CN116877333A - Yaw attitude control system and method for wind turbine generator - Google Patents
Yaw attitude control system and method for wind turbine generator Download PDFInfo
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- CN116877333A CN116877333A CN202310796995.6A CN202310796995A CN116877333A CN 116877333 A CN116877333 A CN 116877333A CN 202310796995 A CN202310796995 A CN 202310796995A CN 116877333 A CN116877333 A CN 116877333A
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- 238000012549 training Methods 0.000 claims abstract description 39
- 238000012360 testing method Methods 0.000 claims abstract description 17
- 238000010606 normalization Methods 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 6
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Classifications
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- 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
- F03D7/022—Adjusting aerodynamic properties of the blades
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- 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
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- 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
- F03D7/045—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with model-based controls
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- 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
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- 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
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Abstract
The invention relates to a yaw attitude control system and a yaw attitude control method for a wind turbine generator, belonging to the technical field of wind turbines, and comprising the following steps: the wind condition acquisition module: acquiring historical operation environment data of the wind turbine generator, and dividing the historical wind condition data of the wind turbine generator into a training set and a testing set after carrying out normalization processing on the historical wind condition data of the wind turbine generator through feature extraction; the wind condition prediction module: constructing a wind condition prediction model, carrying out iterative training on the wind condition prediction model by using a training set, and evaluating the wind condition prediction model by using a test set until iteration is finished to obtain a trained wind condition prediction model, and predicting based on real-time operation environment data of a wind turbine generator by using the wind condition prediction model to obtain wind condition prediction data; yaw control module: based on the current wind condition data and the input wind condition prediction data and combining the maximum value of the wind condition data, executing yaw system control logic of the wind turbine, and adjusting the yaw system posture of the wind turbine until the yaw is stopped after the wind is completed.
Description
Technical Field
The invention relates to the technical field of wind turbines, in particular to a yaw attitude control system and method of a wind turbine generator.
Background
The yaw system of the wind driven generator mainly ensures that the wind driven generator faces the wind direction by controlling the direction of the rotor, thereby capturing wind energy to the greatest extent. At present, because the wind state is changeable, the uncertainty of the wind direction is higher, the yaw system is often required to be started and stopped at any time according to real-time wind condition data, on one hand, the stable operation of the wind driven generator is easily affected, and on the other hand, the yaw system is also easily damaged due to the fact that the yaw system is frequently started and stopped for a long time, so that serious economic loss is caused. Based on the above, in order to improve the running stability of the wind turbine, a yaw attitude control system and a yaw attitude control method of the wind turbine are designed.
Disclosure of Invention
The invention aims to provide a yaw attitude control system and method for a wind turbine, which are used for constructing a wind condition prediction model by combining a BP model and a PSO algorithm, effectively predicting the wind condition of the next time period, comprehensively regulating and controlling a yaw system according to the average value of each time period by combining preset yaw system control logic, effectively overcoming the damage or failure easily caused by starting and stopping the yaw system frequently for a long time and improving the running stability of the wind turbine.
The embodiment of the invention is realized by the following technical scheme:
a wind turbine yaw attitude control system, comprising:
the wind condition acquisition module: acquiring historical operation environment data of the wind turbine generator, extracting features to obtain historical wind condition data of the wind turbine generator, and dividing the historical wind condition data of the wind turbine generator into a training set and a testing set after normalizing the historical wind condition data of the wind turbine generator;
the wind condition prediction module: constructing a wind condition prediction model, carrying out iterative training on the wind condition prediction model by using a training set, and evaluating the wind condition prediction model by using a test set until iteration is finished to obtain a trained wind condition prediction model, and predicting based on real-time operation environment data of a wind turbine generator by using the wind condition prediction model to obtain wind condition prediction data;
yaw control module: based on the current wind condition data and the input wind condition prediction data, and simultaneously combining the maximum value of the wind condition data, executing yaw system control logic of the wind turbine, and adjusting the yaw system posture of the wind turbine until the wind is completely yawed.
Optionally, a preprocessing sub-module is preset in the wind condition acquisition module, and the preprocessing sub-module is used for preprocessing historical operation environment data of the wind turbine generator so as to extract features.
Optionally, the normalizing process is performed on the historical wind condition data of the wind turbine generator, and a specific calculation formula of the normalizing process is as follows:
wherein Q is i For historical wind condition data of wind turbine generator, Q i ' normalized historical wind condition data, Q max Maximum value of historical wind condition data before normalization, Q min Is the minimum value of the historical wind condition data before normalization.
