Intelligent control system capable of adjusting rotating speed of wind driven generator

Abstract

The invention relates to the technical field of rotating speed control, in particular to an intelligent control system capable of adjusting the rotating speed of a wind driven generator. Acquiring operation data of each wind generator; generating a regional wind field difference model, wherein the regional wind field difference model reflects the dynamic distribution condition of wind resources of each region; the generator cooperative control demand generation module is used for calculating an optimal adjustment strategy of the generator operation data by applying a nonlinear control algorithm based on the regional wind field difference model and the generator operation data, and generating a cooperative control demand signal between generators; based on the generated cooperative control demand signal between the generators, distributed control over the rotating speed, the blade angle and the yaw angle is achieved between the multiple generators in different wind speed areas, the optimal adjustment strategy of the generators is calculated, and it is ensured that all the generators can operate cooperatively and synchronously under the condition that the wind speed difference of the different areas is large.

Description

Technical Field

[0001] The invention relates to the technical field of rotation speed control, and in particular to an intelligent control system capable of adjusting the rotation speed of a wind turbine generator. Background Art

[0002] With the increasing demand for renewable energy, wind power generation has become an indispensable part of the energy sector. However, in practical applications, wind power generation faces many challenges, especially in asynchronous wind farms. Due to the significant differences in wind speed and wind direction in different regions, the operating status of wind turbines cannot be synchronized, affecting the power generation efficiency of the entire wind farm and the operating life of the equipment.

[0003] In large-scale wind farms, wind turbines are usually distributed in different wind conditions, and the wind speed and direction may be inconsistent. How to maximize the power generation efficiency of the entire wind farm by coordinating the speed control of multiple generators in such an asynchronous wind farm, and how to avoid system instability caused by the asynchronous speed of each generator set are key issues that require in-depth research.

[0004] In the prior art, although some centralized control systems are used to adjust the speed and blade angle of wind turbines, most of these systems rely on unified environmental parameters and fail to fully consider the differences in wind speed and direction in different areas of an asynchronous wind farm. This approach not only reduces the power generation efficiency, but may also cause the speed of wind turbines to be out of sync, further exacerbating mechanical wear and energy loss. In addition, the existing control algorithms have a slow response speed when dealing with complex wind conditions and are unable to adapt to dynamic changes in the wind farm in real time. Summary of the invention

[0005] The invention provides an intelligent control system capable of adjusting the rotation speed of a wind generator.

[0006] The intelligent control system capable of adjusting the speed of wind turbine generator includes:

[0007] The wind farm environment monitoring module monitors the wind speed and direction of the entire asynchronous wind farm in real time and generates wind farm environment data;

[0008] Generator speed detection module, used to collect the real-time speed, blade angle, and yaw angle of each wind turbine and generate generator operation data;

[0009] Regional wind farm difference analysis module: Based on wind farm environmental data (wind speed, wind direction), analyze the wind speed and wind direction differences in different areas of the asynchronous wind farm, and generate a regional wind farm difference model. The regional wind farm difference model reflects the dynamic distribution of wind resources in each region and guides the speed adjustment of generators in different regions.

[0010] Generator collaborative control demand generation module: Based on the regional wind field difference model and generator operation data (speed, blade angle and yaw angle), considering the geographical distribution distance between generators, nonlinear control algorithm is applied to calculate the optimal adjustment strategy of generator operation data, and the collaborative control demand signal between generators is generated to ensure the maximum power generation efficiency of each generator under asynchronous wind conditions;

[0011] Distributed multi-machine group control module: Based on the generated collaborative control demand signal between generators, distributed control of speed, blade angle and yaw angle is realized among multiple generators in different wind speed areas. Coordinated control between generators is achieved by controlling their respective blade angles, yaw angles and speeds, thereby reducing the problem of generator speed asynchrony caused by differences in wind speed and direction in the wind farm.

[0012] Optionally, the wind farm environment monitoring module includes a distributed meteorological sensor network, which is distributed in different areas of the asynchronous wind farm. Each meteorological sensor collects wind speed and wind direction data of the area in real time, and transmits the data to a central control unit via wireless communication. The central control unit performs standardized preprocessing based on meteorological data of multiple areas to form wind farm environment data.

[0013] Optionally, the generator speed detection module includes a multi-sensor combination installed on each wind turbine, including a speed sensor, a blade angle sensor and a yaw angle sensor. The speed sensor detects the speed data of the generator in real time, the blade angle sensor monitors the angle change of each blade, and the yaw angle sensor is used to measure the yaw angle of the generator relative to the wind direction. The generator controller integrates the data from each sensor to generate generator operation data.

