A wind farm wake optimization method based on the particle swarm optimization algorithm

Abstract

The present invention provides a wake optimization method for a wind farm based on a particle swarm optimization algorithm, belonging to the technical field of wind farm wake optimization. The method includes: obtaining the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data; calculating the wake wind speed of a single wind turbine in the wind farm based on the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data; modeling the wake effect of the entire wind farm according to the wake wind speed of a single wind turbine in the wind farm to obtain a wind farm wake model; obtaining a total active power optimization model for the wind farm based on the wind farm wake model; optimizing the total active power optimization model for the wind farm through an improved particle swarm algorithm to obtain the total power generation of the optimized wind farm. Optimize the total active power optimization model for the wind farm through an improved particle swarm algorithm to maximize the output power of the wind farm.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind farm wake optimization, and particularly relates to a wind farm wake optimization method based on a particle swarm optimization algorithm. Background Art

[0002] As an important part of clean energy, wind power generation plays an indispensable role in the global energy transition and response to climate change. With technological progress and cost reduction, the installed capacity of wind power has increased rapidly, which not only reflects the huge potential of wind energy resource development and utilization, but also highlights its important position in the power system. However, with the expansion of wind farm scale, the wake effect between wind turbines has become one of the main bottlenecks restricting the efficiency improvement of wind farms. The wake effect refers to the adverse impact of upstream wind turbines on downstream wind turbines, which can reduce the wind speed received by subsequent wind turbines and increase the turbulence intensity, resulting in a decrease in the output power of these wind turbines, an increase in mechanical fatigue, and ultimately affecting the economic benefits and service life of the entire wind farm.

[0003] To mitigate the impact of the wake effect on wind farm performance, researchers have developed a series of optimization techniques and methods. Layout optimization adjusts the relative positions between wind turbines to minimize the negative impact brought by the wake effect, and is applicable to newly built wind farms in the planning stage. Active wake control (AWC) is more suitable for wind farms that have been built and put into operation, mainly by adjusting the operating states of wind turbines, such as parameters like yaw angle, pitch angle or rotational speed, to improve the wake distribution and thus increase the overall output power of the wind farm. Axial induction factor control (AIC) and wake redirection control (WRC) are two common AWC strategies. AIC adjusts the wake intensity by changing the axial induction factor of the wind turbine, while WRC makes the wake deviate from downstream wind turbines by changing the yaw angle of the wind turbine, so as to increase the power output of the wind farm. However, the traditional axial induction factor control (AIC) relies on specific model assumptions and parameter settings, and it is difficult to respond in a timely manner under actual working conditions where the wind speed and direction change frequently, resulting in limited optimization effects.

[0004] Therefore, the present invention provides a wind farm wake optimization method based on a particle swarm optimization algorithm. Summary of the Invention

[0005] The present invention provides a wind farm wake optimization method based on a particle swarm optimization algorithm, so as to at least solve the problem in the prior art that the traditional axial induction factor control (AIC) relies on specific model assumptions and parameter settings, and it is difficult to respond in a timely manner under actual working conditions where the wind speed and direction change frequently, resulting in limited optimization effects.

[0006] The wind farm wake optimization method includes the following specific steps:

[0007] S1: Obtain the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data.

[0008] S2: Calculate the wake wind speed of a single wind turbine in the wind farm based on the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data.

[0009] S3: Model the wake effect of the entire wind farm according to the wake wind speed of a single wind turbine in the wind farm to obtain a wind farm wake model.

[0010] S4: Based on the wind farm wake model, obtain the total active power optimization model of the wind farm.

[0011] S5: Optimize the total active power optimization model of the wind farm through an improved particle swarm algorithm to obtain the total power generation of the optimized wind farm.

[0012] Furthermore, optimizing the total active power optimization model of the wind farm through an improved particle swarm algorithm includes the following specific steps:

[0013] S51: Take the wind turbine status quantity as the particle position and randomly initialize it to generate a finite number of particle populations.

[0014] S52: Calculate the fitness of all particles and select the attractor from them.

