Wind storage system control method and device based on genetic algorithm, equipment and medium

Abstract

The application discloses a wind storage system control method and device based on a genetic algorithm, equipment and a storage medium, relates to the technical field of wind power generation, and comprises the following steps: determining whether a preset genetic algorithm optimization condition is met at present; if the preset genetic algorithm optimization condition is met at present, selecting a plurality of variable pitch rate values and a plurality of energy storage charging and discharging rate values from a preset variable pitch rate range and a preset energy storage charging and discharging rate range to obtain an initial data population; performing operation on the initial data population based on the genetic algorithm to obtain a target data population; determining whether the target data population meets a preset iteration termination condition; and if the target data population meets the preset iteration termination condition, determining a target variable pitch rate and a target energy storage charging and discharging rate from the target data population. In this way, the stability of the power system can be improved, and the stability of the unit power output and the utilization rate of the energy storage can be improved by utilizing the characteristics of the rapid charging / discharging of the energy storage.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and in particular to a wind-storage system control method, device, equipment, and storage medium based on a genetic algorithm. Background Technology

[0002] With the development of renewable energy, wind power generation has grown rapidly in recent years. Currently, wind turbines operating above rated wind speed mainly rely on pitch control to maintain constant output power as wind speed changes. Therefore, the continuous variation in wind speed directly affects the stability of the unit's power output. Units frequently experience shutdown faults when operating above rated wind speed, such as unit vibration, overspeed, and overpower. This is mainly due to the intensity of wind turbulence and the intermittency of wind speed causing the control system to either fail to adjust the pitch in time or adjust it too quickly.

[0003] Currently, in the field of wind power generation, the pitch control strategy used both domestically and internationally is mainly the traditional PID (proportional-integral-derivative) control. However, the traditional PID control method cannot adjust the pitch in time under conditions of strong turbulence and rapid wind speed changes. The lack of coordination between wind speed changes and pitch adjustment can lead to unstable wind turbine operation, large speed fluctuations, and consequently power fluctuations, affecting the stability of the power system. Furthermore, the utilization rate of energy storage batteries in current wind power storage systems is low, resulting in a certain degree of resource waste. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a wind power and energy storage system control method, device, equipment, and storage medium based on a genetic algorithm. This method utilizes the automatic optimization capabilities of the genetic algorithm to determine the optimal control strategy parameters for the wind turbine and energy storage system, improving the stability of the power system. Furthermore, by leveraging the rapid charging / discharging characteristics of energy storage, it can supplement the power output of the turbine during power fluctuations, further enhancing the stability of the turbine's power output. The specific solution is as follows:

[0005] Firstly, this application provides a wind-storage system control method based on a genetic algorithm, including:

[0006] Determine whether the current condition for the genetic algorithm to find the optimal solution is met.

[0007] If the preset genetic algorithm optimization conditions are met, then a number of pitch rate values ​​and a number of energy storage charge / discharge rate values ​​are selected from the preset pitch rate range and the preset energy storage charge / discharge rate range to obtain the initial data population.

[0008] The initial data population is processed using a genetic algorithm to obtain the target data population.

[0009] Determine whether the target data population meets the preset iteration termination condition;

[0010] If the target data population satisfies the preset iteration termination condition, the target pitch rate and the target energy storage charge / discharge rate are determined from the target data population so that the wind-storage system can operate according to the target pitch rate and the target energy storage charge / discharge rate.

[0011] Optionally, determining whether the current condition for optimization by the genetic algorithm is met includes:

[0012] Determine whether the current speed fluctuation exceeds a preset speed fluctuation threshold;

[0013] If the current rotational speed fluctuation is greater than the preset rotational speed fluctuation threshold, then determine whether the current turbulence intensity is greater than the preset turbulence intensity threshold.

[0014] If the current turbulence intensity is greater than the preset turbulence intensity threshold, then the current condition for the preset genetic algorithm optimization is determined to be met.

[0015] Optionally, the step of performing calculations on the initial data population based on a genetic algorithm includes:

[0016] The initial data population is subjected to a preset operator operation method based on a genetic algorithm; the preset operator operation method includes a selection operator operation method, a crossover operator operation method, and a mutation operator operation method.

[0017] Optionally, before determining whether the target data population meets the preset iteration termination condition, the method further includes:

[0018] The fitness value of the target data individuals in the target data population is determined using a preset fitness evaluation method;

[0019] Based on the fitness value of the target data individual, determine whether the target data population satisfies the preset condition that an optimal individual exists;

[0020] If the target data population does not meet the preset condition for the existence of an optimal individual, then the process jumps back to the step of selecting several pitch rate values ​​and several energy storage charge / discharge rate values ​​from the preset pitch rate range and the preset energy storage charge / discharge rate range until the preset condition for the existence of an optimal individual is met.