[0014] Optionally, the regional wind field difference analysis module uses the spatiotemporal data analysis method to model the changes in wind speed and wind direction in different regions based on the wind speed and wind direction data collected by the wind field environment monitoring module, and generates a regional wind field difference model. The regional wind field difference model reflects the wind resource distribution characteristics of each region by analyzing the wind speed gradient, wind direction change trend and dynamic changes over time in each region, and is dynamically updated in the central control unit to guide wind turbines in different regions to coordinate speed adjustments according to differences in wind speed and wind direction, so as to improve power generation efficiency and reduce the impact of uneven wind power between regions.

[0015] Optionally, the spatiotemporal data analysis method includes using Kriging interpolation to construct a spatial distribution model of the wind field, and combining it with time series analysis to obtain a regional wind field difference model;

[0016] The Kriging interpolation method is used to perform spatial interpolation of wind speed and wind direction in different areas of the asynchronous wind farm to generate a spatial wind speed distribution model, thereby analyzing the differences between regions;

[0017] Based on the Kriging interpolation processing results, using the known wind speed and wind direction values ​​in all regions, a spatial distribution model of wind speed and wind direction is generated to calculate the differences in wind speed and wind direction between regions;

[0018] Based on the calculation of the wind speed and wind direction differences between the regions, a regional wind field difference model M is generated. 风场差异 .

[0019] Optionally, the regional wind field difference model M 风场差异 It is expressed as:

[0020] M 风场差异 ={(ΔV A-B ,Δθ A-B )|A,B∈wind farm area}, where ΔV A-B represents the wind speed difference between regions A and B, Δθ A-B Represents the difference in wind direction between areas A and B, where A and B represent any two areas.

[0021] Optionally, the generator collaborative control demand generation module specifically includes:

[0022] Input layer submodule: input current wind speed difference ΔV A-B , wind direction difference Δθ A-B and the speed n of each generator i , blade angle β i , yaw angle γ i ;

[0023] Introducing a nonlinear objective function, expressed as:

[0024] Where J represents the objective function, which reflects the deviation between the actual operating state of the current generator group and the optimal operating state.

[0025] f(n i ,β i ,γ i ) is the output power function of the i-th generator, based on the speed n i , blade angle β i , yaw angle γ i Calculate, f opt (V i ,θ i ) indicates that according to the wind speed V i and wind direction θ i The best output power;

[0026] The constraints are:

[0027] a: The changes in speed, blade angle and yaw angle should comply with mechanical and electrical constraints: nmin ≤n i ≤n max ,β min ≤β i ≤β max ,γ min ≤γ i ≤γ max ;

[0028] b: The speed difference between the generators is controlled within the allowable standard range to ensure the synchronization of coordinated control:

[0029] Output layer submodule: Calculate the optimal speed of each generator through fuzzy logic control optimization algorithm Blade angle and yaw angle To ensure that each generator operates synchronously in an asynchronous wind farm and generate a coordinated control demand signal between generators to adjust the operating parameters of the generators and maximize the power generation efficiency.

[0030] Optionally, the output layer submodule calculates the optimal speed of each generator through a fuzzy logic control optimization algorithm. Blade angle and yaw angle Specifically include:

[0031] Define input and output variables:

[0032] The input variables include:

[0033] Wind speed difference ΔV A-B : represents the wind speed difference between areas A and B;

[0034] Wind direction difference Δθ A-B : Indicates the difference in wind direction between areas A and B;

[0035] Generator speed error Δn i =n ref -n i : represents the current speed of generator i and the reference speed n ref differences;

[0036] Blade angle error Δβ i =β ref -β i : represents the current blade angle of generator i and the reference blade angle β ref differences;

[0037] Yaw angle error Δγ i =γ ref -γ i: represents the current yaw angle of generator i and the reference yaw angle γ ref differences;

[0038] Output variables include:

[0039] Optimum speed The optimal speed of generator i;

[0040] Optimal blade angle The optimal blade angle of generator i;

[0041] Optimal yaw angle The optimal yaw angle of generator i;

[0042] Fuzzify input variables: Fuzzify input variables and map them to fuzzy sets. Each fuzzy set is represented by a linguistic variable. Each variable has a corresponding membership function, which represents the degree of membership of the variable in each fuzzy set. For example, the membership function of wind speed difference can be defined as a triangular or trapezoidal function to represent the fuzzy range of different wind speed differences.

