can be used particularly advantageously for such wind energy plants on account of their ambient conditions and operating states.
• a method for controlling the wind energy installation has the step: detecting a value of a one-dimensional or multidimensional apron parameter, in particular a one-dimensional or multidimensional apron wind parameter, which is in a first area at a first point in time is present or prevails, which, in particular in the direction of the rotor axis, has a first, in particular minimum or mean, distance greater than zero from the wind energy installation, in particular the rotor blade or blades, in particular (in the direction of the rotor axis) upstream or in front of or the rotor blades are arranged, with the aid of one or more sensors, in one embodiment capturing a value sequence of the apron parameter up to the first point in time using the sensor or sensors.
• the method has the step: controlling the generator and / or one or more actuators of the wind energy installation on the basis of this recorded apron parameter value, in particular this acquired apron parameter value sequence, and a machine-learned assignment.
• this machine-learned assignment to the or values of the apron parameter (s) or apron parameter (values) sequences assigns a one-dimensional or multidimensional forecast, in particular for a later second point in time Near-field parameters (value), in particular one-dimensional or multidimensional near-field wind parameters (value), on the wind turbine.
• apron parameter or apron parameter sequences which is in the first region (to..., Which is spaced by the first distance from the wind energy installation, in particular in front of the rotor blade or blades ) is / are present at the first point in time or is / are detected by means of the sensor (s), and the near-field parameter which is likely to be set or results at the wind energy installation at a later or later point in time is learned by machine.
• the near-field parameter in one embodiment in particular due to the difficulty in mathematically or theoretically modeling this relationship, Particularly advantageously, in particular quickly (reliably) and / or precisely (r) predicted, and thus the actuator (s) and / or the generator can advantageously be controlled with foresight, which is particularly due to the inherent or occurring effects of this control.
• mechanical, hydraulic, electrical and / or signal and / or computational inertia in particular dead times or the like, can be particularly advantageous.
• the machine-learned assignment to the or values of the apron parameter (s) or apron parameter (values) sequences assigns a one-dimensional or multidimensional predicted for a later second point in time Operating parameters (value) of the wind turbine.
• apron parameter or apron parameter sequences which is / are present in the first region (up to) by the first distance from the wind energy installation, or is / are acquired by means of the sensor (s) and the operating parameter, which is likely to be set or obtained in the wind energy installation at the later second point in time, learned by machine.
• the operating parameter in an embodiment can be predicted particularly advantageously, in particular quickly (er), reliably (er) and / or precisely (r), and thus the actuator (s) and / or the generator can advantageously be controlled in a forward-looking manner, which can be particularly advantageous due to the inertia, in particular dead times or the like, inherent or occurring in this control, in particular mechanical, hydraulic, electrical and / or signal and / or computational technology.
• the method comprises the steps:
• the near-field or operating parameter is first predicted on the basis of the machine-learned assignment for the second point in time and then, in particular with the aid of a, if appropriate conventional, controller, the actuators or the generator (predictive) controlled.
• conventional controllers that operate on the basis of the near-field or operating parameter can be used and / or the safety during operation of the wind energy installation can be increased.
• control can also be integrated into the machine-learned assignment or learned by machine (with). In one embodiment, this can (further) improve the control of the actuator (s) or the generator.
• a rule (u) n (g) or tax (u) n (g) is generally referred to as tax (u) n (g), taking into account the feedback actual values.
• the machine-learned assignment to the or values of the apron parameter (s) or apron parameter (values) sequences assigns a one- or multi-dimensional control variable of the actuator or actuators or for the actuator (s) and / or the or for the generator (s).
• apron parameter or apron parameter sequences which is / are present in the first region (up to) by the first distance from the wind energy installation, or is / are acquired by means of the sensor (s) and the control variable on the basis of which the actuators and / or the generator are controlled.
• control variable in an embodiment can be predicted particularly advantageously, in particular quickly (er), reliably (er) and / or precisely (r) and thus the actuator (s) and / or the generator can advantageously be controlled in a forward-looking manner, which can be particularly advantageous due to the inertia, in particular dead times or the like, inherent or occurring in this control, in particular mechanical, hydraulic, electrical and / or signal and / or computational technology.
• one or more of the sensors measure / measure in a line-like manner or along a so-called “line-of-sight” and / or without contact, in particular optically, acoustically and / or electromagnetically.
• the one or more of the sensors are each a LIDAR sensor, SODAR sensor, RADAR sensor or the like.
• the run-up parameters (value) or the run-up parameters (value) sequence can be recorded particularly advantageously, in particular quickly (er), reliably (er) and / or precisely (r), in one embodiment.
• the present invention can be used with particular advantage in the case of such sensors or measurements, in particular on account of the restriction to a wind speed component along the line-of-sight.
• the one or more of the sensors are arranged on the wind energy installation, in particular the rotor, the nacelle or the tower.
• a detected apron can advantageously be moved or rotated with the rotor, by an arrangement on the rotor side in one embodiment advantageously a field of view disturbance by rotor blades can be avoided, by an arrangement on the tower side in an embodiment of the sensor or sensors be connected advantageously.
