CN116608087A - Control method, system, equipment and storage medium for yaw error of unit - Google Patents
Control method, system, equipment and storage medium for yaw error of unit Download PDFInfo
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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
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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
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
- G06V10/763—Non-hierarchical techniques, e.g. based on statistics of modelling distributions
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- 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
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/32—Wind speeds
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- 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
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/335—Output power or torque
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- 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 embodiment of the application provides a control method, a system, equipment and a storage medium for yaw error of a unit, and belongs to the technical field of wind power generation. The control method of the yaw error of the unit comprises the following steps: acquiring unit operation data; the unit operation data comprise current wind speed data and power data; based on a preset diagonal angle interval, carrying out interval treatment on unit operation data, and fitting to obtain a plurality of power curve subintervals; determining an optimal power subinterval from a plurality of power curve subintervals; judging whether a yaw error exists in the unit according to the optimal power subinterval; if the yaw error exists in the unit, determining the current yaw error value of the unit according to the current wind speed data; and controlling the yaw error of the unit according to the current yaw error value of the unit. The yaw error value accuracy is improved, the yaw control mode of the wind turbine generator system is optimized, and the generated energy of the wind turbine generator system is improved.
Description
Technical Field
The application relates to the technical field of wind power generation, in particular to a control method of yaw error of a unit, a control system of yaw error of the unit, electronic equipment and a readable storage medium.
Background
The wind measuring device of the traditional wind turbine generator is arranged at the tail part of the wind turbine, is influenced by wind wheel disturbance and the appearance of the engine room, and cannot accurately measure wind speed and wind direction. According to incomplete statistics, the average yaw error of fans of most wind farms is between 4 and 12 degrees, but only a very small number of units with quite large yaw errors can be visually distinguished and adjusted. Yaw errors can cause the consistency coefficient of the power characteristics of the unit to be reduced, and the generating capacity of the unit is affected.
In the prior art, real-time operation data of a wind turbine generator system based on wind speed, active power, yaw error, ambient temperature, ambient air pressure and the like are acquired and monitored by a supervisory control and data acquisition (SCADA) system, the data are processed and then are fitted with power curves, different power curves are quantitatively analyzed and interval ranges of inherent yaw error deviation values are determined, and finally the inherent yaw error values obtained through identification are directly compensated to actual yaw error measurement values in an incremental mode, but the accuracy rate of the yaw error obtained by the method is not high, so that the control effect of a determined turbine generator system control strategy is not ideal based on the yaw error.
Disclosure of Invention
The embodiment of the application aims to provide a method, a system, equipment and a storage medium for controlling yaw errors of a unit, so as to solve the problems.
In order to achieve the above object, an embodiment of the present application provides a method for controlling yaw error of a unit, including:
acquiring unit operation data; the unit operation data comprise current wind speed data and power data;
based on a preset diagonal angle interval, carrying out interval treatment on the unit operation data, and fitting to obtain a plurality of power curve subintervals;
determining an optimal power subinterval from the plurality of power curve subintervals;
judging whether a yaw error exists in the unit according to the optimal power subinterval;
if the yaw error exists in the unit, determining the current yaw error value of the unit according to the current wind speed data;
and controlling the yaw error of the unit according to the current yaw error value of the unit.
Optionally, the performing interval processing on the unit operation data based on a preset diagonal angle interval, and fitting to obtain a plurality of power curve subintervals includes:
obtaining a power scatter diagram according to the current wind speed data and the power data;
clustering the power scatter diagram by using a preset clustering model to obtain a target power scatter diagram;
determining abnormal data points in the target power scatter diagram;
correcting the abnormal data points, and fitting to obtain a target power curve;
and based on the preset diagonal angle interval, carrying out interval treatment on the target power curve to obtain a plurality of power curve subintervals.
Optionally, the determining the abnormal data point in the target power scatter diagram includes:
determining the mahalanobis distance between a clustering center in an initial power curve and sample data points of the same cluster as the clustering center according to the target power scatter diagram:
and judging whether abnormal data points exist in the initial power curve or not based on the mahalanobis distance between the sample data points in the initial power curve and the clustering center to which the sample data points belong.
