CN118148832A - Active wake flow control method and system based on active yaw - Google Patents
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Abstract
The invention discloses an active wake flow control method and an active wake flow control system based on active yaw, which relate to the technical field of wind energy engineering, and the method comprises the following steps: the running state set of the target wind driven generator is obtained through real-time monitoring of the sensor; analyzing the running state set, and identifying the influence area and scale of the wake effect; performing yaw mapping on the influence area and the scale according to the wake flow influence-yaw mapping model, and determining the yaw angle of the target wind driven generator; obtaining the adjustment influence of wake flow effect by optimizing the air flow distribution; and carrying out feedback control on the yaw angle according to the adjustment influence of the wake effect, obtaining an optimal control strategy, and carrying out active wake control. The invention solves the technical problems of low wind energy utilization rate and reduced overall efficiency of the wind power plant caused by wake effect in the prior art, and achieves the technical effects of optimizing the influence of the wake effect and improving the overall operation efficiency of the wind power plant.
Description
Technical Field
The invention relates to the technical field of wind energy engineering, in particular to an active wake flow control method and system based on active yaw.
Background
As the world's energy demand continues to grow, traditional fossil energy supplies have been increasingly under pressure. At the same time, environmental and climate change problems are also increasing, which makes the development and utilization of renewable energy a global focus of attention. Against this background, wind energy has been widely studied and used as a form of clean, renewable energy source. However, wake effect problems faced in wind power generation technology also present challenges to the operation of wind farms.
When the wind generating set operates, the problem that the wind energy utilization rate is reduced and the overall efficiency of the wind power plant is reduced due to the wake effect generated by a turbulent flow area formed in the air running in front exists.
Disclosure of Invention
The application provides an active wake flow control method and an active wake flow control system based on active yaw, which are used for solving the technical problems of low wind energy utilization rate and low overall efficiency of a wind farm caused by wake flow effect in the prior art.
In view of the above, the present application provides an active wake control method and system based on active yaw.
In a first aspect of the application, an active wake control method based on active yaw is provided, the method comprising:
The running state set of the target wind driven generator is obtained through real-time monitoring of the sensor; analyzing the running state set, and identifying the influence area and the scale of the wake effect; performing yaw mapping on the influence area and the scale according to the wake flow influence-yaw mapping model, and determining the yaw angle of the target wind driven generator; obtaining the adjustment influence of the wake effect by optimizing the air flow distribution; and carrying out feedback control on the yaw angle according to the adjustment influence of the wake effect to obtain an optimal control strategy, and carrying out active wake control.
In a second aspect of the application, an active wake control system based on active yaw is provided, the system comprising:
The monitoring module monitors and acquires the running state set of the target wind driven generator in real time through a sensor; the running state set analysis module is used for analyzing the running state set and identifying the influence area and the scale of the wake effect; the yaw angle determining module is used for performing yaw mapping on the influence area and the scale according to the wake flow influence-yaw mapping model and determining the yaw angle of the target wind driven generator; the adjustment influence acquisition module is used for acquiring the adjustment influence of the wake effect by optimizing the airflow distribution; and the wake flow control module is used for carrying out feedback control on the yaw angle according to the adjustment influence of the wake flow effect, obtaining an optimal control strategy and carrying out active wake flow control.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The running state set of the target wind driven generator is obtained through real-time monitoring of the sensor; analyzing the running state set, and identifying the influence area and the scale of the wake effect; performing yaw mapping on the influence area and the scale according to the wake flow influence-yaw mapping model, and determining the yaw angle of the target wind driven generator; obtaining the adjustment influence of the wake effect by optimizing the air flow distribution; and carrying out feedback control on the yaw angle according to the adjustment influence of the wake effect to obtain an optimal control strategy, and carrying out active wake control. The application solves the technical problems of low wind energy utilization rate and reduced overall efficiency of the wind power plant caused by wake effect in the prior art, and achieves the technical effects of optimizing the influence of the wake effect and improving the overall operation efficiency of the wind power plant.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an active wake control method based on active yaw according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a yaw mapping process for an affected area and scale in an active wake control method based on active yaw according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an active wake control system based on active yaw according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a monitoring module 11, an operation state set analysis module 12, a yaw angle determination module 13, an adjustment influence acquisition module 14 and a wake flow control module 15.
