Wind power plant control optimization method and system considering load balance

Abstract

The invention belongs to the field of offshore high-power wind power plants, and provides a wind power plant control optimization method and system considering load balance. When a wind power plant does not participate in primary frequency modulation, a multi-target optimization control strategy is adopted, and the maximum power generation total power, the minimum fan load and the uniform load are taken as control targets; optimization is realized by optimizing an axial induction factor; maintaining the control target of maximizing the total power generation power, and storing energy or / and unloading the wind power plant to enable the power generation power of the wind power plant to track the power grid demand; and in the two optimization processes, the influence of turbulent flow on the wake flow diffusion trajectory is considered in the distribution of the load. According to the method, the power generation capacity of the wind power plant can be enhanced, the overall load and load difference of the unit are reduced, the fan maintenance cost and the cost per kilowatt hour are reduced, and the problems of high cost and poor optimization effect in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the field of offshore high-power wind farms, and in particular relates to a wind farm control optimization method and system considering load balancing. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In the operation of offshore wind farms, the control strategy of wind turbines has a key impact on power generation efficiency and equipment life. At present, the operating modes of offshore wind farms are mainly divided into conventional mode and primary frequency regulation mode. In conventional mode, most wind farms adopt the maximum wind energy tracking strategy, which focuses on the maximum power output of a single wind turbine. However, this strategy has obvious limitations. It ignores the aerodynamic coupling effect between wind turbines. Specifically, the upstream wind turbines may over-capture wind energy, causing the downstream units to significantly attenuate due to wind speed. Not only is the power output limited, but the load loss is also increased due to airflow turbulence. In the long run, it will accelerate equipment fatigue, shorten service life and increase maintenance costs.

[0004] The primary frequency regulation mode is an operating mode adopted to meet the grid frequency regulation needs. It mainly responds to the grid power demand based on the proportional allocation method and allocates the frequency regulation tasks according to the rated power or preset ratio of each wind turbine. However, this mode also has shortcomings. It does not fully consider the load conditions of wind turbines during the frequency regulation process, which may cause some units to bear excessive loads, affecting the operational reliability and economy of the entire wind farm.

[0005] The existing scheme focuses on the optimization control of the built wind farm, aiming to improve the overall output power of the wind farm by considering the wake effect. The core method is to deeply study the quantitative relationship between the change of the state parameters of the wind turbine and the output power and the wake distribution. Then the optimization control model is established. The optimization scheme takes maximizing the overall output power of the wind farm as the objective function, selects the axial induction factor as the optimization parameter, and uses the particle swarm algorithm for optimization solution. The axial induction factor is the key variable connecting the wake wind speed and the output power, and its change directly affects the operating state and wake characteristics of the wind turbine. To find the optimal combination of axial induction factors. However, this method only considers the problem of increasing the capture amount under the conventional working mode, and does not consider the optimization process of the wind farm participating in the primary frequency modulation. Moreover, the above method only optimizes on a time section, and does not achieve continuous optimization, so it cannot consider the wake effect under the influence of turbulence. In addition, the above method does not consider the load balance of the wind turbine, resulting in some wind turbines being subjected to excessive loads during the optimization process. The damage rate of these wind turbines will increase, which will lead to a significant difference in the life of the wind turbines, which undoubtedly increases the operation and maintenance costs. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a wind farm control optimization method and system considering load balancing. The present invention constructs a wind farm optimization control that takes into account both wake effect and load loss, strives to enhance the wind farm's power generation capacity, reduce the overall load and load difference of the unit, and reduce the wind turbine maintenance cost and the cost per kilowatt-hour.

[0007] According to some embodiments, the present invention adopts the following technical solutions: A wind farm control optimization method considering load balancing comprises the following steps: When the wind farm does not participate in primary frequency regulation, a multi-objective optimization control strategy is adopted, with the control objectives of maximizing total power generation, minimizing wind turbine load and equalizing load, and the optimization is achieved by optimizing the axial induction factor; When the wind farm participates in primary frequency regulation, the control target of maximizing the total power generation is maintained, and energy storage and / or load unloading are performed on the wind farm so that the power generation of the wind farm tracks the grid demand. At the same time, the total load on each wind turbine and the standard deviation of the load on each wind turbine are adjusted; In both optimization processes, the load distribution takes into account the influence of turbulence on the wake diffusion trajectory.