[0015] S53: Update the particle velocity, calculate the new positions of each particle, and calculate the optimal fitness of each particle.

[0016] S54: Determine whether the optimal fitness meets the termination condition. If it meets, execute step S55; if not, execute S56.

[0017] S55: Output the result.

[0018] S56: Return to continue executing steps S53 - S54.

[0019] Furthermore, in S2, the Jensen wake model is adopted to study the wake of a single wind turbine, and its expression is:

[0020]

[0021]

[0022] In the formula, is the effective input wind speed of the upstream wind turbine; is the wake wind speed at a distance of times the wind turbine impeller diameter downstream of the wind turbine; is the thrust coefficient of the wind turbine; is the wake decay constant; is the wind turbine impeller diameter; Downstream of the fan The wake diameter at a distance that is

[0023] Further, in S3, assuming that in the wind farm, before the th fan, there are fans whose wakes overlap with the wake of the th fan, then the expression of the wind farm wake model is:

[0024]

[0025]

[0026] In the formula, is the wind speed of the th fan; is the free incoming wind speed of the wind farm; is the input wind speed of the th fan upstream of the fan; is the wake wind speed of the input wind speed of the th fan upstream of the fan at the th fan; is the thrust coefficient of the th fan; is the fan 's impeller area; is the th fan and the th fan's distance along the wind direction; is the wake overlap area, represents an intermediate variable, representing the difference between the input wind speed and the downstream wind speed of the

[0027] th fan upstream of the fan.

[0028]

[0029] In the formula, is the radius of the wake area; is the impeller radius of the downstream fan; is the distance between the center of the wake area and the center of the impeller of the downstream fan in the direction perpendicular to the wake.

[0030] Further, based on the wind farm wake model, the total active power optimization model of the wind farm is obtained, including the following specific steps:

[0031] Calculate the power of the th fan in the wind farm, and its expression is:

[0032]

[0033] In the formula, is the power of the th wind turbine in the wind farm; is the air density, is the wind energy utilization coefficient of the th wind turbine;

[0034] Based on the power of the th wind turbine in the wind farm, the total output power of the wind farm is calculated, and its expression is:

[0035]

[0036] In the formula, is the total output power of the wind farm; is the number of wind turbines in the wind farm;

[0037] Finally, the total active power optimization model of the wind farm is expressed as:

[0038]

[0039] In the formula, is the axial induction factor; represents the power of the th wind turbine in the wind farm, represents the maximum value of the total output power of the wind farm in the total active power optimization model of the wind farm.

[0040] Furthermore, the wind energy utilization coefficient is related to the axial induction factor, and its expression is:

[0041] ;

[0042] The thrust coefficient is related to the axial induction factor, and its expression is:

[0043] .

[0044] Furthermore, in S52, the fitness of all particles is calculated, and the expression for selecting the attractor is:

[0045]

[0046] In the formula, is the position of the attractor generated by the th update; is the position of the first attractor after the th iteration; is the position of the second attractor after the th iteration; is the position of the th attractor after the th iteration; is the position of the particle at the -th iteration.

[0047] Furthermore, in S53, the expression for updating the particle velocity is:

[0048]

[0049] In the formula, represents the particle velocity at the -th iteration; represents the particle velocity at the -th iteration; is the weight at the -th iteration; is the individual learning factor; is the population learning factor; is the position of the particle at the -th iteration; is the individual optimal position, that is, the position of the particle with the highest fitness generated after all particles in the population at the -th iteration.

[0050] Furthermore, in S53, the expression for calculating the new position of each particle is:

[0051]

[0052] In the formula, is the position of the particle at the -th iteration.

