A wind farm control optimization method and system considering load balancing

Abstract

The present invention belongs to the field of high-power offshore wind farms and provides a wind farm control optimization method and system that takes load balancing into consideration. 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. Optimization is achieved by optimizing the axial induction factor; the control objective of maximizing total power generation is maintained, and energy storage and / or unloading are performed on the wind farm so that the wind farm's power generation tracks the grid demand; and in both optimization processes, the load distribution considers the impact of turbulence on the wake diffusion trajectory. The present invention can enhance the wind farm's power generation capacity, reduce the overall load and load differences of the units, and reduce wind turbine maintenance costs and the cost per kilowatt-hour, thus solving the problems of high costs and poor optimization effects in the prior art.

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 a 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, this will accelerate equipment fatigue, shorten service life and increase maintenance costs.

[0004] The primary frequency regulation mode is an operational mode used to meet the grid's frequency regulation needs. It primarily responds to grid power demands based on a proportional allocation method, allocating frequency regulation tasks according to the rated power of each wind turbine or a preset ratio. However, this mode also has shortcomings. It does not fully consider the load conditions of wind turbines during frequency regulation, which can result in some turbines bearing excessive loads, affecting the operational reliability and economic efficiency of the entire wind farm.

[0005] Existing approaches focus on optimizing the control of existing wind farms, aiming to increase the overall output power of the wind farm by considering wake effects. Their core approach is to thoroughly study the quantitative relationship between changes in wind turbine state parameters, output power, and wake distribution. An optimization control model is then established. The optimization scheme uses maximizing the overall output power of the wind farm as the objective function, selecting the axial induction factor as the optimization parameter, and employing a particle swarm algorithm to solve the problem. The axial induction factor is a key variable linking wake wind speed and output power, and its changes directly affect the operating state and wake characteristics of the wind turbine. The optimal combination of axial induction factors is sought. However, this approach only considers increasing the capture volume under conventional operating modes and does not account for the optimization process of the wind farm participating in primary frequency regulation. Furthermore, this approach optimizes only over a single timeframe, not continuously, and therefore cannot account for wake effects under the influence of turbulence. Furthermore, this approach does not consider balanced wind turbine loads, resulting in some turbines being overloaded during the optimization process. This increases the damage rate of these turbines, significantly varying their lifespan and undoubtedly increasing operational and maintenance costs. Summary of the Invention

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

[0007] According to some embodiments, the present invention adopts the following technical solutions:

[0008] A wind farm control optimization method considering load balancing includes the following steps:

[0009] 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 uniforming load. Optimization is achieved by optimizing the axial induction factor.

[0010] When a wind farm participates in primary frequency regulation, the control objective of maximizing total power generation is maintained, and energy storage and / or load shedding are performed on the wind farm to ensure that the wind farm's power generation tracks 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.

[0011] In both optimization processes, the load distribution takes into account the influence of turbulence on the wake diffusion trajectory.

[0012] As an optional embodiment, the axial induction factor is , wind energy capture coefficient of wind turbine C P and the wind turbine tower thrust coefficient C T for:

[0013] ;

[0014] ;

[0015] Corresponding captured wind power P and tower loads The calculation formula is:

[0016] ;

[0017] ;

[0018] 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 .

[0019] 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.

[0020] As a further implementation method, the wake diffusion model is modified as follows:

[0021] ;

[0022] ;

[0023] in, is the set baseline turbulence intensity value, is the turbulence coefficient, is the normalized weight factor, m and n are the base expansion rate and additional expansion rate of the wake in the absence of turbulence, respectively. is the radius of the wake after diffusion, is the rotor radius, is the distance from the upstream wind turbine.

[0024] 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. Parallel computing is enabled, and the Pareto front is extracted at the end of the optimization to obtain a compromise solution.

[0025] 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 grid to store energy and / or unload load.

[0026] Furthermore, when the total power of the wind farm is greater than the grid dispatching demand, the excess electric energy exceeding the grid dispatching demand is charged to the supercapacitor;

[0027] 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;

[0028] 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.

[0029] A wind farm control optimization system considering load balancing includes:

[0030] The multi-objective optimization module is configured to use a multi-objective optimization control strategy when the wind farm does not participate in primary frequency regulation. The control objectives are to maximize total power generation, minimize wind turbine load, and equalize load. The optimization is achieved by optimizing the axial induction factor.