[0008] As an optional embodiment, the axial induction factor is , wind energy capture coefficient of the wind turbine C P and the tower thrust coefficient of the wind turbine C T for: ; ; Corresponding captured wind power P and tower load The calculation formula is: ; ; in, v 0 is the inflow wind speed, v 1 is the wind speed behind the fan, ρ is the air density, A is the rotor area, axial induction factor .

[0009] As an optional implementation, the process of considering the influence of turbulence on the wake diffusion trajectory in the distribution of load includes: normalizing the turbulence intensity correction term, using a set percentage of turbulence as a standard, obtaining a turbulence intensity normalization factor, and correcting the wake diffusion model.

[0010] As a further implementation method, the wake diffusion model is modified as follows: ; ; in, is the set baseline turbulence intensity value, is the turbulence coefficient, is the normalized weight factor, m and n are the wake basic expansion rate and wake additional expansion rate in the absence of turbulence, respectively. is the radius of the wake after diffusion, is the radius of the wind wheel, is the distance from the upstream wind turbine.

[0011] As an optional implementation method, when the wind farm does not participate in primary frequency regulation, a multi-objective optimization control strategy is adopted, and the non-dominated genetic algorithm II is applied to optimize and solve each optimization objective, and parallel computing is enabled. After the optimization, the Pareto front is extracted and a compromise solution is obtained.

[0012] As an optional implementation, when the wind farm participates in primary frequency regulation, supercapacitors are connected to the connection line between the wind farm and the AC power grid to store energy and / or unload load.

[0013] As a further step, when the total power of the wind farm is greater than the grid dispatching demand, the excess electric energy exceeding the grid dispatching is charged to the supercapacitor; If the supercapacitor is fully charged and the total power of the wind farm is still greater than the grid dispatching demand, the excess power generation will be unloaded using the unloading resistor connected in parallel with the supercapacitor; When the total power of the wind farm is less than the grid dispatching demand, the supercapacitor supplies power to the grid so that the total power of the wind farm tracks the grid dispatching demand.

[0014] A wind farm control optimization system considering load balancing, comprising: The multi-objective optimization module is configured to adopt a multi-objective optimization control strategy when the wind farm does not participate in primary frequency regulation, with the control objectives of maximizing total power generation, minimizing wind turbine load and equalizing load, and achieve optimization by optimizing the axial induction factor; The fast optimization module is configured to maintain the control target of maximizing the total power generation when the wind farm participates in the primary frequency regulation, and to perform energy storage and / or load shedding on the wind farm so that the power generation of the wind farm tracks the demand of the power grid; The load correction module is configured to consider the influence of turbulence on the wake diffusion trajectory in the distribution of loads in both optimization processes.

[0015] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps in the above method are completed.

[0016] An electronic device comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps in the above method are completed.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention optimizes the output power and tower load of a wind farm based on the Jensen wake model and the axial induction factor theory, and can achieve that when the wind farm does not participate in primary frequency modulation, the wind farm power output is maximized by optimizing the axial induction factor, while the wind farm wind turbine tower load and the load standard deviation between wind turbines are reduced as much as possible; when the wind farm participates in primary frequency modulation, the wind farm power generation is made to track the grid instruction by optimizing the axial induction factor and controlling the supercapacitor energy storage unloading circuit, while adjusting the total tower and main shaft loads on each wind turbine and the load standard deviation of each wind turbine.

[0018] The present invention optimizes the load distribution of the wind farm in conventional wind energy capture and primary frequency modulation modes, takes into account the influence of turbulence on the wake diffusion trajectory in the wake effect, optimizes the load distribution in the two wind farm working modes, reduces the total load and makes the load evenly distributed, avoiding the problem that some wind turbines are easily damaged due to excessive load.

[0019] In the conventional mode of the wind farm, the present invention adopts multi-objective optimization for contradictory optimization objectives to make the optimization result more reasonable. In the mode of the wind farm participating in a single frequency modulation, nonlinear programming is used for rapid optimization, which greatly improves the optimization efficiency and also realizes continuous optimization in time.