[0053] As can be seen from the above technical solutions, the present invention has the following advantages:

[0054] In the wind farm wake optimization method based on the particle swarm optimization algorithm provided by the present application, the overall active power optimization model of the wind farm is optimized through an improved particle swarm algorithm to maximize the output power of the wind farm;

[0055] The improved particle swarm algorithm can dynamically adjust the optimization strategy according to real-time wind condition data to ensure continuous optimization of the operation parameters of the wind turbines throughout the life cycle of the wind farm, maximize the output power and economic benefits of the wind farm, and has stronger adaptability and flexibility;

[0056] Moreover, by introducing multiple dynamic attractors and a particle velocity pause mechanism, the algorithm enhances the global search ability and stability of the particle swarm, and can maintain an efficient and stable optimization process even with a small population size, improving the overall performance of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] To more clearly illustrate the technical solution of the present invention, the accompanying drawings required for description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts.

[0058] Figure 1 It is a flowchart of a wind farm wake optimization method based on the particle swarm optimization algorithm;

[0059] Figure 2 It is a schematic diagram of the movement of three groups of ordinary particles of a wind farm wake optimization method based on the particle swarm optimization algorithm towards the attractor position. Among them, (a) is a schematic diagram of the movement of the first group of ordinary particles towards the attractor position, (b) is a schematic diagram of the movement of the second group of ordinary particles towards the attractor position, and (c) is a schematic diagram of the movement of the third group of ordinary particles towards the attractor position;

[0060] Figure 3 It is a flowchart of an improved particle swarm algorithm for a wind farm wake optimization method based on the particle swarm optimization algorithm. Detailed implementation manners

[0061] In the following, the wind farm wake optimization method based on the particle swarm optimization algorithm will be described in detail, and various embodiments of the present disclosure will be described more comprehensively. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents, and / or alternative solutions falling within the spirit and scope of the various embodiments of the present disclosure.

[0062] In the following, the term "comprise" or "may comprise" that can be used in various embodiments of the present disclosure indicates the presence of the disclosed functions, operations, or elements, and does not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present disclosure, the terms "comprise", "have" and their cognates are only intended to mean the presence of specific features, numbers, steps, operations, elements, components, or combinations of the foregoing items, and should not be construed as precluding the presence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing items first.

[0063] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any combination or all combinations of the recited words. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0064] Expressions used in various embodiments of the present disclosure (such as "first", "second", etc.) may modify various components in various embodiments, but do not limit the corresponding components. For example, the above expressions do not limit the order and / or importance of the components. The above expressions are only used for the purpose of distinguishing one component from other components. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, without departing from the scope of various embodiments of the present disclosure, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component.

[0065] It should be noted that: if it is described that one component is "connected" to another component, the first component may be directly connected to the second component, and a third component may be "connected" between the first component and the second component. Conversely, when one component is "directly connected" to another component, it can be understood that there is no third component between the first component and the second component.

[0066] The term "user" used in various embodiments of the present disclosure may indicate a person using an electronic device, which may be a monitoring person, or a testing person, or an operating person.

[0067] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0068] The embodiment of the present application provides a wind farm wake optimization method based on a particle swarm optimization algorithm, which solves the technical problem that there is an urgent need for a technology that can dynamically adjust the optimization strategy according to real-time wind condition data.

[0069] The technical solutions proposed in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0070] Figure 1 It is a flowchart of a wind farm wake optimization method provided by an embodiment of the present application. As Figure 1 shown, a wind farm wake optimization method provided by an embodiment of the present application, the wind farm wake optimization method includes the following specific steps:

[0071] S1: Obtain the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data; the layout distribution data of the wind turbines in the wind farm includes the number of wind turbines and the coordinates of each wind turbine;

[0072] S2: Calculate the wake wind speed of a single wind turbine in the wind farm based on the layout distribution data of wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data.

[0073] S3: Based on the wake wind speed of a single wind turbine in the wind farm, model the wake effect of the entire wind farm to obtain a wind farm wake model.

[0074] S4: Based on the wind farm wake model, obtain the total active power optimization model of the wind farm.

[0075] S5: Optimize the total active power optimization model of the wind farm through an improved particle swarm optimization algorithm to obtain the total power generation of the optimized wind farm.

[0076] It should be noted that the wind farm wake model is used to calculate the input wind speed of each wind turbine in the wind farm.