Optionally, the performing the compartmentalization processing on the target power curve based on the preset diagonal angle interval to obtain a plurality of power curve subintervals includes:
dividing the preset diagonal interval into a plurality of diagonal subintervals with interval angles being preset angles;
and according to the opposite wind angle subinterval, carrying out interval treatment on the target power curve to obtain a plurality of power curve subintervals.
Optionally, the determining whether the yaw error exists in the unit includes:
and judging whether the absolute value of the diagonal angle of the optimal power subinterval is larger than a preset diagonal angle.
Optionally, the method further comprises:
acquiring historical wind speed data and a historical yaw error value corresponding to the historical wind speed data;
obtaining a wind speed probability distribution curve according to the historical wind speed data;
dividing the historical wind speed data based on the wind speed probability distribution curve to obtain a plurality of wind speed subintervals;
and calculating the mean value and standard deviation of yaw errors in the wind speed subintervals based on the wind speed subintervals to obtain a curve of yaw error values along with the change of wind speed.
Optionally, the determining, according to the current wind speed data, a current yaw error value of the unit includes:
and determining the current yaw error value of the unit according to the curve of the current wind speed data and the yaw error value changing along with the wind speed.
In a second aspect of the embodiment of the present application, there is provided a control system for yaw error of a machine set, including:
the data acquisition module is used for acquiring unit operation data; the unit operation data comprise current wind speed data and power data;
a yaw assessment module for:
based on a preset diagonal angle interval, carrying out interval treatment on the unit operation data, and fitting to obtain a plurality of power curve subintervals;
determining an optimal power subinterval from the plurality of power curve subintervals;
judging whether a yaw error exists in the unit according to the optimal power subinterval;
if the yaw error exists in the unit, determining the current yaw error value of the unit according to the current wind speed data;
and the unit control module is used for controlling the yaw error of the unit according to the current yaw error value of the unit.
And the unit control module is used for controlling the yaw error of the unit according to the current yaw error value of the unit.
In a third aspect of the embodiments of the present application, a processor is provided that is configured to perform the above-described method of controlling a yaw error of a unit.
A fourth aspect of the application provides a machine-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to be configured to perform the method of controlling a yaw error of a set as described above.
According to the embodiment of the application, the unit operation data is obtained, wherein the unit operation data comprises current wind speed data and power data, the unit operation data is subjected to interval processing based on a preset diagonal angle interval, a plurality of power curve subintervals are obtained through fitting, an optimal power subinterval is determined from all the power curve subintervals, whether the unit has yaw error is judged according to the optimal power subinterval of the power curve, if yes, the current yaw error value of the unit is determined according to the current wind speed data, and finally the yaw error of the unit is controlled according to an optimal yaw control strategy.
The embodiment of the application compares the interval wind angle and the power curve, and finds out the interval with the optimal power curve, and determines the yaw error value of the current unit based on the current wind speed data, thereby improving the accuracy of the yaw error value, optimizing the yaw control mode of the unit and improving the generating capacity of the wind generating unit.
Additional features and advantages of embodiments of the application will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:
FIG. 1 is a flow chart of a first embodiment of a method for controlling yaw error of a cluster of the present application;
FIG. 2 is a power scatter plot;
FIG. 3 is a graph of clustered power scatter;
FIG. 4 is a graph of corrected power scatter;
FIG. 5 is a target power curve;
FIG. 6 is a schematic diagram of the architecture of the control system of the unit yaw error according to the present application.
Detailed Description
The following describes the detailed implementation of the embodiments of the present application with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the application, are not intended to limit the application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
In the description of embodiments of the present application, the technical terms "first," "second," and the like are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless explicitly defined otherwise.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of a control method for yaw error of a machine set according to the present embodiment.
Step S100: acquiring unit operation data; the unit operation data comprise current wind speed data and power data.
The current wind speed data is obtained at the current running time of the unit.
The power data is useful power data of the unit during operation.
Step S200: based on a preset diagonal angle interval, the unit operation data are subjected to interval processing, and a plurality of power curve subintervals are obtained through fitting.