[0021] Optionally, determining whether the target data population satisfies the preset iteration termination condition includes:

[0022] Determine whether the current iteration number of the target data population is greater than a preset iteration number threshold;

[0023] If the current iteration number is greater than the preset iteration number threshold, then the target data population is determined to meet the preset iteration termination condition;

[0024] Alternatively, determine whether the target data population satisfies the preset optimal individual change conditions;

[0025] If the target data population satisfies the preset optimal individual change condition, then the target data population is determined to satisfy the preset iteration termination condition.

[0026] Optionally, determining whether the target data population satisfies the preset optimal individual change conditions includes:

[0027] The fitness value of the optimal individual is selected from the fitness values ​​of the target data individuals;

[0028] Determine whether the fitness value of the optimal individual satisfies the preset optimal fitness value change condition;

[0029] If the fitness value of the optimal individual satisfies the preset optimal fitness value change condition, then the target data population is determined to satisfy the preset optimal individual change condition.

[0030] Optionally, the step of selecting several pitch rate values ​​and several energy storage charge / discharge rate values ​​from a preset pitch rate range and a preset energy storage charge / discharge rate range to obtain an initial data population includes:

[0031] Based on a preset screening method, several pitch rate values ​​and several energy storage charge / discharge rate values ​​are selected from a preset pitch rate range and a preset energy storage charge / discharge rate range.

[0032] Each selected pitch rate value and each energy storage charge / discharge rate value are encoded, and the encoded pitch rate value and each encoded energy storage charge / discharge rate value are combined to obtain several initial data individuals.

[0033] The aforementioned initial data individuals are defined as the initial data population.

[0034] Secondly, this application provides a wind-storage system control device based on a genetic algorithm, comprising:

[0035] The first condition judgment module is used to determine whether the preset genetic algorithm optimization conditions are met.

[0036] The initial data population acquisition module is used to select a number of pitch rate values ​​and a number of energy storage charge-discharge rate values ​​from a preset pitch rate range and a preset energy storage charge-discharge rate range if the current conditions for optimization by the preset genetic algorithm are met, so as to obtain the initial data population.

[0037] The target data population acquisition module is used to perform calculations on the initial data population based on a genetic algorithm to obtain the target data population;

[0038] The second condition judgment module is used to determine whether the target data population meets the preset iteration termination condition;

[0039] The target parameter determination module is used to determine the target pitch rate and the target energy storage charge / discharge rate from the target data population if the target data population meets the preset iteration termination condition, so that the wind storage system can operate according to the target pitch rate and the target energy storage charge / discharge rate.

[0040] Thirdly, this application provides an electronic device, comprising:

[0041] Memory, used to store computer programs;

[0042] A processor is used to execute the computer program to implement the aforementioned wind storage system control method based on genetic algorithms.

[0043] Fourthly, this application provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the aforementioned wind-storage system control method based on a genetic algorithm.

[0044] In this application, it is determined whether the current condition for optimization using a preset genetic algorithm is met. If the condition is met, several pitch rate values ​​and several energy storage charge / discharge rate values ​​are selected from a preset pitch rate range and a preset energy storage charge / discharge rate range to obtain an initial data population. The initial data population is then processed using a genetic algorithm to obtain a target data population. It is then determined whether the target data population meets a preset iteration termination condition. If the target data population meets the preset iteration termination condition, a target pitch rate and a target energy storage charge / discharge rate are determined from the target data population, so that the wind-storage system operates according to the target pitch rate and the target energy storage charge / discharge rate. Through this scheme, a target pitch rate and a target energy storage charge / discharge rate are determined from a preset pitch rate range and a preset energy storage charge / discharge rate range using a genetic algorithm, so that the wind-storage system operates according to the target pitch rate and the target energy storage charge / discharge rate. In this way, the optimal control strategy parameters for wind turbines and energy storage systems can be determined by using the automatic optimization feature of genetic algorithms. This avoids power fluctuations caused by unreasonable pitch control due to wind speed uncertainty at wind speeds above the rated wind speed, reduces the impact on the power grid, improves the stability of the power system, and utilizes the rapid charging / discharging characteristics of energy storage to supplement the unit's power output when the unit's power fluctuates, thereby improving the stability of the unit's power output and the utilization rate of energy storage. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0046] Figure 1 A flowchart of a wind-storage system control method based on a genetic algorithm is provided for this application;

[0047] Figure 2 A line graph illustrating the speed-torque relationship during the operation of a wind turbine generator is provided in this application.