Optionally, a specific training process of the wind condition prediction model is as follows:
the wind condition prediction model is constructed by combining a BP neural network model and a PSO algorithm,
initializing PSO parameters of a wind condition prediction model, wherein the PSO parameters comprise the number of PSOs, the maximum iteration number, local learning factors and the overall learning factor, randomly acquiring the initial position and the initial speed of the PSOs in an initialization value range, constructing an fitness function in the training process of the wind condition prediction model, and iteratively updating the PSO parameters through the fitness function;
judging whether iteration is stopped or not, if not, continuing iteration until the maximum iteration times are reached; if so, judging whether the iteration number reaches the maximum iteration number, if not, returning to the previous step until the maximum iteration number is reached; if yes, completing forward propagation of the wind condition prediction model, and updating parameters of the wind condition prediction model through gradient backward propagation;
and after the back propagation is completed, outputting a wind condition prediction model which is completed with training, evaluating the wind condition prediction model which is completed with training based on a test set, and outputting the wind condition prediction model which is completed with training based on an evaluation result.
Optionally, an fitness function in the wind condition prediction model training process is constructed, and a specific calculation formula of the fitness function is as follows:
wherein E is ak For the connection weight of the input layer and the hidden layer, a and k are constants, N k As threshold of hidden layer, M a Is a feature matrix.
Optionally, the wind condition data is specifically wind direction and wind speed data.
Optionally, a first set value a, a second set value B and a third set value C of wind speed data are set in yaw system control logic of the wind turbine generator, where the first set value a is specifically a minimum value, the second set value B is specifically a standard value, and the third set value C is specifically a maximum value, and the specific steps include:
setting a first time period based on the current wind condition data, solving the average value of the current wind direction data in the first time period, and calculating the deviation angle between the average value of the current wind direction data and the engine room of the wind turbine generator;
judging whether the deviation angle absolute value is lower than the minimum yaw angle of the yaw system or not based on the solved deviation angle absolute value, and if so, not starting the yaw system; if the number is greater than or equal to the preset number, entering the next step;
predicting current wind condition data through a wind condition prediction model which is trained, and acquiring wind direction and wind speed data in a second time period;
judging whether the wind speed data is smaller than a first set value A, if so, stopping starting a yaw system of the wind turbine generator; if not, judging whether the wind speed data is between a first set value A and a second set value B, if so, starting a yaw system of the wind turbine, controlling the yaw system of the wind turbine to execute yaw at a preset first yaw speed, and stopping the yaw system of the wind turbine until the wind speed data is lower than the first set value A; if not, judging whether the wind speed data is between a second set value B and a third set value C, if not, judging that the wind speed data is larger than the third set value C, stopping starting a yaw system of the wind turbine, if so, starting the yaw system of the wind turbine, controlling the yaw system of the wind turbine to execute yaw at a preset second yaw speed, and stopping the yaw system of the wind turbine until the wind speed data is lower than the first set value A.
A yaw attitude control method of a wind turbine generator includes the steps:
acquiring historical operation environment data of the wind turbine generator, extracting features to obtain historical wind condition data of the wind turbine generator, and dividing the historical wind condition data of the wind turbine generator into a training set and a testing set after normalizing the historical wind condition data of the wind turbine generator;
constructing a wind condition prediction model, carrying out iterative training on the wind condition prediction model by using a training set, and evaluating the wind condition prediction model by using a test set until iteration is finished to obtain a trained wind condition prediction model, and predicting based on real-time operation environment data of a wind turbine generator by using the wind condition prediction model to obtain wind condition prediction data;
based on the current wind condition data and the input wind condition prediction data, and simultaneously combining the maximum value of the wind condition data, executing yaw system control logic of the wind turbine, and adjusting the yaw system posture of the wind turbine until the wind is completely yawed.
The technical scheme of the embodiment of the invention has at least the following advantages and beneficial effects:
according to the embodiment of the invention, the BP model and the PSO algorithm are combined to construct the wind condition prediction model, the wind condition of the next time period can be effectively predicted, the yaw system is comprehensively regulated and controlled according to the average value of each time period by combining the preset yaw system control logic, the damage or the fault easily caused by starting and stopping the yaw system frequently for a long time is effectively overcome, and the running stability of the wind turbine generator is improved.