[0043] Construct a fuzzy rule base: According to the wind speed, wind direction difference and the current operating status of the generator, a set of fuzzy rules is constructed to guide how to adjust the speed, blade angle and yaw angle of the generator. Each rule determines the adjustment direction and amplitude of the output (speed, blade angle, yaw angle) based on the current wind speed, wind direction and generator operating status;

[0044] Fuzzy reasoning: The membership of the input variable is combined with the fuzzy rules through the fuzzy reasoning mechanism to obtain the fuzzy value of the output variable. The reasoning method adopts Mamdani reasoning;

[0045] Defuzzify output variables: Defuzzify the result of fuzzy reasoning, that is, the fuzzy output value, to obtain specific control output, including the optimal speed. Blade angle and yaw angle The defuzzification method adopts the centroid method;

[0046] Generate collaborative control demand signals: Generate collaborative control demand signals based on the optimal speed, blade angle, and yaw angle of each generator to guide the synchronous operation of the generators. The collaborative control demand signals include the following information:

[0047] Optimum speed Ensure that the generator i is adjusted to the optimal speed according to the current wind speed;

[0048] Optimal blade angle Ensure that the blade angle of the generator is adjusted to the optimal state to maximize wind energy capture;

[0049] Optimal yaw angle Make sure the generator is facing the wind direction to improve power generation efficiency.

[0050] Optionally, the Mamdani reasoning:

[0051] For each rule, the minimum value of the input membership calculation result and the membership in the rule is calculated, that is, the membership in the input variable that best matches the rule is selected as the output membership of the rule;

[0052] The maximum value of the output membership of all rules is calculated, that is, the one with the largest membership among multiple rules is selected as the final output membership.

[0053] Optionally, the centroid method defuzzification is expressed as: Among them, u * is the defuzzified output value, including the optimal speed Optimal blade angle Optimal yaw angle u i For each possible value of the fuzzy output, μ i Indicates the membership degree corresponding to each output value. Through this method, the fuzzy output result is converted into a specific value to adjust the operating parameters of the generator.

[0054] Beneficial effects of the present invention:

[0055] The present invention, through a wind farm environment monitoring module and a regional wind farm difference analysis module, based on the Kriging interpolation method and spatiotemporal data analysis, accurately analyzes the differences in wind speed and wind direction in different areas of an asynchronous wind farm, and generates a dynamic regional wind farm difference model. The model can reflect the dynamic distribution of wind resources in real time, provide accurate environmental data input for the operating status of each generator, and ensure accurate matching of the operating status of the generator in different areas of the asynchronous wind farm with the actual environment through detailed modeling of the wind speed and wind direction differences, thereby avoiding the loss of power generation efficiency caused by ignoring the regional wind speed differences in the prior art.

[0056] The present invention introduces a nonlinear control algorithm, combines the generated wind field difference model with the real-time operating data of the generator (including the rotational speed, blade angle and yaw angle), calculates the optimal adjustment strategy of the generator, and ensures that when the wind speed in different areas varies greatly, the generators can operate synchronously and collaboratively. Through the fuzzy logic control method, the rotational speed, blade angle and yaw angle of each generator are intelligently adjusted to maintain the rotational speed difference between the generators within the allowable range. Compared with traditional control methods, the present invention exhibits higher response speed and control accuracy when dealing with complex wind conditions and wind speed fluctuations, and effectively improves the power generation efficiency of the entire wind field. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A schematic diagram of system logic of an embodiment of the present invention;

[0059] Figure 2 Schematic diagram of system function modules according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0061] like Figure 1-Figure 2 As shown, the intelligent control system capable of adjusting the speed of a wind turbine generator comprises:

[0062] The wind farm environment monitoring module monitors the wind speed and direction of the entire asynchronous wind farm in real time and generates wind farm environment data;

[0063] Generator speed detection module, used to collect the real-time speed, blade angle, and yaw angle of each wind turbine and generate generator operation data;

[0064] Regional wind farm difference analysis module: Based on wind farm environmental data (wind speed, wind direction), analyze the wind speed and wind direction differences in different areas of the asynchronous wind farm, and generate a regional wind farm difference model. The regional wind farm difference model reflects the dynamic distribution of wind resources in each region and guides the speed adjustment of generators in different regions.