[0077] The improved particle swarm optimization algorithm can dynamically adjust the optimization strategy according to real-time wind condition data, ensure continuous optimization of the operation parameters of wind turbines throughout the life cycle of the wind farm, maximize the output power and economic benefits of the wind farm, and has stronger adaptability and flexibility.

[0078] Furthermore, optimizing the total active power optimization model of the wind farm through the improved particle swarm optimization algorithm includes the following specific steps:

[0079] S51: Take the wind turbine status quantity as the particle position and randomly initialize it to generate a finite number of particle populations.

[0080] S52: Calculate the fitness of all particles and select the attractor from them.

[0081] S53: Update the particle velocity, calculate the new positions of each particle, and calculate the optimal fitness of each particle.

[0082] S54: Determine whether the optimal fitness meets the termination condition. If it meets, execute step S55; if it does not meet, execute S56.

[0083] S55: Output the result.

[0084] S56: Return to continue executing steps S53 - S54.

[0085] It should be noted that the wind turbine status quantity is the axial induction factor of each wind turbine, and the value range of the axial induction factor is between (0, 1 / 3), and the maximum value is 1 / 3.

[0086] The optimal fitness, that is, the wind turbine status quantity corresponding to each particle, is the maximum active power output of the wind farm calculated.

[0087] The present invention improves the Particle Swarm Optimization (PSO) algorithm by introducing multiple dynamic attractors and a particle velocity pause mechanism, and optimizes the total active power optimization model of a wind farm through the improved Particle Swarm Optimization (NP-PSO) algorithm to maximize the output power of the wind farm. Moreover, by introducing multiple dynamic attractors and a particle velocity pause mechanism, the algorithm enhances the global search ability and stability of the particle swarm, and can maintain an efficient and stable optimization process even with a small population size, thus improving the overall performance of the wind farm.

[0088] It should be noted that multiple dynamic attractors refer to those in the particle velocity update . After each iterative update, among all the particles, the particles (attractors) with the highest fitness are selected, where is a positive integer, and each particle randomly approaches the position of an attractor.

[0089] The velocity pause mechanism also refers to that in the particle velocity update expression, when , the velocity is updated normally, but if , the velocity of the particle is directly set to 0. Here, Tp is a random number. .

[0090] In an exemplary embodiment, the wind farm wake optimization method includes:

[0091] S1: Obtain the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data; the layout distribution data of the wind turbines in the wind farm includes the number of wind turbines and the coordinates of each wind turbine.

[0092] S2: Calculate the wake wind speed of a single wind turbine in the wind farm based on the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data.

[0093] In S2, the Jensen wake model is used to study the wake of a single wind turbine, and its expression is:

[0094]

[0095]

[0096] In the formula, is the effective input wind speed of the upstream wind turbine; is the wake wind speed at a distance of times the wind turbine impeller diameter downstream of the wind turbine; is the thrust coefficient of the wind turbine; is the wake decay constant; is the wind turbine impeller diameter; is the distance downstream of the wind turbine The wake diameter at a distance from the fan impeller. Wake decay constant is usually determined empirically; the wake decay constant In , in this embodiment, when is the case represents the wake decay constant of an onshore wind turbine, the value of is 0.075; when is the case represents the wake decay constant of an offshore wind turbine,

[0097] S3: Model the wake effect of the entire wind farm based on the wake wind speed of a single wind turbine in the wind farm to obtain the wind farm wake model.

[0098] In one embodiment, assume that in the wind farm, there are a total of wind turbines in front of the th wind turbine, and the wake of these wind turbines overlaps with the th wind turbine. Then the expression of the wind farm wake model is:

[0099]

[0100]

[0101] In the formula, is the wind speed of the th wind turbine; is the free incoming wind speed of the wind farm; is the th wind turbine; ; is the input wind speed of the th wind turbine upstream of the wind turbine; is the input wind speed of the th wind turbine upstream of the wind turbine at the location of the th wind turbine; is the thrust coefficient of the th wind turbine; is the wind turbine 's impeller area; is the th wind turbine and the th wind turbine along the wind direction distance; is the wake overlap area, represents an intermediate variable, representing the difference between the input wind speed and the downstream wind speed of the

[0102] According to the embodiment of the present application, the expression of the wake overlap area is:

[0103]

[0104] In the formula, is the radius of the wake region; is the radius of the downstream wind turbine impeller; is the distance between the center of the wake region and the center of the downstream wind turbine impeller in the direction perpendicular to the wake.