The compartmentalization process is to compress the data in a fixed interval range.
It can be understood that yaw wind is started when the wind angle of the wind turbine generator exceeds a certain range, the range of the wind angle is a preset wind angle interval, the preset wind angle interval is divided into a plurality of subintervals, then the regional processing is performed on the turbine generator operation data based on the subintervals of the preset wind angle, and the turbine generator operation data in each subinterval are fitted to obtain a plurality of power curve subintervals.
In one embodiment, step S200 includes the following steps:
step S201: and obtaining a power scatter diagram according to the current wind speed data and the power data.
The power scatter plot is a plot of the power scatter of the assembly at different wind speeds, as shown in FIG. 2.
Step S202: clustering the power scatter diagram by using a preset clustering model to obtain a target power scatter diagram.
The preset cluster model may be a K-means cluster algorithm, wherein the number of cluster centers is determined by a minimum cost function in the K-means cluster algorithm.
It will be appreciated that as shown in fig. 3, the clustered power scatter plot is divided into clusters, each cluster containing a cluster center.
Step S203: and determining abnormal data points in the target power scatter diagram.
An outlier data point is a data point that has a greater deviation than an adjacent data point.
It will be appreciated that it may be determined whether an anomaly exists in a sample data point of the same cluster as a cluster center based on calculating the mahalanobis distance between the cluster center and the sample data point.
Step S204: and correcting the abnormal data points and fitting to obtain a target power curve.
As shown in fig. 4, after correcting the abnormal data in the target power scatter diagram, all the data points basically form a smooth curve, and the target power curve is obtained by using a fitting means (as shown in fig. 5).
Step S205: and based on a preset diagonal angle interval, carrying out interval treatment on the target power curve to obtain a plurality of power curve subintervals.
It should be understood that, in this embodiment, the preset diagonal interval is divided into a plurality of diagonal intervals with preset interval angles, and then the target power curve is subjected to compartmentalization processing according to the diagonal intervals to obtain a plurality of power curve subintervals.
According to the embodiment, a power scatter diagram is obtained according to wind speed data and power data, a preset clustering model is used for clustering the power scatter diagram to obtain a target power scatter diagram, abnormal data points in the target power scatter diagram are determined, the abnormal data points are corrected, a target power curve is obtained by fitting, and a plurality of power curve subintervals are obtained by carrying out interval treatment on the target power curve based on a preset wind angle interval. In other words, the embodiment improves the accuracy of data by clustering the power scatter diagram of the unit power changing along with the wind speed and cleaning the abnormal data.
Step S300: and determining an optimal power subinterval from the plurality of power curve subintervals.
The optimal power subinterval is a diagonal angle subinterval with an optimal power curve.
Step S400: and judging whether yaw errors exist in the unit according to the optimal power subinterval.
It should be understood that, since the smaller the absolute value of the yaw error is, in this embodiment, by determining whether the absolute value of the yaw angle in the optimal power subinterval is greater than a set value, if not, it is indicated that the yaw error exists in the unit, and if so, it is indicated that the yaw error exists in the unit.
Step S500: if the yaw error exists in the unit, determining the current yaw error value of the unit according to the current wind speed data.
In one embodiment, step S500 includes the following steps:
step S501: historical wind speed data and a historical yaw error value corresponding to the historical wind speed data are obtained.
The historical wind speed data is wind speed data obtained by running the unit before the current moment.
The historical yaw error value is the yaw error value obtained according to the historical wind speed data before the current moment.
Step S502: and obtaining a wind speed probability distribution curve according to the historical wind speed data.
In the step, the probability of occurrence of different wind speed values is counted in the running process of the unit, so that a wind speed probability distribution curve is obtained.
Step S503: based on the wind speed probability distribution curve, the historical wind speed data is segmented, and a plurality of wind speed subintervals are obtained.
Step S504: and calculating the mean value and standard deviation of yaw errors in the wind speed subintervals based on the wind speed subintervals to obtain a curve of yaw error values along with the change of wind speed.