[0065] Generator collaborative control demand generation module: Based on the regional wind field difference model and generator operation data (speed, blade angle and yaw angle), considering the geographical distribution distance between generators, nonlinear control algorithm is applied to calculate the optimal adjustment strategy of generator operation data, and the collaborative control demand signal between generators is generated to ensure the maximum power generation efficiency of each generator under asynchronous wind conditions;

[0066] Distributed multi-machine group control module: Based on the generated collaborative control demand signal between generators, distributed control of speed, blade angle and yaw angle is realized among multiple generators in different wind speed areas. Coordinated control between generators is achieved by controlling their respective blade angles, yaw angles and speeds, thereby reducing the problem of generator speed asynchrony caused by differences in wind speed and direction in the wind farm.

[0067] The wind farm environment monitoring module includes a distributed meteorological sensor network, which is distributed in different areas of the asynchronous wind farm. Each meteorological sensor collects the wind speed and wind direction data of the area in real time, and transmits the data to the central control unit through wireless communication. The central control unit performs standardized preprocessing based on the meteorological data of multiple areas to form wind farm environment data.

[0068] The generator speed detection module includes a multi-sensor combination installed on each wind turbine, including a speed sensor, a blade angle sensor and a yaw angle sensor. The speed sensor detects the speed data of the generator in real time, the blade angle sensor monitors the angle change of each blade, and the yaw angle sensor is used to measure the yaw angle of the generator relative to the wind direction. The generator controller integrates the data from each sensor to generate generator operation data.

[0069] The regional wind field difference analysis module is based on the wind speed and wind direction data collected by the wind field environment monitoring module. It uses the spatiotemporal data analysis method to model the changes in wind speed and wind direction in different regions and generate a regional wind field difference model. The regional wind field difference model reflects the wind resource distribution characteristics of each region by analyzing the wind speed gradient, wind direction change trend and dynamic changes over time in each region, and is dynamically updated in the central control unit to guide wind turbines in different regions to coordinate speed adjustments according to the differences in wind speed and wind direction, so as to improve power generation efficiency and reduce the impact of uneven wind power between regions.

[0070] The spatiotemporal data analysis method includes using Kriging interpolation to construct a spatial distribution model of the wind field, and combining it with time series analysis to obtain a regional wind field difference model;

[0071] The Kriging interpolation method is used to spatially interpolate the wind speed and wind direction in different areas of the asynchronous wind farm to generate a spatial wind speed distribution model, thereby analyzing the differences between regions;

[0072] The Kriging interpolation method is expressed as: Among them, x i is the wind speed or wind direction value of a known point in the wind field, x0 represents the location of the wind speed or wind direction value to be predicted, It represents the wind speed or wind direction value at the predicted point x0, which is used as the representative wind speed or wind direction of a certain area in the regional wind field difference model. i ) represents a known point x iThe wind speed or wind direction value at the location comes from the wind speed and wind direction data in the wind farm environment monitoring module, λ i is the weighting coefficient, which indicates the weight of the predicted point x0 relative to each known point, satisfying

[0073] In order to determine the weighting factor λ i , the spatial correlation between wind speed and wind direction at different locations is calculated by semivariogram, which is expressed as:

[0074] Among them, γ(h) is the semivariogram, which indicates the degree of difference in wind speed or wind direction between two points with a distance of h, N(h) is the number of point pairs with a distance of h, and Z(x i ) and Z(x i +h) are respectively the position x i and point x at distance h i The wind speed or wind direction value at +h. The semivariogram describes the similarity of wind speed and wind direction at different locations. The smaller the value, the smaller the difference in wind speed and wind direction between two points h apart.

[0075] Based on the Kriging interpolation results, the known wind speed and wind direction values ​​in all regions are used to generate a spatial distribution model of wind speed and wind direction, and calculate the differences in wind speed and wind direction between regions;

[0076] The difference in wind speed and direction between any two areas A and B can be calculated using the following expression:

[0077] Among them, ΔV represents the wind speed gradient between regions, ΔV A-B represents the difference in wind speed between areas A and B, and represents the predicted wind speed of areas A and B after Kriging interpolation. Similarly, the calculation of wind direction difference can be performed by the following expression:

[0078] Among them, Δθ represents the wind direction change rate between regions, Δθ A-B represents the difference in wind direction between areas A and B, and represents the predicted wind direction of areas A and B obtained after Kriging interpolation;

[0079] Based on the calculation of wind speed and wind direction differences between regions, a regional wind field difference model M is generated. 风场差异 .