[0105] S4: Based on the wind farm wake model, obtain the total active power optimization model of the wind farm; the specific steps are as follows:

[0106] Calculate the power of the th wind turbine in the wind farm, and its expression is:

[0107]

[0108] In the formula, is the power of the th wind turbine in the wind farm; is the air density, is the th wind turbine's wind energy utilization coefficient.

[0109] Based on the power of the th wind turbine in the wind farm, calculate the total output power of the wind farm, and its expression is:

[0110]

[0111] In the formula, is the total output power of the wind farm; is the number of wind turbines in the wind farm.

[0112] Finally, the total active power optimization model of the wind farm is expressed as:

[0113]

[0114] In the formula, is the axial induction factor; represents the power of the th wind turbine in the wind farm, represents the maximum value of the total output power of the wind farm in the total active power optimization model of the wind farm.

[0115] It should be noted that the wind energy utilization coefficient is related to the axial induction factor, and its expression is:

[0116] .

[0117] The thrust coefficient is related to the axial induction factor, and its expression is:

[0118] .

[0119] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process in this embodiment, another wind farm wake optimization method is provided. In S52, the fitness of all particles is calculated, and the expression for selecting the attractor from them is:

[0120]

[0121] wherein, is the position of the attractor generated by the -th update; is the position of the particle at the -th iteration.

[0122] It should be noted that the definition of the attractor is: after the -th update, among all particles, the top particles with the highest fitness, and the attractor tracked by each particle at the -th update will be randomly selected.

[0123] It should be further noted that in S53, the expression for updating the particle velocity is:

[0124]

[0125] wherein, represents the particle velocity at the -th iteration; represents the particle velocity at the -th iteration; is the weight at the -th iteration; is the individual learning factor; is the population learning factor; it is set that and take the value of 1.4932; , and are state variables and are all random numbers between 0 and 1; if then the next particle velocity update inherits from the previous particle velocity and simultaneously tracks two extreme values, namely: the individual historical optimal position and the attractor position; is the position of the particle at the -th iteration; is the individual optimal position, that is, the position of the particle with the highest fitness generated after all particles in the population at the -th iteration.

[0126] As an example, in S53, the expression for calculating the new position of each particle is:

[0127]

[0128] In the formula, is the position of the particle at the -th iteration.

[0129] In one embodiment, Figure 3 is a flowchart of another wake optimization method for a wind farm based on the particle swarm optimization algorithm according to an embodiment of the present invention. On the basis of the above embodiments, this embodiment further optimizes and expands the wake optimization method for a wind farm based on the particle swarm optimization algorithm.

[0130] The NP-PSO algorithm improves the search and exploitation capabilities of the PSO optimization algorithm by simulating the dynamic behavior of a group of brain neurons. Among them, the attractor strategy improves the solution performance of the algorithm by simulating the neuron attractors in the brain and attracting other particles to explore the current multiple optimal solution regions. In NP-PSO, each particle not only tracks its personal best, but also tracks an attractor position (one of the global optimal solution or local best solutions). This dual-extremum tracking mechanism helps particles jump out of local optima and avoids the premature convergence phenomenon commonly found in traditional PSO algorithms. In addition, the NP-PSO algorithm introduces a velocity pause mechanism. The velocity pause mechanism simulates the real particle movement, and its velocity update can either inherit from the previous particle movement velocity or directly become zero. Through the velocity pause mechanism, particles can either inherit the velocity to develop new regions or may directly stop at the current position with a velocity of zero.