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0022] Figure 1 is a schematic diagram of a wind turbine flow tube model according to an embodiment; Figure 2 is an embodiment and Variation diagram of axial induction factor; Figure 3 is a wind farm wake shielding diagram of an embodiment; Figure 4 is a wind farm control optimization flow chart of an embodiment; Figure 5is a wake distribution diagram considering turbulence factor in an embodiment, wherein (a) is a wake distribution diagram without considering turbulence, and (b) is a wake distribution diagram after considering turbulence correction; Figure 6 is a schematic diagram of a wind farm supercapacitor energy storage and load unloading circuit according to an embodiment; Figure 7 This is a result diagram under the normal mode of an embodiment; Figure 8 The figure is a result diagram of an embodiment participating in a frequency modulation mode. DETAILED DESCRIPTION

[0023] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0025] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0026] In the absence of conflict, the embodiments in this application and the features in the embodiments may be combined with each other.

[0027] Embodiment 1 As described in the background technology, the current wind farm operation modes mainly include conventional mode and primary frequency regulation mode. In conventional mode, the maximum wind energy tracking strategy is often adopted. This strategy focuses on the maximum power output of a single wind turbine, but ignores the aerodynamic coupling effect between units. This can easily cause the upstream wind turbine to capture too much wind energy, resulting in the downstream unit being greatly attenuated due to wind speed, power output being limited, and load loss increasing. The primary frequency regulation mode often responds to the power demand of the power grid with a proportional allocation method, and also does not consider the load conditions of the wind turbine. Therefore, studying the relationship between the operating status of the wind turbine and the wake distribution can clarify the relationship between power output and load loss. By controlling the operating status of the wind turbine, the wake effect can be effectively weakened, the overall power output of the wind farm can be improved, and the load loss and maintenance cost of the unit can be reduced.

[0028] In order to solve the above problems, this embodiment provides a wind farm control optimization method considering load balancing, which can achieve wind farm optimization control that takes into account both wake effect and load loss, strive to enhance the wind farm's power generation capacity, reduce the overall load and load difference of the unit, and reduce the wind turbine maintenance cost and electricity cost.

[0029] A wind farm control optimization method considering load balancing comprises the following steps: When the wind farm does not participate in primary frequency regulation, a multi-objective optimization control strategy is adopted, with the control objectives of maximizing total power generation, minimizing wind turbine load and equalizing load, and the optimization is achieved by optimizing the axial induction factor; When the wind farm participates in primary frequency regulation, the control target of maximizing the total power generation is maintained, and energy storage and / or load shedding are performed on the wind farm so that the power generation of the wind farm tracks the grid demand; In both optimization processes, the load distribution takes into account the influence of turbulence on the wake diffusion trajectory.

[0030] The specific details of this method are described below.

[0031] like Figure 1 As shown, in the momentum theory of the fan, it is assumed that the axial induction factor is defined as , then the wind energy capture coefficient of the wind turbine and the tower thrust coefficient of the wind turbine are: ; ; The corresponding captured wind power, tower load and main shaft torque are calculated as: ; ; ; Where ρ is the air density, A is the rotor area, h is the height of the wind turbine tower, is the wind wheel speed, corresponding to and Waveform diagram Figure 2 As shown. When the wind energy capture coefficient The maximum value is 0.593 in this embodiment, which is the Betz limit. Therefore, in actual fan control, the limit In this range, the wind energy capture coefficient increases with Increase, but the thrust coefficient It also increases accordingly.

[0032] Regarding how to obtain the corresponding rotation speed ω and pitch angle β of the axial induction factor, this embodiment provides a solution. In addition to being expressed by the axial induction factor, the wind energy capture coefficient can also be expressed by the tip speed ratio and the pitch angle, as shown in the following formula: ; ; After obtaining the axial induction factor, the value of Cp can be obtained, and the pitch angle , after substituting into another formula of Cp, the tip speed ratio λ is calculated using the Newton iteration method, and then the formula can be used Calculate the speed if Greater than rated speed , then let , and then substitute it into the Cp formula to obtain the pitch angle .

[0033] The formula is: ; in, is the initial value, is the coefficient, The wind energy capture coefficient that needs to be solved for the axial induction factor is calculated, It is a function provided in Matlab for solving nonlinear equations.