[0080] Regional wind field difference model M 风场差异 It is expressed as:

[0081] M 风场差异 ={(ΔV A-B,Δθ A-B )|A,B∈wind farm area}, where ΔV A-B represents the wind speed difference between regions A and B, Δθ A-B It represents the wind direction difference between areas A and B. A and B represent any two areas. This model can be updated dynamically. As the wind speed and wind direction data collected in real time by the wind farm environment monitoring module change, the regional wind speed difference and wind direction difference will also be adjusted accordingly, reflecting the dynamic wind resource distribution in the asynchronous wind farm.

[0082] In order to further capture the wind speed gradient and wind direction change trend within the wind farm, the wind speed change between regions is expressed by the following formula: Where V(A) and V(B) are the wind speeds in regions A and B, respectively. A-B is the physical distance between areas A and B;

[0083] Similarly, the wind direction change trend can be expressed as: By calculating the wind speed gradient and wind direction change rate, the regional wind field difference model can be further refined to provide a more accurate reference for the speed adjustment of generators in different regions.

[0084] The generator collaborative control demand generation module specifically includes:

[0085] Input layer submodule: input current wind speed difference ΔV A-B , wind direction difference Δθ A-B and the speed n of each generator i , blade angle β i , yaw angle γ i ;

[0086] Introducing a nonlinear objective function, expressed as:

[0087] Where J represents the objective function, which reflects the deviation between the actual operating state of the current generator group and the optimal operating state.

[0088] f(n i ,β i ,γ i ) is the output power function of the i-th generator, based on the speed n i , blade angle β i , yaw angle γ i Calculate, f opt (V i ,θ i ) indicates that according to the wind speed V i and wind direction θ i The best output power;

[0089] The constraints are:

[0090] a: The changes in speed, blade angle and yaw angle should comply with mechanical and electrical constraints: n min ≤n i ≤n max ,β min ≤β i ≤β max ,γ min ≤γ i ≤γ max ;

[0091] b: The speed difference between the generators is controlled within the allowable standard range to ensure the synchronization of coordinated control:

[0092] Output layer submodule: Calculate the optimal speed of each generator through fuzzy logic control optimization algorithm Blade angle and yaw angle To ensure that each generator operates synchronously in an asynchronous wind farm and generate a coordinated control demand signal between generators to adjust the operating parameters of the generators and maximize the power generation efficiency.

[0093] Electrical restrictions are mainly related to the electrical performance of the generator, power output characteristics and connection requirements to the grid, including the maximum output power limit of the generator: the output power of a wind turbine is directly related to the rotational speed and wind speed. To avoid exceeding the design power of the generator, the rotational speed must be kept within a safe range to avoid damage to electrical components. The maximum output power of a wind turbine is between 1.5MW and 5MW, depending on the size and design of the unit.

[0094] Mechanical constraints are set based on the physical structure and operating characteristics of the wind turbine, and mainly involve the following aspects:

[0095] Speed ​​limit min ≤n i ≤n max : The speed limit of the generator is determined by the design of the generator and the maximum bearing capacity of the blades. Too high a speed will lead to increased mechanical losses and even structural damage to the blades; too low a speed will affect the power generation efficiency. The mechanical speed limit is between 6RPM and 20RPM, depending on the design of the generator. 6RPM is the minimum limit for the generator to start generating electricity effectively, and about 20RPM is the maximum speed of the generator within the safe operating range.

[0096] Blade angle limit β min ≤β i ≤β max: The range of blade angles is set by the rotating mechanism of the wind turbine blades. Usually, the blade angle can be adjusted between 0 degrees (parallel to the wind direction, maximizing wind energy capture) and around 30 degrees (to reduce wind pressure). 0 degrees is the angle that maximizes wind energy capture, and 30 degrees is an angle to avoid protecting the generator in strong or extreme wind conditions.

[0097] Yaw Angle Limitγ min ≤γ i ≤γ max : The yaw angle refers to the rotation angle of the generator's nacelle relative to the wind direction. The yaw angle can rotate within the range of -90 degrees to +90 degrees and is used to adjust the orientation of the generator to better capture wind energy.

[0098] In addition, in order to ensure the coordinated operation of generators in asynchronous wind farms, the speed differences between generators must be limited. Too large speed differences between generators will lead to inconsistent output power of different generator sets, affecting the overall efficiency of the wind farm and possibly affecting the stability of the power grid. The maximum allowable speed difference is between 1RPM and 3RPM, which is sufficient to cope with changes in wind speed and wind direction in asynchronous wind farms, while ensuring that the output power of the generator is within the stability range of the power grid.