[0048] Figure 3 A flowchart of charging / discharging compensation for a wind storage system is provided for this application;

[0049] Figure 4 A flowchart of a genetic algorithm provided in this application;

[0050] Figure 5 A flowchart illustrating a specific wind-storage system control method based on a genetic algorithm provided in this application;

[0051] Figure 6 A schematic diagram of a wind-storage system control device based on a genetic algorithm provided for this application;

[0052] Figure 7 This application provides a structural diagram of an electronic device. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Currently, in the field of wind power generation, the pitch control strategies used both domestically and internationally can cause unstable wind turbine operation and large speed fluctuations in strong turbulence and rapid wind speed changes, resulting in power fluctuations and affecting the stability of the power system. Furthermore, the utilization rate of energy storage batteries in current wind-storage systems is low, leading to resource waste. Therefore, this application proposes a wind-storage system control method based on a genetic algorithm. This method utilizes the automatic optimization capabilities of the genetic algorithm to determine the optimal control strategy parameters for the wind turbine and energy storage system, improving the stability of the power system. Moreover, leveraging the rapid charging / discharging characteristics of energy storage, it can supplement the turbine's power output during power fluctuations, thereby improving the stability of the turbine's power output and the utilization rate of energy storage.

[0055] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a wind-storage system control method based on a genetic algorithm, comprising:

[0056] Step S11: Determine whether the current condition for the genetic algorithm to find the optimal solution is met.

[0057] In this embodiment, it should be noted that determining whether the current condition for the preset genetic algorithm optimization is met can specifically include: determining whether the current rotational speed fluctuation is greater than a preset rotational speed fluctuation threshold; if the current rotational speed fluctuation is greater than the preset rotational speed fluctuation threshold, then determining whether the current turbulence intensity is greater than a preset turbulence intensity threshold; if the current turbulence intensity is greater than the preset turbulence intensity threshold, then determining that the current condition for the preset genetic algorithm optimization is met. That is, when both the current rotational speed fluctuation and the current turbulence intensity are greater than their respective preset thresholds, it is determined that the current condition for the preset genetic algorithm optimization is met, so that subsequent operations can be performed.

[0058] Step S12: If the preset genetic algorithm optimization conditions are met, select a number of pitch rate values ​​and a number of energy storage charge / discharge rate values ​​from the preset pitch rate range and the preset energy storage charge / discharge rate range to obtain the initial data population.

[0059] In this embodiment, it can be understood that the output power performance of the wind turbine is mainly determined by its mechanical efficiency, i.e., the power coefficient C. P (λ,β), the power coefficient is mainly related to air density, turbine radius, wind speed, turbine speed, tip speed ratio, and blade pitch angle. The mechanical power of wind energy converted into electrical energy is shown in the following formula:

[0060]

[0061] Based on the principles of electric motors, the mechanical torque of a wind turbine can be calculated as follows:

[0062]

[0063] Where λ is defined as follows:

[0064]

[0065] In the above formula, ρ air R is air density, R is the fan radius, and V is air density. w It's wind speed, ω t It is the fan speed, C P (λ,β) is a nonlinear function relating to the tip speed ratio λ and the blade pitch angle β, representing the power efficiency of the wind turbine.

[0066] like Figure 2 As shown, Figure 2 This is a speed-torque curve diagram of a wind turbine during operation. ω1 represents the grid-connected speed of the turbine, and ω2 represents the rated speed. When the turbine reaches the grid-connected speed ω1, it begins to generate electricity, and the torque instantaneously increases from... Figure 2 Point A in the diagram reaches point B. As wind speed increases, rotational speed and torque increase proportionally, such as... Figure 2 Section BC. When the generator speed rises to the rated speed ω2, if the wind speed continues to increase, it will enter section CD for constant speed operation, and the unit will increase power output by increasing torque. When it reaches... Figure 2 Point D in the diagram indicates that the wind speed is at the rated wind speed, the wind turbine output power is at the rated power, and the current state is full-power output.

[0067] When wind speed increases above the rated wind speed, according to the mechanical power formula for converting wind energy into electrical energy, the turbine power will also increase. To ensure rated power output, the wind-storage system will control the pitch system to retract the blades, reducing the wind-receiving area of ​​the blades to maintain stable power output. When wind speed decreases above the rated wind speed, the turbine power will decrease. At this time, the pitch system will open the blades to increase the wind-receiving area of ​​the blades to maintain power output.