[0034] like Figure 3 As shown in Figure 1, the Jensen wake model is the most classic semi-empirical wake model. This model assumes that the wake diffuses along both sides of the wind turbine rotor with a fixed diffusion coefficient, the wake radius increases linearly with the downstream distance, and the wind speed is evenly distributed in the wake area. The model calculates the wind speed attenuation after the wind passes through the wind turbine using the following formula: ; in, is the fan rotor radius, is the original wind speed, is the radius of the wake after diffusion, is the radius of the wind wheel, is the wake diffusion coefficient, is the distance from the upstream wind turbine, is the thrust coefficient. This model is only applicable to flat terrain and no turbulence. However, turbulence will enhance the diffusion of the wake, making the growth of the wake radius not only dependent on the distance, but also related to the turbulence intensity.

[0035] Therefore, in the case of high turbulence in offshore wind farms, it is necessary to correct the wake diffusion radius. The proposed correction model first normalizes the turbulence intensity correction term. This embodiment uses 15% turbulence as the standard to obtain the turbulence intensity normalization factor, and then corrects the wake diffusion model. The formula is as follows: ; ; in, is the turbulence coefficient, is the normalized weight factor, is the radius of the wake after diffusion, is the radius of the wind wheel, is the distance from the upstream wind turbine, 0.04 and 0.06 are the basic wake expansion rate and additional wake expansion rate in the absence of turbulence, respectively, which means that for every 15% increase in turbulence intensity, the wake expansion rate increases by an additional 0.06.

[0036] like Figure 5 As shown in the figure, after considering the expansion effect of turbulence on the wake, the influence range of the wake is larger, which will also make the subsequent optimization process more accurate.

[0037] Of course, in other embodiments, the above parameter setting values ​​can be adjusted.

[0038] This embodiment uses the modified model to determine a more accurate wake distribution, thereby obtaining a more accurate load distribution. Since wake diffusion directly affects the mutual interference between wind turbines, a more accurate wake model can better reflect the load distribution of wind turbines in actual operation.

[0039] The wake model will affect the inflow wind speed and load of the downstream fan. Therefore, using the modified wake model will obtain more accurate inflow wind speed and load of the downstream fan, and optimizing on this basis will obtain more accurate results.

[0040] like Figure 4 As shown in the figure, the working state of the wind farm can be divided into not participating in the primary frequency regulation and participating in the primary frequency regulation. When the wind farm is not participating in the primary frequency regulation, the wind farm is required to generate as much power as possible, while reducing the wind turbine load and the standard deviation of the load between wind turbines as much as possible. This can reduce the maintenance frequency of the wind turbines and the fatigue damage difference between wind turbines to prevent the damage rate of individual wind turbines from increasing. The load of the wind turbine mainly includes the tower load and the main shaft load. Figure 2It can be seen that when the axial induction factor increases between 0 and 1 / 3, although the wind energy capture coefficient increases, the wind turbine load also increases. Therefore, in this working state, a multi-objective optimization control strategy is adopted. In this state, the reference wind speed is set to 12m / s, the wind direction is 0°, the number of wind turbines is 16 (4*4 wind field), the wind turbine rotor radius is 63m, the rated power is 5MW, the straight-line distance between wind turbines is 500m, and the time span of time=50s is considered to simulate the dynamic change of wind speed. Based on the reference wind speed, Gaussian random distribution is used to generate the wind speed per second to simulate the fluctuation characteristics of the wind speed, which is expressed as: ; In multi-objective optimization, the following three objective functions need to be considered: ① Maximization of total power generation, the specific formula is: ; ② Minimize the fan load. The specific formula is: ; ③ Load uniformity, the specific formula is: ; in, and For the Tower load and shaft torque of each wind turbine, and is the average value of tower load and main shaft torque of all wind turbines. N is the total number of wind turbines.

[0041] The three objectives are optimized using a genetic algorithm to find the best compromise solution. In this embodiment, the genetic algorithm population size is set to 300, the maximum number of iterations is 800, and parallel computing is enabled to improve computational efficiency. The Pareto front is extracted at the end of the optimization and a compromise solution is obtained. The optimization results are shown in Figure 1. Figure 7 As shown in the figure, the proposed benchmark method is the case where the axial induction factor of the wind turbine is 1 / 3 in the initial state. It can be seen that compared with the benchmark, the proposed method can improve the power generation while greatly reducing the standard deviation of the wind turbine tower load and main shaft load, and the load standard deviation is basically maintained at 10 4 horizontal, the slight increase in spindle load is due to the increase in power generation.