[0099] The output layer submodule calculates the optimal speed of each generator through the fuzzy logic control optimization algorithm Blade angle and yaw angle Specifically include:

[0100] Define input and output variables:

[0101] The input variables include:

[0102] Wind speed difference ΔV A-B : represents the wind speed difference between areas A and B;

[0103] Wind direction difference Δθ A-B : Indicates the difference in wind direction between areas A and B;

[0104] Generator speed error Δn i =n ref -n i : represents the current speed of generator i and the reference speed n ref differences;

[0105] Blade angle error Δβ i =β ref -β i : represents the current blade angle of generator i and the reference blade angle β ref differences;

[0106] Yaw angle error Δγi =γ ref -γ i : represents the current yaw angle of generator i and the reference yaw angle γ ref differences;

[0107] Output variables include:

[0108] Optimum speed The optimal speed of generator i;

[0109] Optimal blade angle The optimal blade angle of generator i;

[0110] Optimal yaw angle The optimal yaw angle of generator i;

[0111] Fuzzify input variables: Fuzzify input variables and map them to fuzzy sets. Each fuzzy set is represented by a linguistic variable. Each variable has a corresponding membership function, which represents the degree of membership of the variable in each fuzzy set. For example, the membership function of wind speed difference can be defined as a triangular or trapezoidal function to represent the fuzzy range of different wind speed differences, as follows:

[0112] Wind speed difference ΔV A-B Divided into "low difference", "medium difference" and "high difference";

[0113] Wind direction difference Δθ A-B Divided into "small change", "moderate change", and "dramatic change";

[0114] Speed ​​error Δn i Divided into "low speed", "normal speed" and "high speed";

[0115] Blade angle error Δβ i Divided into "small angle", "normal angle" and "large angle";

[0116] Yaw angle error Δγ i It is divided into "insufficient yaw", "normal yaw" and "excessive yaw".

[0117] Construct a fuzzy rule base: According to the wind speed, wind direction difference and the current operating status of the generator, construct a set of fuzzy rules to guide how to adjust the speed, blade angle and yaw angle of the generator. Each rule determines the adjustment direction and amplitude of the output (speed, blade angle, yaw angle) based on the current wind speed, wind direction and generator operating status. For example:

[0118] Rule 1: If *Wind Speed ​​Difference* is *High Difference* and *Speed ​​Error* is *Speed ​​is too low, then *increase the speed.

[0119] Rule 2: If the wind direction difference is changing dramatically and the yaw angle error is too large, then reduce the yaw angle.

[0120] Rule 3: If the wind speed difference is medium and the blade angle error is small, increase the blade angle.

[0121] Fuzzy reasoning: The membership of the input variable is combined with the fuzzy rules through the fuzzy reasoning mechanism to obtain the fuzzy value of the output variable. The reasoning method adopts Mamdani reasoning;

[0122] Defuzzify output variables: Defuzzify the result of fuzzy reasoning, that is, the fuzzy output value, to obtain specific control output, including the optimal speed. Blade angle and yaw angle The defuzzification method adopts the centroid method;

[0123] Generate collaborative control demand signals: Generate collaborative control demand signals based on the optimal speed, blade angle, and yaw angle of each generator to guide the synchronous operation of the generators. The collaborative control demand signals include the following information:

[0124] Optimum speed Ensure that the generator i is adjusted to the optimal speed according to the current wind speed;

[0125] Optimal blade angle Ensure that the blade angle of the generator is adjusted to the optimal state to maximize wind energy capture;

[0126] Optimal yaw angle Make sure the generator is facing the wind direction to improve power generation efficiency.

[0127] Mamdani reasoning:

[0128] For each rule, the minimum value of the input membership calculation result and the membership in the rule is calculated, that is, the membership in the input variable that best matches the rule is selected as the output membership of the rule;

[0129] The maximum value of the output membership of all rules is calculated, that is, the one with the largest membership among multiple rules is selected as the final output membership.

[0130] The centroid defuzzification is expressed as: Among them, u * is the defuzzified output value, including the optimal speed Optimal blade angle Optimal yaw angle u i For each possible value of the fuzzy output, μ iIndicates the membership degree corresponding to each output value. Through this method, the fuzzy output result is converted into a specific value to adjust the operating parameters of the generator.

[0131] Based on the generated inter-generator coordinated control demand signal, the distributed multi-machine group control module implements the distributed control of the speed, blade angle and yaw angle of each generator in different wind speed areas through the following steps to ensure coordinated control between generators, as follows:

[0132] The generated inter-generator collaborative control demand signal is transmitted to each generator controller through the communication network, and each generator receives a control instruction containing its optimal speed, optimal blade angle and optimal yaw angle.