• the apron wind parameter depends on a wind speed, in particular wind direction and / or strength, at one or more points in the first region, in particular it can indicate this or this.
• the near-field wind parameter depends on a wind speed, in particular wind direction and / or strength, at one or more points on the wind energy installation, in particular the rotor, in an embodiment of one or more rotor blades, it can correspond in particular or this specify.
• the wind power installation can be controlled in a particularly advantageous manner.
• the operating parameter depends on a speed, acceleration and / or load of the rotor, in particular one or more rotor blades, and / or the nacelle and / or an output, in particular a speed and / or a torque, of the generator.
• the load of the nacelle can include, in particular, a thrust force acting on it and / or a pitching and / or yaw moment acting on it, in particular, the load of the rotor particularly a torque and / or forces and / or moments acting on it in or the rotor blades or the resulting deformations.
• the or one or more of the actuators adjusts the or one or more of the rotor blades about its or their longitudinal or blade axis or is / are set up for this purpose or is / are used for this purpose.
• the actuator (s) adjust the so-called pitch angle in one embodiment, collectively in one embodiment, (individual) blade-specifically in another embodiment, or are set up or used for this purpose.
• the or one or more of the actuators adjusts the rotor, in particular the nacelle, about one or the yaw axis or is / are set up for this purpose or is / are used for this.
• the actuator (s) adjust the so-called azimuth in one embodiment.
• a collective or (single) sheet (specific) pitch angle and an azimuth adjustment have been found to be particularly advantageous for using the present invention in addition to a generator control.
• the assignment is or is learned mechanically with the aid of the wind energy installation, the actuator (s) and / or generator of which is then controlled on the basis of this assignment.
• the assignment can advantageously be optimized specifically for the wind energy installation or for the conditions prevailing in the controlled wind energy installation.
• the assignment is or is learned mechanically in one embodiment with the aid of at least one further wind energy installation.
• the wind energy installation can already be controlled directly according to the invention and / or (further) machine learning can be improved with the aid of this wind energy installation.
• the assignment in an embodiment is or is learned by machine using at least one, in particular mathematical, simulation model, in particular the one wind energy installation and / or its surroundings.
• the wind energy installation can be controlled directly according to the invention and / or (further) machine learning can be (further) improved with the aid of this wind energy installation.
• the assignment is also learned mechanically while the wind turbine is being controlled. Accordingly, the control of the actuator (s) and / or the generator is self-learning in one embodiment (machine). This allows the control of the actuator (s) and / or the generator to be self-learning in one embodiment (machine). This allows the actuator (s) and / or the generator to be self-learning in one embodiment (machine). This allows the actuator (s) and / or the generator to be self-learning in one embodiment (machine). This allows the
• the assignment in an execution improved, in particular adapted to changing conditions.
• the assignment is or is implemented using an artificial neural network, in a further development using a recurrent or feedback artificial neural network and / or LSTM network (“long short-term memory”) that is particularly suitable for this purpose.
• a recurrent or feedback artificial neural network and / or LSTM network long short-term memory
• the assignment is or is learned by machine on the basis of a comparison of recorded and predicted values of the near field and / or operating parameter.
• values of the near field and / or operating parameter are forecast for at least a second point in time, at this second point in time the corresponding near field or operating parameter is recorded, in particular measured, and these values are compared with one another, the assignment being learned by machine, in particular that is, the artificial neural network is trained in such a way that a quality criterion which is dependent on this difference between these detected and predicted values is optimized.
• the time interval between the first and second point in time can be estimated on the basis of an, in particular average, wind speed at the first point in time, which can be determined from the value of the apron wind parameter. Likewise, the time interval can also be learned mechanically.
• the assignment can assign individual values of the apron parameter X respectively values of the near-field or operating parameter or the control variable Y, in particular according to X t.) - assignment 9—> gt 2 ) with the first time ti and the second time t 2 .
• the assignment can also map a time window (up to the first point in time) to near-field or operating parameters or control variables. This allows the dynamics, in particular, in one embodiment Aerodynamics, between the first and second point in time, are particularly advantageously taken into account.
• the first distance is at least 10%, in particular at least 50%, in one embodiment at least 90%, and / or at most 1000%, in particular at most 800%, in one embodiment at most 600%, a length of the rotor blade in the case of a multi-bladed blade Rotor with a (maximum) diameter D, in particular at least 0.05-D, in particular at least 0.25-D, in one embodiment at least 0.45-D, and / or at most 5-D, in particular at most 4-D, in an embodiment at most 3 D.
• the or one or more of the actuators and / or the generator become continuous or quasi-continuous on the basis of the (respectively or currently) recorded apron parameter value, in particular the (respectively or currently) recorded apron parameters -Value sequence, and the machine-learned assignment (to) controlled.
• This has proven to be particularly advantageous, in particular for pitch angle adjustment and control of the generator (torque), without being limited to this.
• the one or more of the actuators and / or the generator are only exceeded when a predetermined limit value is exceeded on the basis of the (respectively or currently) recorded apron parameter value, in particular the (respectively or currently) recorded apron parameter.