[0131] Combined with Figure 2 in (a), (b), and (c) therein, during the update process of the improved PSO algorithm, the velocity update formula of each particle is as follows:

[0132]

[0133] In the formula, represents the particle velocity at the -th iteration; represents the particle velocity at the -th iteration; is the weight at the -th iteration; is the individual learning factor; is the population learning factor; it is set that and take the value of 1.4932; , and are state variables and are all random numbers between 0 and 1; if Then the velocity update of the next particle inherits from the previous particle velocity, and at the same time, two extreme values are tracked, namely: the individual historical optimal position and the attractor position; is the position of the particle at the th iteration; is the individual optimal position, that is, the position of the particle with the highest fitness generated after all particles in the population have iterated times.

[0134] Among them, the definition of the attractor is: after the th update, among all particles, the top particles with the highest fitness, and the attractor tracked by each particle at the th update will be randomly selected.

[0135] The expression for calculating the fitness of all particles and selecting the attractor from them is:

[0136]

[0137] In the formula, is the position of the attractor generated at the th update; is the position of the first attractor after the th iteration; is the position of the second attractor after the th iteration; is the position of the th attractor after the th iteration; is the position of the particle at the th iteration.

[0138] It should be noted that if , then the velocity of this particle directly becomes 0. The present invention introduces a velocity pause mechanism, and the velocity of each particle in the population can not only inherit the velocity of the previous iteration but also directly become zero. The NP-PSO algorithm not only improves the probability of finding the global optimal solution but also reduces unnecessary computational overhead, making the active power optimization of large-scale wind farms more feasible.

[0139] The expression for the new position of each particle is:

[0140]

[0141] In the formula, is the position of the particle at the th iteration.

[0142] Combined with Figure 3, The calculation process of the NP-PSO algorithm is as follows: First, start with randomly generating a specified number of particle populations and calculating their initial fitness values. Second, select the top particles as attractors, and these attractors will guide other particles to explore the solution space in subsequent iterations. Determine whether the velocity needs to be updated based on the state variable values of the particles. If the condition is met, adjust the velocity of the particles according to the defined velocity update formula. Subsequently, use the updated velocity to adjust the positions of all particles and calculate the new fitness values. Finally, check whether the preset iteration termination condition is satisfied (such as reaching the maximum number of iterations or fitness convergence). If satisfied, stop the iteration and output the optimal solution; otherwise, select the top particles with the highest fitness in the updated particle swarm as new attractors and prepare to enter the next iteration.

[0143] The present invention introduces a particle swarm optimization algorithm based on a multi-attractor mechanism (NP-PSO). By introducing multiple dynamic attractor points, this algorithm enhances the global search ability and stability of the particle swarm, and can maintain an efficient and stable optimization process even with a small population size. Additionally, the present invention simultaneously introduces a velocity pause mechanism, where the velocity of each particle in the population can either inherit the velocity of the previous iteration or directly become zero. Thanks to the two introduced mechanisms, the NP-PSO algorithm not only increases the probability of finding the global optimal solution but also reduces unnecessary computational overhead, making the active power optimization of large-scale wind farms more feasible. By applying the improved particle swarm algorithm, the present invention not only improves the overall performance of the wind farm but also provides new ideas and technical means for the design and management of future wind farms. In summary, the present invention solves the limitations existing in the prior art through innovative optimization strategies, especially making significant progress in the operation optimization of wind turbines in existing wind farms.

[0144] The wind farm wake optimization method based on the particle swarm optimization algorithm provided by the embodiments of the present application can be applied to electronic devices. Those skilled in the art can understand that the structure of the electronic devices involved in the embodiments of the present invention does not constitute a limitation on the electronic devices. The electronic devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In the embodiments of the present invention, the electronic devices include, but are not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described herein and / or claimed.