[0031] The fast optimization module is configured to maintain the control target of maximizing total power generation when the wind farm participates in primary frequency regulation, and to perform energy storage and / or load shedding on the wind farm so that the wind farm's power generation tracks the grid demand;

[0032] The load correction module is configured to consider the influence of turbulence on the wake diffusion trajectory in the load distribution in both optimization processes.

[0033] 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.

[0034] 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 above method are completed.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 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. 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 minimizing the wind turbine tower load and the load standard deviation between wind turbines. When the wind farm participates in primary frequency modulation, the wind farm power generation power is tracked by optimizing the axial induction factor and controlling the supercapacitor energy storage unloading circuit while adjusting the total tower and main shaft load on each wind turbine and the load standard deviation of each wind turbine.

[0037] The present invention optimizes the load distribution of wind farms in conventional wind energy capture and primary frequency modulation modes, considers the influence of turbulence on the wake diffusion trajectory in the wake effect, optimizes the load distribution in the two wind farm operating modes, reduces the total load while making the load evenly distributed, and avoids the problem of some wind turbines being easily damaged by excessive loads.

[0038] In the conventional mode of the wind farm, the present invention adopts multi-objective optimization for contradictory optimization objectives, making the optimization results more reasonable. In the mode in which the wind farm participates 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.

[0039] 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

[0040] The accompanying drawings, 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.

[0041] Figure 1 is a schematic diagram of a wind turbine flow tube model according to an embodiment;

[0042] Figure 2 is an embodiment and Graph showing changes with axial induction factor;

[0043] Figure 3 is a wind farm wake shielding diagram of an embodiment;

[0044] Figure 4 is a flow chart of wind farm control optimization according to an embodiment;

[0045] Figure 5 1 is a wake distribution diagram considering turbulence factors in an embodiment, wherein (a) is a wake distribution diagram without considering turbulence, and (b) is a wake distribution diagram after correction considering turbulence;

[0046] Figure 6 This is a schematic diagram of a wind farm supercapacitor energy storage and unloading circuit according to an embodiment;

[0047] Figure 7 This is a result diagram under the normal mode of an embodiment;

[0048] Figure 8 This is a result diagram of participating in a frequency modulation mode in an embodiment. DETAILED DESCRIPTION

[0049] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0050] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0051] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0052] In the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0053] Example 1

[0054] As described in the background technology, the current wind farm operating 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 to be greatly attenuated due to wind speed, limited power output, and increased load loss. The primary frequency regulation mode often responds to the power demand of the power grid with a proportional allocation method, and also does not take into account 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 influence of the wake 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.

[0055] To solve the above problems, this embodiment provides a wind farm control optimization method that takes load balancing into consideration, which can achieve optimized control of the wind farm that takes into account both wake effect and load loss, and strives to enhance the power generation capacity of the wind farm, reduce the overall load and load difference of the units, and reduce the maintenance cost and cost per kilowatt-hour of wind turbines.

[0056] A wind farm control optimization method considering load balancing includes the following steps:

[0057] 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 uniforming load. Optimization is achieved by optimizing the axial induction factor.

[0058] When a wind farm participates in primary frequency regulation, the control objective of maximizing total power generation is maintained, and energy storage and / or load shedding are performed on the wind farm to ensure that the wind farm's power generation tracks grid demand.

[0059] In both optimization processes, the load distribution takes into account the influence of turbulence on the wake diffusion trajectory.

[0060] The specific details of this method are described below.

[0061] 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:

[0062] ;

[0063] ;

[0064] The corresponding calculation formulas for captured wind power, tower load and main shaft torque are:

[0065] ;

[0066] ;

[0067] ;

[0068] 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 Increased, but the thrust coefficient It also increases accordingly.

[0069] Regarding how to calculate the corresponding rotational speed ω and pitch angle β from the axial induction factor, this embodiment provides a solution. In addition to using the axial induction factor to express the wind energy capture coefficient, it can also be expressed using the tip speed ratio and pitch angle, as shown in the following formula:

[0070] ;

[0071] ;

[0072] After obtaining the axial induction factor, the value of Cp can be obtained and the pitch angle can be set. , 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 get the pitch angle .

[0073] It can be expressed as:

[0074] ;

[0075] 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 by Matlab for solving nonlinear equations.