Drawings
FIG. 1 is a schematic diagram of a yaw attitude control system of a wind turbine generator provided by an embodiment of the present invention;
fig. 2 is a schematic flow chart of a yaw attitude control method of a wind turbine generator according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
As shown in fig. 1, the present invention provides one of the embodiments: a wind turbine yaw attitude control system, comprising:
the wind condition acquisition module: acquiring historical operation environment data of the wind turbine generator, extracting features to obtain historical wind condition data of the wind turbine generator, and dividing the historical wind condition data of the wind turbine generator into a training set and a testing set after normalizing the historical wind condition data of the wind turbine generator;
the wind condition prediction module: constructing a wind condition prediction model, carrying out iterative training on the wind condition prediction model by using a training set, and evaluating the wind condition prediction model by using a test set until iteration is finished to obtain a trained wind condition prediction model, and predicting based on real-time operation environment data of a wind turbine generator by using the wind condition prediction model to obtain wind condition prediction data;
yaw control module: based on the current wind condition data and the input wind condition prediction data, and simultaneously combining the maximum value of the wind condition data, executing yaw system control logic of the wind turbine, and adjusting the yaw system posture of the wind turbine until the wind is completely yawed.
In this embodiment, a preprocessing sub-module is preset in the wind condition acquisition module, and the preprocessing sub-module is used for preprocessing historical operating environment data of the wind turbine generator so as to perform feature extraction.
It can be understood that the data preprocessing preset in the preprocessing sub-module comprises steps of data cleaning, abnormal data removing and the like, and is used for processing the historical data of the wind turbine so as to extract the historical wind condition data of the wind turbine.
Specifically, the normalization processing is performed on the historical wind condition data of the wind turbine generator, and a specific calculation formula of the normalization processing is as follows:
wherein Q is i For historical wind condition data of wind turbine generator, Q i ' normalized historical wind condition data, Q max Maximum value of historical wind condition data before normalization, Q min Is the minimum value of the historical wind condition data before normalization.
In this embodiment, the specific training process of the wind condition prediction model is as follows:
the wind condition prediction model is constructed by combining BP neural network model and PSO (particle swarm) algorithm,
initializing PSO parameters of a wind condition prediction model, wherein the PSO parameters comprise the number of PSOs, the maximum iteration number, local learning factors and the overall learning factor, randomly acquiring the initial position and the initial speed of the PSOs in an initialization value range, constructing an fitness function in the training process of the wind condition prediction model, and iteratively updating the PSO parameters through the fitness function;
judging whether iteration is stopped or not, if not, continuing iteration until the maximum iteration times are reached; if so, judging whether the iteration number reaches the maximum iteration number, if not, returning to the previous step until the maximum iteration number is reached; if yes, completing forward propagation of the wind condition prediction model, and updating parameters of the wind condition prediction model through gradient backward propagation;
and after the back propagation is completed, outputting a wind condition prediction model which is completed with training, evaluating the wind condition prediction model which is completed with training based on a test set, and outputting the wind condition prediction model which is completed with training based on an evaluation result.
Specifically, the fitness function in the wind condition prediction model training process is constructed, and the specific calculation formula of the fitness function is as follows:
wherein E is ak For the connection weight of the input layer and the hidden layer, a and k are constants, N k As threshold of hidden layer, M a Is a feature matrix.
More specifically, the wind condition data is wind direction and wind speed data.
In this embodiment, a first set value a, a second set value B, and a third set value C of wind speed data are set in yaw system control logic of the wind turbine generator, where the first set value a is specifically a minimum value, the second set value B is specifically a standard value, and the third set value C is specifically a maximum value, and the specific steps include:
setting a first time period based on the current wind condition data, solving the average value of the current wind direction data in the first time period, and calculating the deviation angle between the average value of the current wind direction data and the engine room of the wind turbine generator;
judging whether the deviation angle absolute value is lower than the minimum yaw angle of the yaw system or not based on the solved deviation angle absolute value, and if so, not starting the yaw system; if the number is greater than or equal to the preset number, entering the next step;
predicting current wind condition data through a wind condition prediction model which is trained, and acquiring wind direction and wind speed data in a second time period;
judging whether the wind speed data is smaller than a first set value A, if so, stopping starting a yaw system of the wind turbine generator; if not, judging whether the wind speed data is between a first set value A and a second set value B, if so, starting a yaw system of the wind turbine, controlling the yaw system of the wind turbine to execute yaw at a preset first yaw speed, and stopping the yaw system of the wind turbine until the wind speed data is lower than the first set value A; if not, judging whether the wind speed data is between a second set value B and a third set value C, if not, judging that the wind speed data is larger than the third set value C, stopping starting a yaw system of the wind turbine, if so, starting the yaw system of the wind turbine, controlling the yaw system of the wind turbine to execute yaw at a preset second yaw speed, and stopping the yaw system of the wind turbine until the wind speed data is lower than the first set value A.