[0145] The above-mentioned electronic device implements the method for optimizing the wake of a wind farm based on the particle swarm optimization algorithm in the present application to obtain the layout distribution data of wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data; calculate the wake wind speed of a single wind turbine in the wind farm based on the layout distribution data of wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data; model the wake effect of the entire wind farm according to the wake wind speed of a single wind turbine in the wind farm to obtain a wind farm wake model; calculate the total power generation of the wind farm based on the wind farm wake model; use the wind turbine status quantity as the particle position and randomly initialize it to generate a finite number of particle populations; calculate the fitness of all particles and select the attractor from them; update the particle velocity, calculate the new positions of each particle, and calculate the optimal fitness of each particle; determine whether the optimal fitness meets the termination condition. If it meets, output the result; if it does not meet, return and continue to execute the above steps until the optimal fitness meets the termination condition. The present invention introduces a particle swarm optimization algorithm based on a multi-attractor mechanism (NP-PSO). This algorithm enhances the global search ability and stability of the particle swarm by introducing multiple dynamic attraction points, and can maintain an efficient and stable optimization process even with a relatively small population size. In addition, the present invention simultaneously introduces a velocity pause mechanism, where the velocity of each particle in the population can either inherit the velocity of the previous iteration or directly become zero. Thanks to the introduction of the two mechanisms, the NP-PSO algorithm not only increases the probability of finding the global optimal solution but also reduces unnecessary computational overhead, making the active power optimization of large-scale wind farms more feasible. By applying the improved particle swarm algorithm, the present invention not only improves the overall performance of the wind farm but also provides new ideas and technical means for the design and management of future wind farms. In summary, the present invention solves the limitations existing in the prior art through an innovative optimization strategy, and has made remarkable progress especially in the operation optimization of wind turbines in existing wind farms.

[0146] In the storage medium provided by the present application, there is a program product capable of implementing the method for optimizing the wake of a wind farm based on the particle swarm optimization algorithm.

[0147] The wake optimization method for a wind farm based on the particle swarm optimization algorithm includes: obtaining the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data; calculating the wake wind speed of a single wind turbine in the wind farm based on the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data; modeling the wake effect of the entire wind farm according to the wake wind speed of a single wind turbine in the wind farm to obtain a wind farm wake model; calculating the total power generation of the wind farm based on the wind farm wake model; using the wind turbine status quantity as the particle position and randomly initializing it to generate a finite number of particle populations; calculating the fitness of all particles, and selecting an attractor from them; updating the particle velocity, calculating the new positions of each particle, and calculating the optimal fitness of each particle; determining whether the optimal fitness meets the termination condition. If it meets, output the result; if it does not meet, return to continue to execute the above steps until the optimal fitness meets the termination condition.

[0148] The present invention improves the particle swarm algorithm (PSO) by introducing multiple dynamic attractors and a particle velocity pause mechanism, optimizes the total active power optimization model of the wind farm through the improved particle swarm algorithm (NP-PSO), maximizes the output power of the wind farm, and this algorithm enhances the global search ability and stability of the particle swarm by introducing multiple dynamic attractors and a particle velocity pause mechanism, and can maintain an efficient and stable optimization process even under a small population size, improving the overall performance of the wind farm.

[0149] In some possible implementation manners, the wake optimization method for a wind farm based on the particle swarm optimization algorithm of the present disclosure may be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section above of this specification.