[0076] like Figure 3As shown in Figure 2, 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 downstream distance, and the wind speed is uniformly distributed within the wake region. The model calculates the wind speed attenuation after the wind passes through the wind turbine using the following formula:

[0077] ;

[0078] in, is the fan rotor radius, is the original wind speed, is the radius of the wake after diffusion, is the rotor radius, 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 the absence of turbulence. However, turbulence enhances the diffusion of the wake, making the increase in the wake radius not only dependent on distance but also on the turbulence intensity.

[0079] 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. In this embodiment, 15% turbulence is used as the standard to obtain the turbulence intensity normalization factor, and then the wake diffusion model is corrected. The formula is as follows:

[0080] ;

[0081] ;

[0082] in, is the turbulence coefficient, is the normalized weight factor, is the radius of the wake after diffusion, is the rotor radius, is the distance from the upstream wind turbine, 0.04 and 0.06 are the basic expansion rate and additional expansion rate of the wake 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.

[0083] 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.

[0084] Of course, in other embodiments, the above parameter setting values can be adjusted.

[0085] 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.

[0086] The wake model affects the inlet wind speed and load of the downstream wind turbine. Therefore, using a modified wake model will obtain more accurate inlet wind speed and load of the downstream wind turbine, and optimization based on this will achieve more accurate results.

[0087] like Figure 4 As shown in the figure, the working state of a wind farm can be divided into two types: not participating in primary frequency regulation and participating in primary frequency regulation. When a wind farm is not participating in primary frequency regulation, it is required to generate as much power as possible while minimizing the load on the wind turbines and the standard deviation of the load between wind turbines. This can reduce the number of wind turbine maintenance times and the difference in fatigue damage between wind turbines, thereby preventing an increase in the damage rate of individual wind turbines. The load on the wind turbine mainly includes the tower load and the main shaft load. Figure 2 It 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, under this working state, a multi-objective optimization control strategy is adopted. In this state, the baseline wind speed is set to 12m / s, the wind direction is 0°, the number of wind turbines is 16 (4*4 wind farm), the wind turbine rotor radius is 63m, the rated power is 5MW, the straight-line distance between wind turbines is 500m, and a time span of time=50s is considered to simulate the dynamic changes of wind speed. Based on the baseline wind speed, a 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 follows:

[0088] ;

[0089] In multi-objective optimization, the following three objective functions need to be considered: ① Maximizing the total power generation, the specific formula is:

[0090] ;

[0091] ② Minimize the fan load. The specific formula is:

[0092] ;

[0093] ③ Load uniformity, the specific formula is:

[0094] ;

[0095] 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.

[0096] 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. After the optimization is completed, the Pareto front is extracted and the compromise solution is obtained. The optimization results are shown in Figure 2. Figure 7 As shown in the figure, the proposed benchmark method is the case where the initial state of the wind turbine axial induction factor is 1 / 3. It can be seen that compared with the benchmark case, 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 level, the slight increase in spindle load is due to the increase in power generation.

[0097] When the wind farm participates in a 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.

[0098] Control switches D1 and D2 are connected in series, with their connecting point (i.e., the upper end of control switch D2) connected to one end of supercapacitor C, and the other end of supercapacitor C connected to the lower end of control switch D2. One end of control switch D3 is connected to one end of control switch D1, and the other end of control switch D3 is connected to a dump resistor R, which in turn is connected to the lower end of control switch D2. In some embodiments, supercapacitor C may also be connected in series with an inductor.

[0099] The specific workflow is as follows:

[0100] 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 closed, and the excess power exceeding the grid dispatching demand is charged to the supercapacitor C.

[0101] 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.

[0102] 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.

[0103] During the simulation, the wind farm's base wind speed remains at 12m / s, and all parameters remain consistent with the previous ones. The grid demand settings are as follows:

[0104] ;

[0105] The same as before. 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, the actual output power can stably track grid demand, while also reducing wind turbine load and the standard deviation of loads between turbines, significantly reducing wind turbine operation and maintenance costs. The above two solutions, based on the axial induction factor, can achieve fast and accurate wind farm power increase and load reduction control.

[0106] 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. It has good convergence and diversity maintenance capabilities. In the multi-objective optimization problem, it can find a set of uniform Pareto optimal solutions.

[0107] 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.

[0108] Example 2

[0109] A wind farm control optimization method considering load balancing includes the following steps:

[0110] Optimize the load distribution, consider the impact of turbulence on the wake diffusion trajectory, and modify the wake diffusion model;

[0111] 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 uniforming load. Optimization is achieved by optimizing the axial induction factor.