Detailed Description
The application provides an active wake flow control method and an active wake flow control system based on active yaw, which are used for solving the technical problems of low wind energy utilization rate and reduced overall efficiency of a wind power plant caused by wake flow effect in the prior art, and achieving the technical effects of optimizing the influence of the wake flow effect and improving the overall operation efficiency of the wind power plant.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides an active wake control method based on active yaw, the method comprising:
step S100: the running state set of the target wind driven generator is obtained through real-time monitoring of the sensor;
In the embodiment of the application, a series of sensors are required to be selected in order to comprehensively monitor the running state of the wind driven generator. Including wind speed and direction sensors, temperature and humidity sensors, pressure sensors, vibration sensors, current and voltage sensors, etc. These sensors are able to provide critical information about the wind turbine and its surroundings.
The selected sensors are mounted on the main components of the wind power generator, such as the blades, the generator, the tower, etc. These sensors need to be able to accurately measure various parameters such as wind speed, wind direction, temperature, pressure etc. Meanwhile, the sensor is also required to be connected with a data acquisition system so as to be capable of transmitting data in real time.
The sensor will continuously collect data with a preset acquisition frequency. Such data includes wind speed, wind direction, temperature, pressure, vibration, etc., as well as current, voltage, power, etc. of the generator, and the data is transmitted to the data receiving device by wired or wireless means. The data center receives and stores the data in real time to form an operation state set of the wind driven generator.
Step S200: analyzing the running state set, and identifying the influence area and the scale of the wake effect;
In the embodiment of the application, the running state set is preprocessed, and the characteristics related to the wake effect are extracted from the preprocessed data set. These characteristics include wind speed, wind direction, turbulence intensity, temperature, pressure, etc. The purpose of feature extraction is to extract key information from the raw data that can reflect wake effects. And carrying out time sequence analysis on the extracted characteristics to identify dynamic change of wake effect. By analyzing the time series data, patterns and features of wake effects are identified. This includes identifying the shape, size, diffusion rate, etc. of the wake region.
Based on the result of pattern recognition, the area of influence and the size of wake effects are further determined. The area of influence refers to the area that is directly affected by the wake effect, while the scale describes the intensity and extent of the wake effect.
Step S300: performing yaw mapping on the influence area and the scale according to the wake flow influence-yaw mapping model, and determining the yaw angle of the target wind driven generator;
in the embodiment of the application, a wake effect-yaw mapping model is firstly established to describe the relationship between the wake effect and the yaw angle of the wind driven generator. This model is based on a deep understanding of wake effects, as well as the operating principle and dynamics of the wind turbine.
Training a wake influence-yaw mapping model by using the running state of the wind driven generator, the influence area and scale of the wake effect and corresponding yaw angle records, and learning the mapping relation between the wake effect and the yaw angle. The area of influence and the scale of wake effects identified by analyzing the running state set are input as input data into the established wake effect-yaw mapping model. And outputting a corresponding yaw angle by the model through an internal algorithm and calculation according to the input influence area and scale.
Step S400: obtaining the adjustment influence of the wake effect by optimizing the air flow distribution;
In the embodiments of the present application, the specific effect of wake effects on the surrounding airflow distribution is well understood. This includes how wake changes key parameters such as wind speed, turbulence intensity and temperature gradient, and how these changes affect the overall performance of the wind farm. Based on the understanding of wake effects, a mathematical model describing the airflow distribution in the wind farm is established. This model may include hydrodynamic equations, boundary conditions, initial conditions, etc. for simulating airflow dynamics in a wind farm.
Numerical simulation methods, such as finite element analysis, finite volume methods, and the like, are used to solve and build the mathematical model. By setting different initial conditions and boundary conditions, the influence of wake effects on the airflow distribution under different conditions can be simulated.
The results of the numerical simulation are analyzed to understand how wake effects affect the performance of the wind farm. Including observing changes in airflow distribution, assessing the output power of the wind motor, analyzing wind energy utilization efficiency, and the like. Based on the analysis of the simulation results, a series of optimization schemes are proposed. These include adjusting the layout of the wind motors, changing the wind direction control strategy, optimizing yaw angle, etc. And evaluating the proposed optimization scheme, and comparing the air flow distribution and the wind power field performance under different schemes. By contrast analysis, an optimal scheme is selected for implementation.