[0150] The storage medium of the present disclosure may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0151] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0152] For those of ordinary skill in the art, according to the teachings of the present invention, designing different forms of control circuits does not require creative labor. These changes, modifications, substitutions, and variations to the embodiments still fall within the protection scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A wind farm wake optimization method based on the particle swarm optimization algorithm, characterized in that, The wake optimization method for the wind farm includes the following specific steps: S1: Obtain the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data; S2: Calculate the wake wind speed of a single wind turbine in the wind farm based on the layout distribution data of the wind turbines in the wind farm, the wind direction data in the wind farm, and the wind speed data; S3: Model the wake effect of the entire wind farm according to the wake wind speed of a single wind turbine in the wind farm to obtain a wind farm wake model; S4: Obtain the total active power optimization model of the wind farm based on the wind farm wake model; S5: Optimize the total active power optimization model of the wind farm through an improved particle swarm algorithm to obtain the total power generation of the optimized wind farm; Optimizing the total active power optimization model of the wind farm through an improved particle swarm algorithm includes the following specific steps: S51: Take the wind turbine status quantity as the particle position and randomly initialize it to generate a finite number of particle populations; S52: Calculate the fitness of all particles and select attractors from them; S53: Update the particle velocity, calculate the new positions of each particle, and calculate the optimal fitness of each particle; S54: Determine whether the optimal fitness meets the termination condition. If it meets, execute step S55. If it does not meet, execute S56; S55: Output the result; S56: Return to continue executing steps S53 - S54; In S4, obtaining the total active power optimization model of the wind farm based on the wind farm wake model includes the following specific steps: Calculate the power of the th wind turbine in the wind farm, and its expression is: In the formula, is the power of the th wind turbine in the wind farm; is the air density, is the wind energy utilization coefficient of the th wind turbine; Based on the power of the th wind turbine in the wind farm, calculate the total output power of the wind farm. Its expression is: Wherein, is the total output power of the wind farm; is the number of wind turbines in the wind farm; Finally, the total active power optimization model of the wind farm is expressed as: In the formula, is the axial induction factor; represents the power of the th wind turbine in the wind farm, represents the maximum value of the total output power of the wind farm in the total active power optimization model of the wind farm.

2. The wind farm wake optimization method according to claim 1, characterized in that, In S2, the Jensen wake model is used to study the wake of a single wind turbine, and its expression is: In the formula, is the effective input wind speed of the upstream fan; is the wake wind speed at a distance of times the diameter of the fan impeller downstream of the fan; is the thrust coefficient of the fan; is the wake decay constant; is the diameter of the fan impeller; is the wake diameter at a distance of times the fan impeller downstream of the fan.

3. The wake optimization method for a wind farm according to claim 2, wherein In S3, assuming that in the wind farm, in front of the th wind turbine, there are wind turbines whose wakes overlap with the wake of the th wind turbine, then the expression of the wind farm wake model is: Wherein, is the wind speed of the th fan; is the free incoming wind speed of the wind farm; is the th fan; ; is the input wind speed of the th fan upstream of the fan; is the wake wind speed at the th fan of the input wind speed of the th fan upstream of the fan; is the thrust coefficient of the th fan; is the impeller area of the fan ; is the distance along the wind direction between the th fan and the th fan; is the wake overlap area, represents an intermediate variable, representing the difference between the input wind speed and the downstream wind speed of the jth fan upstream of the fan.

4. The method for optimizing the wake of a wind farm according to claim 3, wherein The expression for the wake overlap area is: In the formula, is the radius of the wake region; is the radius of the downstream wind turbine impeller; is the distance between the center of the wake region and the center of the downstream wind turbine impeller in the direction perpendicular to the wake.

5. The wake optimization method for a wind farm according to claim 4, characterized in that The wind energy utilization coefficient is related to the axial induction factor, and its expression is: ； The thrust coefficient is related to the axial induction factor, and its expression is: 。 6. The method for optimizing the wake of a wind farm according to claim 5, characterized in that In S52, the expression for calculating the fitness of all particles and selecting attractors from them is: Wherein, is the attractor position generated by the -th update; is the position of the first attractor after the -th iteration; is the position of the second attractor after the -th iteration; is the position of the -th attractor after the -th iteration; is the position of the particle at the -th iteration.

7. The method for optimizing the wake of a wind farm according to claim 6, characterized in that, In S53, the expression for updating the particle velocity is: Wherein, represents the particle velocity at the -th iteration; represents the particle velocity at the -th iteration; is the weight at the -th iteration; is the individual learning factor; is the population learning factor; , and are state variables, and all are random numbers between 0 and 1; is the position of the particle at the -th iteration; is the individual optimal position, that is, the position of the particle with the highest fitness generated after all particles in the population at the -th iteration.

8. The wind farm wake optimization method according to claim 7, characterized in that, In S53, the expression for calculating the new positions of each particle is: In the formula, is the position of the particle at the -th iteration.

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