[0133] Speed ​​control: Each generator gradually adjusts its speed according to the speed control command it receives by adjusting the generator's pitch system or generator current, so that its speed remains within the allowable difference range with other generators to ensure synchronous operation of generators in different wind speed areas.

[0134] Blade angle control: Each generator adjusts the windward angle of the blades according to the received blade angle control instructions to optimize the wind energy capture efficiency. The blade angle adjustment is achieved through an electric or hydraulic control system to ensure that generators in different wind speed areas can flexibly adjust the blade angle according to the wind speed difference to avoid mechanical damage when the wind is too strong.

[0135] Yaw angle control: Each generator adjusts the orientation of the generator cabin through the yaw system according to the yaw angle control command it receives, so that it is consistent with the wind direction. The yaw system automatically detects changes in wind direction and combines the yaw angle commands between generators to ensure that the generators in different areas face their respective optimal wind directions, thereby improving power generation efficiency.

[0136] Distributed feedback regulation: Through the status sensors of each generator (including speed sensors, blade angle sensors and yaw angle sensors), the current status data of the generator is fed back to the central control unit in real time. The central control unit performs dynamic evaluation based on the feedback data and sends adjusted control signals to each generator to achieve distributed real-time optimization and coordinated control.

[0137] Each generator is gradually adjusted to the optimal operating state through distributed speed, blade angle and yaw angle control in different wind speed areas, ensuring synchronous control between generators, reducing operating inconsistencies caused by differences in wind speed and direction, and thereby improving the power generation efficiency of the entire wind farm.

[0138] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0139] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent control system capable of adjusting the speed of a wind turbine generator, characterized in that: include: The wind farm environment monitoring module monitors the wind speed and direction of the entire asynchronous wind farm in real time and generates wind farm environment data; Generator speed detection module, used to collect the real-time speed, blade angle, and yaw angle of each wind turbine and generate generator operation data; Regional wind field difference analysis module: Based on the wind field environment data, the wind speed and wind direction differences in different areas of the asynchronous wind field are analyzed to generate a regional wind field difference model, which reflects the dynamic distribution of wind resources in each region; Generator collaborative control demand generation module: Based on the regional wind field difference model and generator operation data, the nonlinear control algorithm is applied to calculate the optimal adjustment strategy of the generator operation data and generate the collaborative control demand signal between generators; Distributed multi-machine group control module: Based on the generated coordinated control demand signal between generators, distributed control of speed, blade angle and yaw angle is realized among multiple generators in different wind speed areas, and coordinated control between generators is achieved by controlling their respective blade angles, yaw angles and speeds.

2. The intelligent control system capable of adjusting the speed of a wind turbine generator according to claim 1 is characterized in that: The wind farm environment monitoring module includes a distributed meteorological sensor network, which is distributed in different areas of the asynchronous wind farm. Each meteorological sensor collects wind speed and wind direction data in the area in real time, and transmits the data to a central control unit via wireless communication. The central control unit performs standardized preprocessing based on meteorological data from multiple areas to form wind farm environment data.

3. The intelligent control system capable of adjusting the speed of a wind turbine generator according to claim 1 is characterized in that: The generator speed detection module includes a multi-sensor combination installed on each wind turbine, including a speed sensor, a blade angle sensor and a yaw angle sensor. The speed sensor detects the speed data of the generator in real time, the blade angle sensor monitors the angle change of each blade, and the yaw angle sensor is used to measure the yaw angle of the generator relative to the wind direction. The generator controller integrates the data from each sensor to generate generator operation data.

4. The intelligent control system capable of adjusting the speed of a wind turbine generator according to claim 1 is characterized in that: The regional wind field difference analysis module is based on the wind speed and wind direction data collected by the wind field environment monitoring module, and uses the spatiotemporal data analysis method to model the wind speed and wind direction changes in different regions to generate a regional wind field difference model. The regional wind field difference model reflects the wind resource distribution characteristics of each region by analyzing the wind speed gradient, wind direction change trend and dynamic changes over time in each region, and is dynamically updated in the central control unit.