[0042] When the wind farm participates in primary frequency regulation, the power generation is required to track the grid dispatching demand. The wind farm still maintains the maximum power generation strategy in the previous mode. There is no need to switch strategies. It only needs to connect to the supercapacitor circuit. Figure 6 The circuit shown performs energy storage and / or load shedding to ensure that the power generated by the wind farm can track the demand of the power grid.

[0043] The control switches D1 and D2 are connected in series, and the connection point between the two (i.e., the upper end of the control switch D2) is connected to one end of the supercapacitor C, and the other end of the supercapacitor C is connected to the lower end of the control switch D2. One end of the control switch D3 is connected to one end of the control switch D1, and the other end of the control switch D3 is connected to the unloading resistor R, and the other end of the unloading resistor R is connected to the lower end of the control switch D2. In some embodiments, the supercapacitor C may also be connected in series with an inductor.

[0044] The specific workflow is as follows: ① When the total power of the wind farm is greater than the grid dispatching demand, the supercapacitor circuit switch D1 is turned on, D2 and D3 are turned off, and the excess electric energy exceeding the grid dispatching is charged to the supercapacitor C.

[0045] ② If the supercapacitor C is fully charged and the total power of the wind farm is still greater than the grid dispatching demand, D1 and D2 are closed, D3 is opened, and the excess power generation is unloaded using the unloading resistor R.

[0046] ③ When the total power of the wind farm is less than the grid dispatching demand, D1 and D3 are turned off, D2 is turned on, and the supercapacitor supplies power to the grid so that the total power of the wind farm tracks the grid dispatching demand.

[0047] During the specific simulation, the basic wind speed of the wind farm is still 12m / s, and all parameters remain the same as before. The grid demand is set as follows: ; The above method is still used to optimize the axial induction factor of each fan, and the final optimization results are as follows: Figure 8 As shown in the figure, it can be seen that the actual output power can track the grid demand more stably, and at the same time can reduce the wind turbine load and the load standard deviation between wind turbines, which greatly reduces the wind turbine operation and maintenance cost. The above two solutions start from the axial induction factor and can achieve fast and accurate wind farm energy increase and load reduction control.

[0048] In this embodiment, the genetic algorithm used in the optimization and solution process of the multi-objective optimization problem under the two power generation modes is NSGA-II, which stands for non-dominated sorting genetic algorithm II. This algorithm simulates natural selection and genetic mechanisms to find the optimal solutions of multiple objective functions, has good convergence and diversity maintenance capabilities, and can find a set of uniform Pareto optimal solutions in multi-objective optimization problems.

[0049] The above is only the preferred algorithm of this embodiment. In other embodiments, other multi-objective optimization solution algorithms and / or nonlinear solution methods may be selected.

[0050] Embodiment 2 A wind farm control optimization method considering load balancing comprises the following steps: Optimize the load distribution, consider the influence of turbulence on the wake diffusion trajectory, and modify the wake diffusion model; When the wind farm does not participate in primary frequency regulation, a multi-objective optimization control strategy is adopted, with the control objectives of maximizing total power generation, minimizing wind turbine load and equalizing load, and the optimization is achieved by optimizing the axial induction factor; When the wind farm participates in primary frequency modulation, the above control method is still maintained. The supercapacitor energy storage unloading circuit is controlled to control the wind farm power generation to track the grid instructions, while adjusting the total tower load on each wind turbine and the standard deviation of each wind turbine load for the purpose of solving the problem.

[0051] Embodiment 3 A wind farm control optimization system considering load balancing, comprising: The multi-objective optimization module is configured to adopt a multi-objective optimization control strategy when the wind farm does not participate in primary frequency regulation, with the control objectives of maximizing total power generation, minimizing wind turbine load and equalizing load, and achieve optimization by optimizing the axial induction factor; The supercapacitor energy storage and unloading module is configured to maintain the control target of maximizing the total power generation when the wind farm participates in the primary frequency regulation, and to store energy and / or unload the wind farm so that the power generation of the wind farm tracks the demand of the power grid, and at the same time, to adjust the total load on each wind turbine and the standard deviation of the load on each wind turbine; The load correction module is configured to consider the influence of turbulence on the wake diffusion trajectory in the distribution of loads in both optimization processes.