According to the selected optimization scheme, corresponding measures are implemented to improve the airflow distribution of the wind power plant. After implementation of the optimization measures, it is necessary to continuously monitor the airflow distribution and the operational performance of the wind farm. And acquiring the adjustment influence of the wake effect through real-time data acquisition and analysis.
Step S500: and carrying out feedback control on the yaw angle according to the adjustment influence of the wake effect to obtain an optimal control strategy, and carrying out active wake control.
In the embodiment of the application, a feedback control system is required to be established for monitoring the state of the wind power plant and the change of wake effect in real time. The system comprises a sensor network, a data acquisition system, a communication network, a control center and other components for collecting data, transmitting information and performing control operations.
Through sensor network and data acquisition system, the change of wake effect in the real-time supervision wind power plant. Including monitoring parameters such as wind speed, wind direction, turbulence intensity, temperature, pressure, etc., the operating state and output power of the wind motor. Based on the monitored wake effect changes, the effect of these changes on the performance of the wind farm is analyzed. Including evaluating the output power of the wind motor, analyzing wind energy utilization efficiency, predicting maintenance requirements, etc.
And calculating the optimal yaw angle by using an optimization algorithm according to the analysis result. This algorithm includes linear programming, nonlinear programming, etc., for finding yaw angles that maximize wind farm performance. Based on the calculated optimal yaw angle, a corresponding control command is generated. These commands are sent to the control system of the wind motor via the communication network, directing the control system to adjust the yaw angle of the wind motor.
Finally, after receiving the control command, the control system of the wind motor can adjust the yaw angle of the wind motor according to the command.
Further, the method further comprises:
Denoising the running state set, and analyzing a time sequence state according to the wake effect to generate periodic state characteristics;
Performing variance calculation on the periodic state characteristics, and performing variance statistics to obtain a variance statistics result;
and determining the correlation between the wake effect and the running state according to the variance statistical result, comparing the correlation in the domain, and obtaining the influence area and the scale of the target wind driven generator based on the wake effect.
In the embodiment of the application, in the actual sensor data acquisition process, data containing noise is often acquired due to various reasons, such as sensor errors, environmental noise and the like. The noise may interfere with the subsequent data analysis, so that the noise in the running state set needs to be removed by a filtering method, a median filtering method, a statistical filtering method and the like.
The wake effect is a dynamic process that affects the presentation of a certain regularity over time. In order to better understand wake effects, a time-series state analysis is required. And carrying out preliminary time sequence analysis on the denoised running state data, and identifying state parameters related to wake effects. These parameters are then subjected to further timing analysis such as trend analysis, periodicity analysis, etc. The state characteristics of wake effects at different time points are identified through time sequence state analysis, then the time sequence is processed by methods such as Fourier transformation, wavelet transformation and the like, the periodic characteristics are extracted, and the characteristics reflecting the periodic changes of the wake effects are generated.
And carrying out variance calculation on the periodic state characteristics, and carrying out statistics on the variance to obtain a variance statistical result. And carrying out correlation analysis by using methods such as correlation coefficient, regression analysis and the like according to the variance statistics result to obtain the correlation between the wake effect and the running state parameter. And (3) comparing and analyzing the correlation between the wake effect and the running state parameter on a time axis, observing the dynamic change relation between the wake effect and the running state parameter, and knowing the dynamic relation between the wake effect and the running state.
And finally, comprehensively judging the influence area and the scale of the wake effect according to the correlation analysis result and the correlation comparison result in the time domain and combining the actual conditions of the wind power plant, such as terrain, wind direction distribution and the like, and determining the specific influence area and the scale of the wake effect.
Further, the method further comprises:
Constructing a mode identification channel, clustering the correlation, and carrying out channel training through a clustering result to obtain a wake effect mode and characteristics;
and determining wake influence according to the wake effect mode and the characteristics, and determining the influence area and the scale.
In the embodiment of the application, in order to effectively identify and classify the mode of the wake effect, a mode identification channel is constructed. Based on the data driven approach, pattern recognition channels are designed and constructed based on the collected operational state data and knowledge about wake effects. This channel may be based on different feature choices, data preprocessing, and classifier designs.