5. The intelligent control system capable of adjusting the speed of a wind turbine generator according to claim 4 is characterized in that: The spatiotemporal data analysis method includes using Kriging interpolation to construct a spatial distribution model of the wind field, and combining it with time series analysis to obtain a regional wind field difference model; The Kriging interpolation method is used to perform spatial interpolation of wind speed and wind direction in different areas of the asynchronous wind farm to generate a spatial wind speed distribution model, thereby analyzing the differences between regions; Based on the Kriging interpolation processing results, using the known wind speed and wind direction values ​​in all regions, a spatial distribution model of wind speed and wind direction is generated to calculate the differences in wind speed and wind direction between regions; Based on the calculation of the wind speed and wind direction differences between the regions, a regional wind field difference model M is generated. 风场差异 .

6. The intelligent control system capable of adjusting the speed of a wind turbine generator according to claim 5 is characterized in that: The regional wind field difference model M 风场差异 It is expressed as: M 风场差异 ={(ΔV A-B , Δθ A-B )|A, B∈wind farm area}, where ΔV A-B represents the wind speed difference between regions A and B, Δθ A-B Represents the difference in wind direction between areas A and B, where A and B represent any two areas.

7. The intelligent control system capable of adjusting the speed of a wind turbine generator according to claim 5, characterized in that: The generator coordinated control demand generation module specifically includes: Input layer submodule: input current wind speed difference ΔV A-B , wind direction difference Δθ A-B and the speed n of each generator i , blade angle β i , yaw angle γ i ; Introducing a nonlinear objective function, expressed as: Where J represents the objective function, which reflects the deviation between the actual operating state of the current generator group and the optimal operating state, f(n i , β i , γ i ) is the output power function of the i-th generator, based on the speed n i , blade angle β i , yaw angle γ i Calculate, f opt (V i ,θ i ) indicates that according to the wind speed V i and wind direction θ i The best output power; The constraints are: a: The changes in speed, blade angle and yaw angle should comply with mechanical and electrical constraints: n min ≤n i ≤n max , β min ≤β i ≤β max , γ min ≤γ i ≤γ max ; b: The speed difference between the generators is controlled within the allowable standard range to ensure the synchronization of coordinated control: Output layer submodule: Calculate the optimal speed of each generator through fuzzy logic control optimization algorithm Blade angle and yaw angle To ensure that each generator operates synchronously in an asynchronous wind farm and generate a coordinated control demand signal between generators.

8. The intelligent control system capable of adjusting the speed of a wind turbine generator according to claim 7 is characterized in that: The output layer submodule calculates the optimal speed of each generator through the fuzzy logic control optimization algorithm Blade angle and yaw angle Specifically include: Define input variables and output variables: Input variables include wind speed difference ΔV A-B , wind direction difference Δθ A-B , generator speed error Δn i , blade angle error Δβ i And the yaw angle error Δγ i ; Output variables include optimal speed Optimal blade angle And the optimal yaw angle Fuzzify input variables: Fuzzify input variables and map them to fuzzy sets. Each fuzzy set is represented by a linguistic variable. Each variable has a corresponding membership function, which represents the degree of membership of the variable in each fuzzy set. Construct a fuzzy rule base: Based on the wind speed, wind direction differences and the current operating status of the generator, a set of fuzzy rules is constructed to guide how to adjust the generator's speed, blade angle and yaw angle. Each rule determines the output adjustment direction and amplitude based on the current wind speed, wind direction and generator operating status; Fuzzy reasoning: The membership of the input variable is combined with the fuzzy rules through the fuzzy reasoning mechanism to obtain the fuzzy value of the output variable. The reasoning method adopts Mamdan i reasoning; Defuzzify output variables: Defuzzify the result of fuzzy reasoning, that is, the fuzzy output value, to obtain specific control output, including the optimal speed. Blade angle and yaw angle The defuzzification method adopts the centroid method; Generate collaborative control demand signals: Generate collaborative control demand signals based on the optimal speed, blade angle and yaw angle of each generator to guide the synchronous operation of the generators.

9. The intelligent control system capable of adjusting the speed of a wind turbine generator according to claim 8, characterized in that: The Mamdan i reasoning: For each rule, the minimum value of the input membership calculation result and the membership in the rule is calculated, that is, the membership in the input variable that best matches the rule is selected as the output membership of the rule; The maximum value of the output membership of all rules is calculated, that is, the one with the largest membership among multiple rules is selected as the final output membership.

10. The intelligent control system capable of adjusting the speed of a wind turbine generator according to claim 8, characterized in that: The centroid defuzzification is expressed as: Among them, u * is the defuzzified output value, including the optimal speed Optimal blade angle Optimal yaw angle u i For each possible value of the fuzzy output, μ i Indicates the degree of membership corresponding to each output value.

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