In the above application, the first time period set in this embodiment may be understood as a yaw time maximum value, and the following includes a second time period, a third time period, …, and an nth time period, which are all consistent with the lengths of the first time period, where the first set value a, the second set value B, and the third set value C of the wind speed may be understood that the first set value a corresponds to a yaw system start wind speed, the second set value B corresponds to a yaw system standard wind speed, and the third set value C corresponds to a wind speed threshold of the yaw system, where the logic is set based on this, and specifically, the first set value a, the second set value B, and the third set value C are exemplified as: the first set point a is in particular 2.5 meters per second, the second set point B is in particular 6 meters per second, the third set point C is in particular 20 meters per second, and the wind direction reaches at least a minimum yaw angle of 3 degrees of the yaw system.
As shown in fig. 2, the present invention also provides another embodiment: a yaw attitude control method of a wind turbine generator includes the steps:
acquiring historical operation environment data of the wind turbine generator, extracting features to obtain historical wind condition data of the wind turbine generator, and dividing the historical wind condition data of the wind turbine generator into a training set and a testing set after normalizing the historical wind condition data of the wind turbine generator;
constructing a wind condition prediction model, carrying out iterative training on the wind condition prediction model by using a training set, and evaluating the wind condition prediction model by using a test set until iteration is finished to obtain a trained wind condition prediction model, and predicting based on real-time operation environment data of a wind turbine generator by using the wind condition prediction model to obtain wind condition prediction data;
based on the current wind condition data and the input wind condition prediction data, and simultaneously combining the maximum value of the wind condition data, executing yaw system control logic of the wind turbine, and adjusting the yaw system posture of the wind turbine until the wind is completely yawed.
It can be understood that, for the same inventive concept as the yaw attitude control method of the wind turbine generator provided in the foregoing embodiment, reference is made to the foregoing implementation manner for the more specific working principle of each module in the embodiment of the present invention, and details are not repeated in the embodiment of the present invention.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A wind turbine yaw attitude control system, comprising:
the wind condition acquisition module: acquiring historical operation environment data of the wind turbine generator, extracting features to obtain historical wind condition data of the wind turbine generator, and dividing the historical wind condition data of the wind turbine generator into a training set and a testing set after normalizing the historical wind condition data of the wind turbine generator;
the wind condition prediction module: constructing a wind condition prediction model, carrying out iterative training on the wind condition prediction model by using a training set, and evaluating the wind condition prediction model by using a test set until iteration is finished to obtain a trained wind condition prediction model, and predicting based on real-time operation environment data of a wind turbine generator by using the wind condition prediction model to obtain wind condition prediction data;
yaw control module: based on the current wind condition data and the input wind condition prediction data, and simultaneously combining the maximum value of the wind condition data, executing yaw system control logic of the wind turbine, and adjusting the yaw system posture of the wind turbine until the wind is completely yawed.
2. The yaw attitude control system of the wind turbine generator according to claim 1, wherein a preprocessing sub-module is preset in the wind condition acquisition module, and the preprocessing sub-module is used for preprocessing historical operation environment data of the wind turbine generator so as to extract features.
3. The yaw attitude control system of a wind turbine generator according to claim 1, wherein the normalization processing is performed on historical wind condition data of the wind turbine generator, and a specific calculation formula of the normalization processing is as follows:
wherein Q is i For historical wind condition data of wind turbine generator, Q i ' normalized historical wind condition data, Q max Maximum value of historical wind condition data before normalization, Q min Is the minimum value of the historical wind condition data before normalization.