In order to better organize and manage the patterns of wake effects, cluster analysis is required. Clustering algorithms, such as K-means, are used to perform cluster analysis on the data in the pattern recognition channels. These algorithms group based on similarity between data points, forming different clusters. The pattern recognition channels are trained using historical data or known wake effect samples. The method comprises the steps of feature selection, model parameter adjustment, optimization and the like, so that the channel can accurately classify the mode of the wake effect. And analyzing the output of the trained pattern recognition channel, and extracting the pattern and the characteristics of the wake effect.
Based on the determined wake effect influence, its area of influence and scale may be further determined. And analyzing and determining the influence area and the scale of the wake effect by combining the geographic information, the meteorological data and the mode and the characteristics of the wake effect of the wind power plant. In the process, the factors such as the geographic information system technology, the spatial analysis method, the wind direction and the wind speed distribution and the like need to be comprehensively considered.
Further, the method further comprises:
Performing wind farm numerical simulation according to the periodic state characteristics to obtain simulation influence data of the wake effect;
And according to the simulation influence data and the actual wind farm numerical value, the simulation influence data of the wake effect is mapped and corrected, and the influence area and the scale of the wake effect are obtained.
In the embodiment of the application, in order to deeply understand the influence of wake effects on a wind power plant, numerical simulation needs to be performed, and numerical simulation software such as wind energy simulation software and the like is used. And setting a corresponding physical model, boundary conditions and initial conditions according to the extracted periodic state characteristics, and simulating the specific influence of the wake effect in the wind power plant through numerical simulation to generate simulation influence data.
The analog data may be different from the actual situation and need to be adjusted. The difference between the influence data and the actual wind power plant operation data is simulated through comparison analysis, and the difference comprises comparison of key parameters such as wind speed distribution, turbulence intensity, wind energy utilization rate and the like. And by contrast analysis, the deviation between simulation and actual is identified, and a basis is provided for subsequent mapping correction.
Based on the result of the comparative analysis, the simulated influence data of the wake effect is adjusted and corrected. This process includes adjusting model parameters, optimizing the physical model, or introducing empirical correction factors, etc. Through mapping correction, the accuracy of wake effect simulation data is improved, and the influence of wake effect simulation data in an actual wind power plant is reflected more accurately.
Based on the corrected simulation influence data, analyzing the specific influence area and scale of the wake effect in the wind power plant by comprehensively considering factors such as a Geographic Information System (GIS) technology, a spatial analysis method, wind direction and wind speed distribution, and finally determining the actual influence area and scale of the wake effect in the wind power plant.
Further, as shown in fig. 2, step S300 in the method provided in the application embodiment further includes:
Establishing the wake flow influence-yaw mapping model, and acquiring a yaw angle set by training an influence area and scale records of wake flow influence;
And performing yaw mapping on the influence area and the scale through the wake influence-yaw mapping model through the yaw angle set, and determining the yaw angle of the target wind driven generator.
In an embodiment of the application, a mapping model is built in order to relate the effect of wake effects to yaw angle. Based on the known wake effect influence data and the geographical information of the wind farm, a wake effect-yaw mapping model is established for describing the relationship between wake effect and yaw angle.
A wake effect-yaw mapping model is established using a convolutional neural network, and is trained using recorded data of known wake effect impact regions and scales. These data are a subset of the historical operating data of the wind farm, including a record of wake effects and corresponding yaw angle information. The model extracts a yaw angle set by analyzing yaw angle changes under the influence of different wake effects in training data. This yaw angle set covers the yaw behavior of the wind park under the influence of different wake effects.
The simulated or actual impact data of the wake effect is input into a wake impact-yaw mapping model. And outputting a corresponding yaw angle through model calculation. And comprehensively judging the optimal yaw angle of the target wind driven generator according to the prediction result of the mapping model and the actual conditions of the wind power plant, such as wind direction, wind speed and the like.
Further, the method further comprises:
Monitoring the running state set and the wind farm performance of the target wind driven generator through an active control center, optimizing air flow distribution, and obtaining an air flow distribution result;
and monitoring and acquiring the adjustment influence of the wake effect according to the airflow distribution result.
In the embodiment of the application, the active control center is used for monitoring the running state set of the target wind driven generator in real time by utilizing the sensor and the monitoring system, wherein the running state set comprises key parameters such as wind speed, wind direction, generator rotating speed, power output and the like. In addition to the operational status of the individual wind turbines, the active control center may also monitor overall performance metrics of the wind farm, such as wind farm output power, energy conversion efficiency, turbulence intensity, and the like.