[0068] Due to the uncertainty and intermittency of wind speed, and the increased turbulence in strong winds causing repeated increases and decreases in wind speed, the pitch control system cannot predict these changes. This leads to repeated adjustments in pitch control, with the speed of these adjustments determined by the pitch rate. Therefore, an inappropriate pitch rate cannot guarantee stable power output from the generator unit, and power fluctuations will impact the power grid, thus affecting the stability of the power system. Therefore, when wind speed varies above the rated wind speed, the generator unit's power output is related to both wind speed and pitch rate.

[0069] Furthermore, the energy storage system in the wind-storage system has the characteristic of rapid charging / discharging. When the wind speed increases above the rated wind speed, the unit power increases. At this time, the blades retract, and the excess power caused by the blade retraction can be used for energy storage charging to ensure stable unit power output. When the wind speed decreases above the rated wind speed, the unit power decreases. At this time, the stored energy can be discharged to maintain stable unit power output. Figure 3 As shown, Figure 3 This is a flowchart of the charge / discharge compensation process for a wind power storage system. It shows that, at a certain pitch rate, the pitch system can maintain stable power output by combining the energy storage charge / discharge rate. In this way, utilizing energy storage charge / discharge can mitigate mechanical fatigue damage caused by repeated rapid pitch changes, avoid power fluctuations, and improve grid stability. The energy storage charge / discharge rate, represented by the letter C, indicates the ratio of the charge / discharge current of the energy storage battery, and the formula is shown below:

[0070]

[0071] In the above formula, C represents the energy storage charge / discharge rate, I represents the energy storage charge / discharge current, and C n This indicates the rated capacity of the energy storage.

[0072] In this way, this application can maintain stable power output of the generator unit and improve the utilization rate of energy storage by determining the pitch rate and energy storage charge / discharge rate of the unit that meet preset conditions. It should be noted that the step of selecting a number of pitch rate values ​​and energy storage charge / discharge rate values ​​from the preset pitch rate range and preset energy storage charge / discharge rate range to obtain an initial data population can specifically include: selecting a number of pitch rate values ​​and energy storage charge / discharge rate values ​​from the preset pitch rate range and preset energy storage charge / discharge rate range based on a preset screening method; encoding each selected pitch rate value and each energy storage charge / discharge rate value and combining the encoded pitch rate value and the encoded energy storage charge / discharge rate value to obtain a number of initial data individuals; and determining the number of initial data individuals as the initial data population. The above scheme combines several selected pitch rate values ​​and several energy storage charge / discharge rate values ​​to obtain several initial data individuals and determine an initial data population, so that the target pitch rate value and target energy storage charge / discharge rate value can be obtained by operating on the initial data population based on a genetic algorithm.

[0073] Step S13: Perform calculations on the initial data population based on a genetic algorithm to obtain the target data population.

[0074] In this embodiment, it can be understood that the operation on the initial data population based on the genetic algorithm specifically includes: performing a preset operator operation method on the initial data population based on the genetic algorithm; the preset operator operation method includes a selection operator operation method, a crossover operator operation method, and a mutation operator operation method. The flowchart of the genetic algorithm is as follows: Figure 4 As shown, the fitness of individuals in the original population is evaluated, and individuals that meet the requirements are selected. The operator operations in the genetic algorithm are then performed to generate a new population. It is then determined from the new population whether there is a globally optimal solution that meets the preset relevant conditions. If so, the globally optimal solution is output. Otherwise, individuals in the new population are selected, and the subsequent operations are performed again until a globally optimal solution that meets the preset relevant conditions is determined from the current population.

[0075] Step S14: Determine whether the target data population meets the preset iteration termination condition.

[0076] In this embodiment, considering the need to improve the operating efficiency of the wind storage system, it is possible to determine whether the target data population meets the preset iteration termination condition, and to determine whether to execute the operation of step S15 based on the determination result.

[0077] Step S15: If the target data population satisfies the preset iteration termination condition, then the target pitch rate and the target energy storage charge / discharge rate are determined from the target data population so that the wind-storage system can operate according to the target pitch rate and the target energy storage charge / discharge rate.

[0078] In this embodiment, if the target data population meets the preset iteration termination condition, the optimal individual is determined from the target data population, and the target pitch rate and target energy storage charge / discharge rate are determined based on the optimal individual, so that the wind storage system can operate according to the target pitch rate and the target energy storage charge / discharge rate.