• Parameter value sequence, and the machine-learned assignment (to) controlled This has proven particularly advantageous for the azimuth adjustment, without being limited to this.
• Wind energy installation in particular hardware and / or software, in particular program technology, set up to carry out a method described here and / or has:
• Apron wind parameter which is present at a first point in time in a first area and is at a first distance from the wind energy installation, in particular the rotor blade, in particular a value sequence of the apron parameter up to the first point in time, or is provided for this purpose, in particular are set up and / or used;
• system or its means have:
• system or its means has one version:
• system or its means has one version:
• a means in the sense of the present invention can be designed in terms of hardware and / or software, in particular a data, or signal-linked, preferably digital, processing, in particular microprocessor unit (CPU), graphics card (GPU), preferably data or signal connected to a memory and / or bus system ) or the like, and / or have one or more programs or program modules.
• CPU microprocessor unit
• GPU graphics card
• the processing unit can be designed to process commands that are implemented as a program stored in a memory system, to acquire input signals from a data bus and / or to output signals to a data bus.
• a storage system can have one or more, in particular different, storage media, in particular optical, magnetic, solid-state and / or other non-volatile media.
• the program can be designed in such a way that it embodies or is capable of executing the methods described here, so that the processing unit can execute the steps of such methods and thus in particular can control the wind power installation.
• a computer program product can have, in particular a non-volatile, storage medium for storing a program or with a program stored thereon, an execution of this program prompting a system or a controller, in particular a computer to carry out the method described here or one or more of its steps.
• one or more, in particular all, steps of the method are carried out completely or partially automatically, in particular by the system or its means.
• the system has the wind turbine. Further advantages and features result from the subclaims and the exemplary embodiments. Here shows, partly schematically:
• FIG. 1 a system for controlling a wind turbine according to an embodiment of the present invention
• FIG. 2 a method for controlling the wind power installation according to an embodiment of the present invention.
• FIG. 1 shows a system for controlling a wind turbine according to an embodiment of the present invention.
• the wind power plant has a rotor 10 with a plurality (in the exemplary embodiment three) rotor blades 11, which is mounted in a nacelle 30 which is rotatable about a substantially horizontal rotor axis R and which is mounted rotatably about a substantially vertical yaw axis G on a tower 31 of the wind power plant .
• the generator 20 has a transmission for this purpose or is coupled to the rotor 10 via a transmission.
• Actuators 12 adjust the pitch angles of the rotor blades 11 about their longitudinal or blade axes B.
• An actuator 32 adjusts the yaw angle or azimuth of the nacelle 30 against the tower 31.
• a lidar, sodar, radar or similar sensor 40 is arranged on the nacelle 30 and detects a multidimensional apron parameter in the form of wind speeds in a first area A (FIG. 2: step S10), which is at a first distance a the rotor 10 is arranged.
• a controller 43 has an artificial neural network 41 and a controller 42.
• the neural network 41 receives raw data from the sensor 40 and forms it in a step S20 (see FIG. 2) on the basis of a machine-learned assignment to wind speeds on the rotor and / or operating parameter values, for example an aerodynamically induced rotor speed, an aerodynamically induced generator torque or the like, from that are predicted for a second point in time that is after a first point in time of the acquisition of the raw data.
• the time offset between recorded and predicted values can be estimated on the basis of a (average) wind speed averaged from the recorded wind speeds or can also be learned mechanically from the neural network 41.
• the wind speeds on the rotor and / or operating parameter values predicted by the neural network 41 and / or operating parameter values compared with wind speeds recorded on the rotor or operating parameter values recorded in the wind energy installation are compared, the neural network 41 using machine learning Seeks to minimize the difference between forecast and recorded data.
• the neural network 41 outputs the predicted wind speeds on the rotor or operating parameter values to a controller 42, which determines control values for the generator 20, the pitch angle actuators 12 and the azimuth actuator 32 on the basis of these variables and outputs them to them.
• the neural network 41 can assign wind speeds detected by the sensor 40 at a first point in time in the first region A and wind speeds on the rotor predicted therefrom for a later second point in time or further improve operating parameter values through (further) machine learning.
• the neural network 41 can also be directly based on the Sensor 40 at a first point in time detected wind speeds in the first area A and a machine-learned assignment of these apron parameter values to control variables for the generator 20 and the pitch angle actuators 12 each determine these control variables and the generator 20, the pitch angle actuators 12 and the azimuth actuator 32 control it.
• the exemplary embodiments are only examples that are not intended to restrict the scope of protection, the applications and the structure in any way.

Applications Claiming Priority (2)

Publications (1)

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ID=68234005

Family Applications (1)

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Family Cites Families (17)

• 2018
  • 2018-10-25 DE DE102018008391.9A patent/DE102018008391A1/de active Pending
• 2019
  • 2019-10-10 CN CN201980070238.2A patent/CN112888853A/zh active Pending
  • 2019-10-10 US US17/288,050 patent/US20210340957A1/en not_active Abandoned
  • 2019-10-10 WO PCT/EP2019/077508 patent/WO2020083656A1/de unknown
  • 2019-10-10 EP EP19786779.9A patent/EP3870849A1/de active Pending

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