Based on the monitored running state set and the wind power plant performance data, the airflow distribution of the wind power plant is optimized by using an algorithm and a model of an active control center. This step includes adjusting the yaw angle of the wind turbine, starting and stopping certain generators, and so on. And according to the output of the optimization algorithm, the active control center generates an airflow distribution result. This result describes the optimized wind farm airflow distribution.
After the airflow distribution optimization is implemented, the active control center continuously monitors the change of wake effects in the wind power plant by observing the change of key parameters such as power output, turbulence intensity and the like of the wind generating set, and obtains the adjustment influence of the wake effects.
Further, the method further comprises:
When the adjustment influence of the wake effect does not meet the wake influence threshold, performing feedback control on the yaw angle to obtain the optimal control strategy;
and performing active wake flow control according to the optimized control strategy.
In the embodiment of the application, the monitored wake effect adjustment effect is compared with the threshold value by setting a wake effect threshold value. If the adjustment effect does not reach the threshold, further optimization control is required. And the active control center calculates a new yaw angle according to the real-time running state of the wind power plant and the influence of the adjustment of the wake effect. These angular adjustments aim to further optimize the airflow distribution, reducing the effect of wake effects. Based on the monitoring data of the yaw angle adjustment and the wind farm performance for many times, the active control center outputs a set of optimal control strategy, namely the optimal control strategy.
According to the optimized control strategy, the active control center guides the wind generating set in the wind power plant to perform corresponding yaw angle adjustment or other control actions, and the influence of wake flow effect is reduced.
In summary, the embodiment of the application has at least the following technical effects:
The running state set of the target wind driven generator is obtained through real-time monitoring of the sensor; analyzing the running state set, and identifying the influence area and the scale of the wake effect; performing yaw mapping on the influence area and the scale according to the wake flow influence-yaw mapping model, and determining the yaw angle of the target wind driven generator; obtaining the adjustment influence of the wake effect by optimizing the air flow distribution; and carrying out feedback control on the yaw angle according to the adjustment influence of the wake effect to obtain an optimal control strategy, and carrying out active wake control. The application solves the technical problems of low wind energy utilization rate and reduced overall efficiency of the wind power plant caused by wake effect in the prior art, and achieves the technical effects of optimizing the influence of the wake effect and improving the overall operation efficiency of the wind power plant.
Example two
Based on the same inventive concept as the active wake control method based on active yaw in the foregoing embodiments, as shown in fig. 3, the present application provides an active wake control system based on active yaw, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
The monitoring module 11 is used for monitoring and acquiring an operation state set of the target wind driven generator in real time through a sensor by the monitoring module 11;
An operation state set analysis module 12, the operation state set analysis module 12 analyzing the operation state set to identify an influence region and a scale of wake effects;
a yaw angle determining module 13, wherein the yaw angle determining module 13 performs yaw mapping on the influence area and the scale according to the wake influence-yaw mapping model to determine the yaw angle of the target wind driven generator;
an adjustment influence acquisition module 14, wherein the adjustment influence acquisition module 14 acquires the adjustment influence of the wake effect by optimizing the airflow distribution;
And the wake control module 15 is used for carrying out feedback control on the yaw angle according to the adjustment influence of the wake effect, obtaining an optimized control strategy and carrying out active wake control.
Further, the system further comprises:
Denoising the running state set, and analyzing a time sequence state according to the wake effect to generate periodic state characteristics;
Performing variance calculation on the periodic state characteristics, and performing variance statistics to obtain a variance statistics result;
and determining the correlation between the wake effect and the running state according to the variance statistical result, comparing the correlation in the domain, and obtaining the influence area and the scale of the target wind driven generator based on the wake effect.
Further, the system further comprises:
Constructing a mode identification channel, clustering the correlation, and carrying out channel training through a clustering result to obtain a wake effect mode and characteristics;
and determining wake influence according to the wake effect mode and the characteristics, and determining the influence area and the scale.
Further, the system further comprises:
Performing wind farm numerical simulation according to the periodic state characteristics to obtain simulation influence data of the wake effect;
And according to the simulation influence data and the actual wind farm numerical value, the simulation influence data of the wake effect is mapped and corrected, and the influence area and the scale of the wake effect are obtained.