[0052] Embodiment 4 A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the steps in the method provided in embodiment 1 or embodiment 2 are completed.

[0053] Embodiment 5 An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps in the method provided in Embodiment 1 or Embodiment 2 are completed.

[0054] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of one or more computer-usable storage media (including but not limited to disk storage, CD - ROM, optical storage, etc.).

[0055] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0056] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principle of the present invention without creative labor shall be included in the protection scope of the present invention.

Claims

1. A wind farm control optimization method considering load balancing, characterized in that: The following steps are involved: When the wind farm does not participate in primary frequency regulation, a multi-objective optimization control strategy is adopted, with the control objectives of maximizing total power generation, minimizing wind turbine load and equalizing load, and the optimization is achieved by optimizing the axial induction factor; When the wind farm participates in primary frequency regulation, the control target of maximizing the total power generation is maintained, and energy storage and / or load unloading are performed on the wind farm so that the power generation of the wind farm tracks the grid demand. At the same time, the total load on each wind turbine and the standard deviation of the load on each wind turbine are adjusted; In both optimization processes, the load distribution takes into account the influence of turbulence on the wake diffusion trajectory.

2. A wind farm control optimization method considering load balancing as claimed in claim 1, characterized in that: The axial induction factor is , wind energy capture coefficient of the wind turbine C P and the tower thrust coefficient of the wind turbine C T for: ； ； Corresponding captured wind power P and tower load The calculation formula is: ； ； in, v 0 is the inflow wind speed, v 1 is the wind speed behind the fan, ρ is the air density, A is the rotor area, axial induction factor .

3. A wind farm control optimization method considering load balancing as claimed in claim 1, characterized in that: The process of considering the influence of turbulence on the wake diffusion trajectory in the distribution of loads includes: normalizing the turbulence intensity correction term, taking the set percentage of turbulence as the standard, obtaining the turbulence intensity normalization factor, and correcting the wake diffusion model.

4. A wind farm control optimization method considering load balancing as claimed in claim 3, characterized in that: The correction of the wake diffusion model is as follows: ； ； in, is the set percentage, is the turbulence coefficient, is the normalized weight factor, m and n are the wake basic expansion rate and wake additional expansion rate in the absence of turbulence, respectively. is the distance from the upstream wind turbine, is the radius of the wake after diffusion, is the wind wheel radius.

5. The method for optimizing wind farm control considering load balancing according to claim 1, characterized in that: When the wind farm does not participate in primary frequency regulation, a multi-objective optimization control strategy is adopted, and the non-dominated genetic algorithm II is applied to optimize and solve each optimization objective. Parallel computing is enabled, and the Pareto front is extracted at the end of the optimization to obtain a compromise solution.

6. The method for optimizing wind farm control considering load balancing according to claim 1, characterized in that: Supercapacitors are used to access the connection lines between the wind farm and the AC power grid for energy storage and / or load shedding.

7. A wind farm control optimization method considering load balancing as claimed in claim 6, characterized in that: When the total power of the wind farm is greater than the grid dispatch demand, the excess power beyond the grid dispatch is charged to the supercapacitor; If the supercapacitor is fully charged and the total power of the wind farm is still greater than the grid dispatching demand, the excess power generation will be unloaded using the unloading resistor connected in parallel with the supercapacitor; When the total power of the wind farm is less than the grid dispatching demand, the supercapacitor supplies power to the grid so that the total power of the wind farm tracks the grid dispatching demand.

8. A wind farm control optimization system considering load balancing, characterized in that it includes: The multi-objective optimization module is configured to adopt a multi-objective optimization control strategy when the wind farm does not participate in primary frequency regulation, with the control objectives of maximizing total power generation, minimizing wind turbine load and equalizing load, and achieve optimization by optimizing the axial induction factor; The supercapacitor energy storage and unloading module is configured to maintain the control target of maximizing the total power generation when the wind farm participates in the primary frequency regulation, and to store energy and / or unload the wind farm so that the power generation of the wind farm tracks the demand of the power grid, and at the same time, to adjust the total load on each wind turbine and the standard deviation of the load on each wind turbine; The load correction module is configured to consider the influence of turbulence on the wake diffusion trajectory in the distribution of loads in both optimization processes.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the steps of the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps in the method according to any one of claims 1 to 7 are completed.

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