[0079] In this embodiment, it is determined whether the current condition for optimization using a preset genetic algorithm is met. If the condition is met, several pitch rate values ​​and several energy storage charge / discharge rate values ​​are selected from a preset pitch rate range and a preset energy storage charge / discharge rate range to obtain an initial data population. The initial data population is then processed using a genetic algorithm to obtain a target data population. It is then determined whether the target data population meets a preset iteration termination condition. If the target data population meets the preset iteration termination condition, a target pitch rate and a target energy storage charge / discharge rate are determined from the target data population, so that the wind-storage system operates according to the target pitch rate and the target energy storage charge / discharge rate. Through this scheme, a target pitch rate and a target energy storage charge / discharge rate are determined from a preset pitch rate range and a preset energy storage charge / discharge rate range using a genetic algorithm, so that the wind-storage system operates according to the target pitch rate and the target energy storage charge / discharge rate. In this way, the optimal control strategy parameters for wind turbines and energy storage systems can be determined by using the automatic optimization feature of genetic algorithms. This avoids power fluctuations caused by unreasonable pitch control due to wind speed uncertainty at wind speeds above the rated wind speed, reduces the impact on the power grid, improves the stability of the power system, and utilizes the rapid charging / discharging characteristics of energy storage to supplement the unit's power output when the unit's power fluctuates, thereby improving the stability of the unit's power output and the utilization rate of energy storage.

[0080] See Figure 5 As shown in the figure, this invention discloses a specific wind-storage system control method based on a genetic algorithm, including:

[0081] Step S21: Determine whether the current condition for the genetic algorithm to find the optimal solution is met.

[0082] Step S22: If the preset genetic algorithm optimization conditions are met, select a number of pitch rate values ​​and a number of energy storage charge / discharge rate values ​​from the preset pitch rate range and the preset energy storage charge / discharge rate range to obtain the initial data population.

[0083] In this embodiment, the range of pitch rate and energy storage charge / discharge rate is preset. If the preset genetic algorithm optimization conditions are met, a number of pitch rate values ​​and a number of energy storage charge / discharge rate values ​​are selected from the preset pitch rate range and the preset energy storage charge / discharge rate range to obtain an initial data population.

[0084] Step S23: Perform calculations on the initial data population based on a genetic algorithm to obtain the target data population.

[0085] Step S24: Determine the fitness value of the target data individuals in the target data population using a preset fitness evaluation method.

[0086] In this embodiment, considering the need to determine whether the optimization results obtained based on the genetic algorithm meet the requirements, the power deviation can be used as the fitness evaluation function, and a corresponding fitness evaluation method can be determined to determine the fitness value of each target data individual in the target data population.

[0087] Step S25: Determine whether the target data population satisfies the preset condition of having an optimal individual based on the fitness value of the target data individual.

[0088] In this embodiment, after determining the fitness value of each target data individual in the target data population, it is determined whether the target data population satisfies the preset condition of the existence of an optimal individual. For example, the fitness value of the data individual is preset to be in the range of (5, 10). If the fitness value of all target data individuals in the target data population is not greater than 5, it is determined that the target data population does not satisfy the preset condition of the existence of an optimal individual. At this time, the initial data population can be re-determined and corresponding processing can be performed.

[0089] Step S26: If the target data population satisfies the preset condition of having an optimal individual, then determine whether the target data population satisfies the preset iteration termination condition.

[0090] In this embodiment, it can be understood that the determination of whether the target data population meets the preset iteration termination condition includes, but is not limited to, the following two specific implementation methods.

[0091] In a first specific embodiment, determining whether the target data population meets the preset iteration termination condition may specifically include: determining whether the current iteration number of the target data population is greater than a preset iteration number threshold; if the current iteration number is greater than the preset iteration number threshold, then the target data population is determined to meet the preset iteration termination condition. For example, the iteration number threshold can be preset to 5 times, then when the current iteration number is 6, the target data population is determined to meet the preset iteration termination condition.

[0092] In a second specific implementation, determining whether the target data population satisfies the preset iteration termination condition may specifically include: determining whether the target data population satisfies the preset optimal individual change condition; if the target data population satisfies the preset optimal individual change condition, then determining that the target data population satisfies the preset iteration termination condition.

[0093] It should be noted that determining whether the target data population satisfies the preset optimal individual change condition can specifically include: selecting the fitness value of the optimal individual from the fitness values ​​of the target data individuals; determining whether the fitness value of the optimal individual satisfies the preset optimal fitness value change condition; if the fitness value of the optimal individual satisfies the preset optimal fitness value change condition, then the target data population is determined to satisfy the preset optimal individual change condition. For example, if the change in the fitness value of the optimal individual in the target data population does not exceed 2 in three consecutive iterations, then the target data population can be determined to satisfy the preset optimal individual change condition, thereby determining that the target data population satisfies the preset iteration termination condition.