4. The wind turbine yaw attitude control system of claim 1, wherein the specific training process of the wind condition prediction model is as follows:
the wind condition prediction model is constructed by combining a BP neural network model and a PSO algorithm,
initializing PSO parameters of a wind condition prediction model, wherein the PSO parameters comprise the number of PSOs, the maximum iteration number, local learning factors and the overall learning factor, randomly acquiring the initial position and the initial speed of the PSOs in an initialization value range, constructing an fitness function in the training process of the wind condition prediction model, and iteratively updating the PSO parameters through the fitness function;
judging whether iteration is stopped or not, if not, continuing iteration until the maximum iteration times are reached; if so, judging whether the iteration number reaches the maximum iteration number, if not, returning to the previous step until the maximum iteration number is reached; if yes, completing forward propagation of the wind condition prediction model, and updating parameters of the wind condition prediction model through gradient backward propagation;
and after the back propagation is completed, outputting a wind condition prediction model which is completed with training, evaluating the wind condition prediction model which is completed with training based on a test set, and outputting the wind condition prediction model which is completed with training based on an evaluation result.
5. The yaw attitude control system of the wind turbine generator according to claim 4, wherein the fitness function in the wind condition prediction model training process is constructed, and the fitness function has the following specific calculation formula:
wherein E is ak For the connection weight of the input layer and the hidden layer, a and k are constants, N k As threshold of hidden layer, M a Is a feature matrix.
6. The wind turbine yaw attitude control system of any one of claims 1-5, wherein the wind condition data is wind direction and wind speed data.
7. The yaw attitude control system of a wind turbine generator according to any one of claim 6, wherein a first set value a, a second set value B and a third set value C of wind speed data are set in yaw system control logic of the wind turbine generator, the first set value a is specifically a minimum value, the second set value B is specifically a standard value, and the third set value C is specifically a maximum value, and the specific steps include:
setting a first time period based on the current wind condition data, solving the average value of the current wind direction data in the first time period, and calculating the deviation angle between the average value of the current wind direction data and the engine room of the wind turbine generator;
judging whether the deviation angle absolute value is lower than the minimum yaw angle of the yaw system or not based on the solved deviation angle absolute value, and if so, not starting the yaw system; if the number is greater than or equal to the preset number, entering the next step;
predicting current wind condition data through a wind condition prediction model which is trained, and acquiring wind direction and wind speed data in a second time period;
judging whether the wind speed data is smaller than a first set value A, if so, stopping starting a yaw system of the wind turbine generator; if not, judging whether the wind speed data is between a first set value A and a second set value B, if so, starting a yaw system of the wind turbine, controlling the yaw system of the wind turbine to execute yaw at a preset first yaw speed, and stopping the yaw system of the wind turbine until the wind speed data is lower than the first set value A; if not, judging whether the wind speed data is between a second set value B and a third set value C, if not, judging that the wind speed data is larger than the third set value C, stopping starting a yaw system of the wind turbine, if so, starting the yaw system of the wind turbine, controlling the yaw system of the wind turbine to execute yaw at a preset second yaw speed, and stopping the yaw system of the wind turbine until the wind speed data is lower than the first set value A.
8. The yaw attitude control method of the wind turbine generator is characterized by comprising the following steps of:
acquiring historical operation environment data of the wind turbine generator, extracting features to obtain historical wind condition data of the wind turbine generator, and dividing the historical wind condition data of the wind turbine generator into a training set and a testing set after normalizing the historical wind condition data of the wind turbine generator;
constructing a wind condition prediction model, carrying out iterative training on the wind condition prediction model by using a training set, and evaluating the wind condition prediction model by using a test set until iteration is finished to obtain a trained wind condition prediction model, and predicting based on real-time operation environment data of a wind turbine generator by using the wind condition prediction model to obtain wind condition prediction data;
based on the current wind condition data and the input wind condition prediction data, and simultaneously combining the maximum value of the wind condition data, executing yaw system control logic of the wind turbine, and adjusting the yaw system posture of the wind turbine until the wind is completely yawed.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117869217A (en) * | 2024-01-24 | 2024-04-12 | 武汉联动设计股份有限公司 | Wind turbine generator monitoring method and system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117869217A (en) * | 2024-01-24 | 2024-04-12 | 武汉联动设计股份有限公司 | Wind turbine generator monitoring method and system |
| CN117869217B (en) * | 2024-01-24 | 2024-08-06 | 中国华电科工集团有限公司 | Wind turbine generator monitoring method and system |
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