It should be understood that, in this embodiment, by performing and cutting on wind speed, the mean value and standard deviation of yaw error values in each wind speed section are calculated, a curve of yaw error values varying with wind speed is obtained, when yaw error control of a unit is performed subsequently, only the current wind speed is required to be obtained, the wind speed section where the current wind speed is located in the curve of yaw error values varying with wind speed is determined, and the yaw error mean value of the wind speed section is used as the yaw error value corresponding to the current wind speed.
According to the embodiment, the historical yaw error value corresponding to the historical wind speed data is obtained, the wind speed probability distribution curve is obtained according to the historical wind speed data, the historical wind speed data is divided based on the wind speed probability distribution curve to obtain a plurality of wind speed subintervals, the mean value and the standard deviation of yaw errors in each wind speed subinterval are calculated based on the wind speed subintervals, the curve of the yaw error value changing along with the wind speed is obtained, abrasion of a unit caused by frequent deviation adjustment is reduced, and practicality is improved.
Step S600: and controlling the yaw error of the unit according to the current yaw error value of the unit.
The yaw of the unit is corrected according to the current yaw error value of the unit.
According to the embodiment, the unit operation data are obtained, wherein the unit operation data comprise current wind speed data and power data, the unit operation data are subjected to interval processing based on a preset opposite wind angle interval, a plurality of power curve subintervals are obtained through fitting, an optimal power subinterval is determined from the plurality of power curve subintervals, whether the unit has yaw error is judged according to the optimal power subinterval of the power curve, if yes, the current yaw error value of the unit is determined according to the current wind speed data, finally the yaw error of the unit is controlled according to an optimal yaw control strategy, accuracy of the yaw error value is improved, a yaw control mode of the unit is optimized, and the generating capacity of the wind generating set is improved.
Example two
Referring to fig. 6, fig. 6 is a schematic diagram of a control system architecture for yaw error of a machine set according to an embodiment of the present application.
As shown in fig. 6, the control system for the crew yaw error includes a data acquisition module 10, a yaw estimation module 20, and a crew control module 30.
The data acquisition module 10 may be a SCADA system, and the data acquisition module 10 is connected to the yaw estimation module 20 and the crew control module 30, respectively. In the operation process, the data acquisition module 10 acquires unit operation data from the unit control module 30 and sends the unit operation data to the yaw evaluation module 20 so as to support the yaw evaluation module 20 to determine the current yaw error value of the unit according to the unit operation data; the data acquisition module 10 may also receive the current yaw error value sent by the yaw estimation module 20 and send the current yaw error value to the crew control module 30 to support the crew control module 30 to control the crew according to the current yaw error value.
Yaw estimation module 20 may be a software system running on a server, yaw estimation module 20 being coupled to data acquisition module 10. In the operation process, the yaw evaluation module 20 receives the unit operation data sent by the data acquisition module 10, performs interval processing on the unit operation data based on a preset diagonal angle interval, fits to obtain a plurality of power curve subintervals, determines an optimal power subinterval according to all the power curve subintervals, judges whether a yaw error exists in the unit according to the optimal power subinterval, and finally determines a current yaw error value of the unit according to current wind speed data, and sends the current yaw error value to the data acquisition module 10.
The unit control module 30 may be a control unit of a wind generating unit, and the unit control module 30 is connected to the data acquisition module 10. During operation, the unit control module 30 monitors the operation data of the wind turbine in real time so as to support the data acquisition module 10 to acquire the unit operation data from the unit control module 30; the unit control module 30 can also receive the current yaw error value sent by the data acquisition module 10, and yaw control the wind turbine unit according to the current yaw error value.
Example III
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
Example IV
Embodiments of the present application also provide a computer readable storage medium having stored thereon instructions for a program adapted to perform the steps of a method of controlling yaw error of an organic group when being executed by a processor.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, various possible combinations of embodiments of the present application are not described in detail.
In addition, the functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. A method for controlling yaw error of a machine set, comprising:
acquiring unit operation data; the unit operation data comprise current wind speed data and power data;
based on a preset diagonal angle interval, carrying out interval treatment on the unit operation data, and fitting to obtain a plurality of power curve subintervals;
determining an optimal power subinterval from the plurality of power curve subintervals;
judging whether a yaw error exists in the unit according to the optimal power subinterval;
if the yaw error exists in the unit, determining the current yaw error value of the unit according to the current wind speed data;
and controlling the yaw error of the unit according to the current yaw error value of the unit.