[0112] When the wind farm participates in primary frequency regulation, 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. At the same time, the total tower load on each wind turbine and the standard deviation of each wind turbine load are adjusted to solve the problem.

[0113] Example 3

[0114] A wind farm control optimization system considering load balancing includes:

[0115] The multi-objective optimization module is configured to use a multi-objective optimization control strategy when the wind farm does not participate in primary frequency regulation. The control objectives are to maximize total power generation, minimize wind turbine load, and equalize load. The optimization is achieved by optimizing the axial induction factor.

[0116] The supercapacitor energy storage and unloading module is configured to maintain the control target of maximizing total power generation when the wind farm participates in primary frequency regulation, and to store energy and / or unload the wind farm so that the wind farm's power generation tracks the grid demand. At the same time, it adjusts the total load on each wind turbine and the standard deviation of the load on each wind turbine.

[0117] The load correction module is configured to consider the influence of turbulence on the wake diffusion trajectory in the load distribution in both optimization processes.

[0118] Example 4

[0119] 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.

[0120] Example 5

[0121] 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 of the method provided in the first or second embodiment are completed.

[0122] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely 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.).

[0123] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. 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.

[0124] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0126] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made by those skilled in the art that fall within the spirit and principles of the present invention and do not require creative effort are intended to be within the scope of protection of the present invention.

Claims

1. A wind farm control optimization method considering load balancing, characterized by: The following steps are involved: Optimize the load distribution, consider the impact 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 uniform load. Optimization is achieved by optimizing the axial induction factor. The specific formula for uniform load 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, and N is the total number of wind turbines; When a wind farm participates in primary frequency regulation, the control goal of maximizing total power generation is maintained, and energy storage and / or load shedding are performed on the wind farm to ensure that the wind farm's power generation tracks grid demand. At the same time, the total load on each wind turbine and the standard deviation of each turbine's load are adjusted. Specifically, supercapacitors are connected to the connection line between the wind farm and the AC grid for energy storage and / or load shedding. 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; In both optimization processes, the load distribution considers the influence of turbulence on the wake diffusion trajectory. The process includes normalizing the turbulence intensity correction term, using the set percentage of turbulence as the standard, obtaining the turbulence intensity normalization factor, and correcting the wake diffusion model. Specifically, ； ； in, is the set percentage, is the turbulence coefficient, is the normalized weight factor, m and n are the base expansion rate and additional expansion rate of the wake in the absence of turbulence, respectively. is the distance from the upstream wind turbine, is the radius of the wake after diffusion, is the radius of the wind wheel.

2. The wind farm control optimization method considering load balancing according to claim 1, characterized in that: The axial induction factor is , wind energy capture coefficient of wind turbine C P and the wind turbine tower thrust coefficient C T for: ； ； Corresponding captured wind power P and tower loads 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. The wind farm control optimization method 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.

4. A wind farm control optimization system considering load balancing, characterized by: include: A multi-objective optimization module is configured to optimize the load distribution, consider the effect 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 uniform load. Optimization is achieved by optimizing the axial induction factor. The specific formula for uniform load 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, and N is the total number of wind turbines; The supercapacitor energy storage and unloading module is configured to maintain the control target of maximizing total power generation when the wind farm participates in primary frequency regulation, and to store energy and / or unload the wind farm so that the wind farm's power generation tracks the grid demand. At the same time, it adjusts the total load on each wind turbine and the standard deviation of the load on each wind turbine. Specifically, it uses supercapacitors to connect the connection line between the wind farm and the AC grid to store energy and / or unload the load. 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; The load correction module is configured to consider the effect of turbulence on the wake diffusion trajectory in the load distribution during both optimization processes. The process includes normalizing the turbulence intensity correction term to a set percentage of turbulence as the standard to obtain the turbulence intensity normalization factor and correcting the wake diffusion model. Specifically, ； ； in, is the set percentage, is the turbulence coefficient, is the normalized weight factor, m and n are the base expansion rate and additional expansion rate of the wake in the absence of turbulence, respectively. is the distance from the upstream wind turbine, is the radius of the wake after diffusion, is the radius of the wind wheel.

5. 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 3.

6. 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 the steps of the method according to any one of claims 1 to 3 are completed when the computer instructions are executed by the processor.

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