[0094] Step S27: If the target data population satisfies the preset iteration termination condition, then the target pitch rate and the target energy storage charge / discharge rate are determined from the target data population so that the wind-storage system can operate according to the target pitch rate and the target energy storage charge / discharge rate.

[0095] For specific implementation methods of steps S21, S23 and S27, please refer to the corresponding disclosures in the foregoing embodiments, which will not be repeated here.

[0096] In this embodiment, it is determined whether the current condition for optimization by a preset genetic algorithm is met. If the condition is met, several pitch rate values ​​and several energy storage charge / discharge rate values ​​are selected from a preset pitch rate range and a preset energy storage charge / discharge rate range to obtain an initial data population. The initial data population is then processed using a genetic algorithm to obtain a target data population. A preset fitness evaluation method is used to determine the fitness value of the target data individuals in the target data population. Based on the fitness value of the target data individuals, it is determined whether the target data population meets the preset condition for the existence of an optimal individual. If the target data population meets the preset condition for the existence of an optimal individual, it is determined whether the target data population meets the preset iteration termination condition. If the target data population meets the preset iteration termination condition, the target pitch rate and target energy storage charge / discharge rate are determined from the target data population so that the wind-storage system can operate according to the target pitch rate and the target energy storage charge / discharge rate. By using the above scheme, before determining whether the target data population meets the preset iteration termination condition, it is first determined whether the target data population meets the preset condition of the existence of an optimal individual. In this way, iteration and optimization are avoided for target data populations that do not meet the preset condition of the existence of an optimal individual, thereby improving the optimization efficiency of the algorithm and improving the effect of the optimization results.

[0097] See Figure 6 As shown, this application discloses a wind-storage system control device based on a genetic algorithm, comprising:

[0098] The first condition judgment module 11 is used to determine whether the preset genetic algorithm optimization conditions are met.

[0099] The initial data population acquisition module 12 is used to select a number of pitch rate values ​​and a number of energy storage charge-discharge rate values ​​from the preset pitch rate range and the preset energy storage charge-discharge rate range if the current conditions for optimization of the preset genetic algorithm are met, so as to obtain the initial data population.

[0100] The target data population acquisition module 13 is used to perform calculations on the initial data population based on a genetic algorithm to obtain the target data population;

[0101] The second condition judgment module 14 is used to determine whether the target data population satisfies the preset iteration termination condition.

[0102] The target parameter determination module 15 is used to determine the target pitch rate and the target energy storage charge / discharge rate from the target data population if the target data population meets the preset iteration termination condition, so that the wind storage system can operate according to the target pitch rate and the target energy storage charge / discharge rate.

[0103] In this application, it is determined whether the current condition for optimization using a preset genetic algorithm is met. If the condition is met, several pitch rate values ​​and several energy storage charge / discharge rate values ​​are selected from a preset pitch rate range and a preset energy storage charge / discharge rate range to obtain an initial data population. The initial data population is then processed using a genetic algorithm to obtain a target data population. It is then determined whether the target data population meets a preset iteration termination condition. If the target data population meets the preset iteration termination condition, a target pitch rate and a target energy storage charge / discharge rate are determined from the target data population, so that the wind-storage system operates according to the target pitch rate and the target energy storage charge / discharge rate. Through this scheme, a target pitch rate and a target energy storage charge / discharge rate are determined from a preset pitch rate range and a preset energy storage charge / discharge rate range using a genetic algorithm, so that the wind-storage system operates according to the target pitch rate and the target energy storage charge / discharge rate. In this way, the optimal control strategy parameters for wind turbines and energy storage systems can be determined by using the automatic optimization feature of genetic algorithms. This avoids power fluctuations caused by unreasonable pitch control due to wind speed uncertainty at wind speeds above the rated wind speed, reduces the impact on the power grid, improves the stability of the power system, and utilizes the rapid charging / discharging characteristics of energy storage to supplement the unit's power output when the unit's power fluctuates, thereby improving the stability of the unit's power output and the utilization rate of energy storage.

[0104] In some specific embodiments, the first condition judgment module 11 may specifically include:

[0105] The speed fluctuation judgment unit is used to determine whether the current speed fluctuation is greater than the preset speed fluctuation threshold;

[0106] The turbulence intensity determination unit is used to determine whether the current turbulence intensity is greater than the preset turbulence intensity threshold if the current rotational speed fluctuation is greater than the preset rotational speed fluctuation threshold.