Further, the system further comprises:
Establishing the wake flow influence-yaw mapping model, and acquiring a yaw angle set by training an influence area and scale records of wake flow influence;
And performing yaw mapping on the influence area and the scale through the wake influence-yaw mapping model through the yaw angle set, and determining the yaw angle of the target wind driven generator.
Further, the system further comprises:
Monitoring the running state set and the wind farm performance of the target wind driven generator through an active control center, optimizing air flow distribution, and obtaining an air flow distribution result;
and monitoring and acquiring the adjustment influence of the wake effect according to the airflow distribution result.
Further, the system further comprises:
When the adjustment influence of the wake effect does not meet the wake influence threshold, performing feedback control on the yaw angle to obtain the optimal control strategy;
and performing active wake flow control according to the optimized control strategy.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (8)
1. An active wake control method based on active yaw, the method comprising:
the running state set of the target wind driven generator is obtained through real-time monitoring of the sensor;
analyzing the running state set, and identifying the influence area and the scale of the wake effect;
Performing yaw mapping on the influence area and the scale according to the wake flow influence-yaw mapping model, and determining the yaw angle of the target wind driven generator;
obtaining the adjustment influence of the wake effect by optimizing the air flow distribution;
And carrying out feedback control on the yaw angle according to the adjustment influence of the wake effect to obtain an optimal control strategy, and carrying out active wake control.
2. The method of claim 1, wherein analyzing the set of operating conditions, identifying areas and scales of influence of wake effects, comprises:
Denoising the running state set, and analyzing a time sequence state according to the wake effect to generate periodic state characteristics;
Performing variance calculation on the periodic state characteristics, and performing variance statistics to obtain a variance statistics result;
and determining the correlation between the wake effect and the running state according to the variance statistical result, comparing the correlation in the domain, and obtaining the influence area and the scale of the target wind driven generator based on the wake effect.
3. The method of claim 2, wherein the method further comprises:
Constructing a mode identification channel, clustering the correlation, and carrying out channel training through a clustering result to obtain a wake effect mode and characteristics;
and determining wake influence according to the wake effect mode and the characteristics, and determining the influence area and the scale.
4. The method of claim 2, wherein the method further comprises:
Performing wind farm numerical simulation according to the periodic state characteristics to obtain simulation influence data of the wake effect;
And according to the simulation influence data and the actual wind farm numerical value, the simulation influence data of the wake effect is mapped and corrected, and the influence area and the scale of the wake effect are obtained.
5. The method of claim 1, wherein yaw mapping the area of influence and the scale to determine a yaw angle of the target wind turbine based on the wake influence-yaw mapping model comprises:
Establishing the wake flow influence-yaw mapping model, and acquiring a yaw angle set by training an influence area and scale records of wake flow influence;
And performing yaw mapping on the influence area and the scale through the wake influence-yaw mapping model through the yaw angle set, and determining the yaw angle of the target wind driven generator.
6. The method of claim 1, wherein obtaining the adjusted influence of the wake effect by optimizing airflow distribution comprises:
Monitoring the running state set and the wind farm performance of the target wind driven generator through an active control center, optimizing air flow distribution, and obtaining an air flow distribution result;
and monitoring and acquiring the adjustment influence of the wake effect according to the airflow distribution result.
7. The method of claim 1, wherein feedback controlling the yaw angle based on the adjustment effect of the wake effect, obtaining an optimal control strategy, performing active wake control, comprises:
When the adjustment influence of the wake effect does not meet the wake influence threshold, performing feedback control on the yaw angle to obtain the optimal control strategy;
and performing active wake flow control according to the optimized control strategy.
8. An active wake control system based on active yaw, the system comprising:
the monitoring module monitors and acquires the running state set of the target wind driven generator in real time through a sensor;
The running state set analysis module is used for analyzing the running state set and identifying the influence area and the scale of the wake effect;
The yaw angle determining module is used for performing yaw mapping on the influence area and the scale according to the wake flow influence-yaw mapping model and determining the yaw angle of the target wind driven generator;
The adjustment influence acquisition module is used for acquiring the adjustment influence of the wake effect by optimizing the airflow distribution;
and the wake flow control module is used for carrying out feedback control on the yaw angle according to the adjustment influence of the wake flow effect, obtaining an optimal control strategy and carrying out active wake flow control.
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