2. The method for controlling yaw error of a unit according to claim 1, wherein the performing an interval process on the unit operation data based on a preset diagonal interval and fitting to obtain a plurality of power curve subintervals includes:
obtaining a power scatter diagram according to the current wind speed data and the power data;
clustering the power scatter diagram by using a preset clustering model to obtain a target power scatter diagram;
determining abnormal data points in the target power scatter diagram;
correcting the abnormal data points, and fitting to obtain a target power curve;
and based on the preset diagonal angle interval, carrying out interval treatment on the target power curve to obtain a plurality of power curve subintervals.
3. The method of controlling a yaw error of a machine set of claim 2, wherein the determining abnormal data points in the target power scatter plot comprises:
determining the mahalanobis distance between a clustering center in an initial power curve and sample data points of the same cluster as the clustering center according to the target power scatter diagram:
and judging whether abnormal data points exist in the initial power curve or not based on the mahalanobis distance between the sample data points in the initial power curve and the clustering center to which the sample data points belong.
4. The method for controlling yaw error of a unit according to claim 3, wherein the performing an interval process on the target power curve based on the preset diagonal interval to obtain a plurality of power curve subintervals includes:
dividing the preset diagonal interval into a plurality of diagonal subintervals with interval angles being preset angles;
and according to the opposite wind angle subinterval, carrying out interval treatment on the target power curve to obtain a plurality of power curve subintervals.
5. The method for controlling yaw error of a machine set according to claim 1, wherein the determining whether the yaw error exists in the machine set comprises:
and judging whether the absolute value of the diagonal angle of the optimal power subinterval is larger than a preset diagonal angle.
6. The method of controlling a yaw error of a machine set according to claim 1, further comprising:
acquiring historical wind speed data and a historical yaw error value corresponding to the historical wind speed data;
obtaining a wind speed probability distribution curve according to the historical wind speed data;
dividing the historical wind speed data based on the wind speed probability distribution curve to obtain a plurality of wind speed subintervals;
and calculating the mean value and standard deviation of yaw errors in the wind speed subintervals based on the wind speed subintervals to obtain a curve of yaw error values along with the change of wind speed.
7. The method for controlling yaw error of a wind turbine according to claim 6, wherein determining a current yaw error value of the wind turbine based on the current wind speed data comprises:
and determining the current yaw error value of the unit according to the curve of the current wind speed data and the yaw error value changing along with the wind speed.
8. A control system for yaw error of a unit, comprising:
the data acquisition module is used for acquiring unit operation data; the unit operation data comprise current wind speed data and power data;
a yaw assessment module for:
based on a preset diagonal angle interval, carrying out interval treatment on the unit operation data, and fitting to obtain a plurality of power curve subintervals;
determining an optimal power subinterval from the plurality of power curve subintervals;
judging whether a yaw error exists in the unit according to the optimal power subinterval;
if the yaw error exists in the unit, determining the current yaw error value of the unit according to the current wind speed data;
and the unit control module is used for controlling the yaw error of the unit according to the current yaw error value of the unit.
9. An electronic device, comprising: a processor and a memory storing machine readable instructions executable by the processor, which when executed by the processor, perform the method of controlling a yaw error of a unit as claimed in any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon instructions for causing a machine to perform the method of controlling yaw error of a unit according to any one of claims 1-7.
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| CN202310077547.0A CN116608087A (en) | 2023-01-30 | 2023-01-30 | Control method, system, equipment and storage medium for yaw error of unit |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310077547.0A CN116608087A (en) | 2023-01-30 | 2023-01-30 | Control method, system, equipment and storage medium for yaw error of unit |
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| CN202310077547.0A Pending CN116608087A (en) | 2023-01-30 | 2023-01-30 | Control method, system, equipment and storage medium for yaw error of unit |
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| CN (1) | CN116608087A (en) |
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