[0107] The first judgment result determination unit is used to determine that the current turbulence intensity is greater than the preset turbulence intensity threshold, and thus the current condition for the genetic algorithm optimization is met.

[0108] In some specific embodiments, the target data population acquisition module 13 may specifically include:

[0109] The preset operation method execution unit is used to execute preset operator operation methods on the initial data population based on a genetic algorithm; the preset operator operation methods include selection operator operation methods, crossover operator operation methods, and mutation operator operation methods.

[0110] In some specific embodiments, the wind-storage system control device based on genetic algorithms may further include:

[0111] A fitness value determination unit is used to determine the fitness value of a target data individual in the target data population using a preset fitness evaluation method.

[0112] A preset optimal individual condition judgment unit is used to determine whether the target data population satisfies the preset optimal individual condition based on the fitness value of the target data individual.

[0113] The step jump unit is used to jump back to the step of selecting several pitch rate values ​​and several energy storage charge / discharge rate values ​​from the preset pitch rate range and the preset energy storage charge / discharge rate range if the target data population does not meet the preset condition for the existence of optimal individuals, until the preset condition for the existence of optimal individuals is met.

[0114] In some specific embodiments, the second condition judgment module 14 may specifically include:

[0115] The first termination condition judgment submodule is used to determine whether the current iteration number of the target data population is greater than a preset iteration number threshold.

[0116] The first termination condition determination result determination unit is used to determine that the target data population satisfies the preset iteration termination condition if the current iteration number is greater than the preset iteration number threshold.

[0117] The second termination condition judgment submodule is used to determine whether the target data population satisfies the preset optimal individual change condition;

[0118] The second termination condition determination unit is used to determine that the target data population satisfies the preset iteration termination condition if the target data population satisfies the preset optimal individual change condition.

[0119] In some specific embodiments, the second termination condition determination submodule may specifically include:

[0120] The optimal individual fitness value filtering unit is used to filter out the fitness value of the optimal individual from the fitness values ​​of the target data individuals;

[0121] The third condition judgment unit is used to determine whether the fitness value of the optimal individual meets the preset optimal fitness value change condition;

[0122] The third judgment result determination unit is used to determine that the target data population satisfies the preset optimal individual change condition if the fitness value of the optimal individual satisfies the preset optimal fitness value change condition.

[0123] In some specific embodiments, the initial data population acquisition module 12 may specifically include:

[0124] The parameter filtering unit is used to filter out several pitch rate values ​​and several energy storage charge / discharge rate values ​​from a preset pitch rate range and a preset energy storage charge / discharge rate range based on a preset filtering method.

[0125] The initial data individual determination unit is used to encode each selected pitch rate value and each energy storage charge / discharge rate value, and combine the encoded pitch rate value and the encoded energy storage charge / discharge rate value to obtain several initial data individuals.

[0126] An initial data population determination unit is used to determine the plurality of initial data individuals as an initial data population.

[0127] Furthermore, embodiments of this application also disclose an electronic device, Figure 7 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0128] The figure is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the wind-storage system control method based on genetic algorithms disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0129] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0130] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0131] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the genetic algorithm-based wind and energy storage system control method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0132] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned wind-storage system control method based on a genetic algorithm. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0133] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0134] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0135] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0136] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0137] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A wind-storage system control method based on genetic algorithm, characterized in that, include: Determine whether the current condition for the genetic algorithm to find the optimal solution is met. If the preset genetic algorithm optimization conditions are met, then a number of pitch rate values ​​and a number of energy storage charge / discharge rate values ​​are selected from the preset pitch rate range and the preset energy storage charge / discharge rate range to obtain the initial data population. The initial data population is processed using a genetic algorithm to obtain the target data population. Determine whether the target data population meets the preset iteration termination condition; If the target data population satisfies the preset iteration termination condition, the target pitch rate and the target energy storage charge / discharge rate are determined from the target data population so that the wind-storage system can operate according to the target pitch rate and the target energy storage charge / discharge rate. The determination of whether the preset genetic algorithm optimization conditions are met includes: Determine whether the current speed fluctuation exceeds a preset speed fluctuation threshold; If the current rotational speed fluctuation is greater than the preset rotational speed fluctuation threshold, then determine whether the current turbulence intensity is greater than the preset turbulence intensity threshold. If the current turbulence intensity is greater than the preset turbulence intensity threshold, then the current condition for the preset genetic algorithm optimization is met. The step of selecting several pitch rate values ​​and several energy storage charge / discharge rate values ​​from a preset pitch rate range and a preset energy storage charge / discharge rate range to obtain an initial data population includes: Based on a preset screening method, several pitch rate values ​​and several energy storage charge / discharge rate values ​​are selected from a preset pitch rate range and a preset energy storage charge / discharge rate range. Each selected pitch rate value and each energy storage charge / discharge rate value are encoded, and the encoded pitch rate value and each encoded energy storage charge / discharge rate value are combined to obtain several initial data individuals. The aforementioned initial data individuals are defined as the initial data population.

2. The wind-storage system control method based on genetic algorithm according to claim 1, characterized in that, The operation on the initial data population based on the genetic algorithm includes: The initial data population is subjected to a preset operator operation method based on a genetic algorithm; the preset operator operation method includes a selection operator operation method, a crossover operator operation method, and a mutation operator operation method.

3. The wind-storage system control method based on genetic algorithm according to claim 1, characterized in that, Before determining whether the target data population meets the preset iteration termination condition, the method further includes: The fitness value of the target data individuals in the target data population is determined using a preset fitness evaluation method; Based on the fitness value of the target data individual, determine whether the target data population satisfies the preset condition that an optimal individual exists; If the target data population does not meet the preset condition for the existence of an optimal individual, then the process jumps back to the step of selecting several pitch rate values ​​and several energy storage charge / discharge rate values ​​from the preset pitch rate range and the preset energy storage charge / discharge rate range until the preset condition for the existence of an optimal individual is met.

4. The wind-storage system control method based on genetic algorithm according to claim 3, characterized in that, The step of determining whether the target data population meets the preset iteration termination condition includes: Determine whether the current iteration number of the target data population is greater than a preset iteration number threshold; If the current iteration number is greater than the preset iteration number threshold, then the target data population is determined to meet the preset iteration termination condition; Alternatively, determine whether the target data population satisfies the preset optimal individual change conditions; If the target data population satisfies the preset optimal individual change condition, then the target data population is determined to satisfy the preset iteration termination condition.

5. The wind-storage system control method based on genetic algorithm according to claim 4, characterized in that, The step of determining whether the target data population meets the preset optimal individual change conditions includes: The fitness value of the optimal individual is selected from the fitness values ​​of the target data individuals; Determine whether the fitness value of the optimal individual satisfies the preset optimal fitness value change condition; If the fitness value of the optimal individual satisfies the preset optimal fitness value change condition, then the target data population is determined to satisfy the preset optimal individual change condition.

6. A wind-storage system control device based on a genetic algorithm, characterized in that, include: The first condition judgment module is used to determine whether the preset genetic algorithm optimization conditions are met. The initial data population acquisition module is used to select a number of pitch rate values ​​and a number of energy storage charge-discharge rate values ​​from a preset pitch rate range and a preset energy storage charge-discharge rate range if the current conditions for optimization by the preset genetic algorithm are met, so as to obtain the initial data population. The target data population acquisition module is used to perform calculations on the initial data population based on a genetic algorithm to obtain the target data population; The second condition judgment module is used to determine whether the target data population meets the preset iteration termination condition; The target parameter determination module is used to determine the target pitch rate and the target energy storage charge / discharge rate from the target data population if the target data population meets the preset iteration termination condition, so that the wind storage system can operate according to the target pitch rate and the target energy storage charge / discharge rate. The first condition judgment module includes: The speed fluctuation judgment unit is used to determine whether the current speed fluctuation is greater than the preset speed fluctuation threshold; The turbulence intensity determination unit is used to determine whether the current turbulence intensity is greater than the preset turbulence intensity threshold if the current rotational speed fluctuation is greater than the preset rotational speed fluctuation threshold. The first judgment result determination unit is used to determine that the current turbulence intensity is greater than the preset turbulence intensity threshold, and thus the current condition for the genetic algorithm optimization is met. The initial data population acquisition module includes: The parameter filtering unit is used to filter out several pitch rate values ​​and several energy storage charge / discharge rate values ​​from a preset pitch rate range and a preset energy storage charge / discharge rate range based on a preset filtering method. The initial data individual determination unit is used to encode each selected pitch rate value and each energy storage charge / discharge rate value, and combine the encoded pitch rate value and the encoded energy storage charge / discharge rate value to obtain several initial data individuals. An initial data population determination unit is used to determine the plurality of initial data individuals as an initial data population.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the wind storage system control method based on genetic algorithm as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the wind storage system control method based on a genetic algorithm as described in any one of claims 1 to 5.

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