Energy storage converter optimization control method based on fusion genetic-particle swarm optimization and related device

Abstract

The invention provides an energy storage converter optimization control method based on a fusion genetic-particle swarm algorithm and a related device, and belongs to the technical field of oscillation suppression of energy storage converters. According to the method, a virtual synchronous machine simulation model is established, a second-order motion equation of the synchronous machine is analyzed to obtain a selection principle of rotational inertia and a damping coefficient, then a related control strategy function is constructed, and parameters of the control strategy function are optimized by using a fusion genetic-particle swarm optimization algorithm. Differential adjustment coefficient configuration can be implemented according to specific requirements of different power system scenes on overshoot and response time, the control strategy strain capacity is effectively improved, and the system instability risk is reduced; and meanwhile, the global search capability of the genetic algorithm and the rapid convergence characteristic of the particle swarm algorithm are fully exerted, the function parameters of the control strategy are accurately optimized, the stable regulation and control capability of the energy storage converter in the power system is enhanced, the low-frequency oscillation of the virtual synchronous machine is effectively suppressed, and the stability and dynamic regulation performance of the energy storage converter are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oscillation suppression of energy storage converters, and particularly relates to an optimized control method and related device for an energy storage converter based on a fusion genetic-particle swarm algorithm. Background Technique

[0002] In the power system, the grid-forming energy storage converter plays a key role in promoting the integration of renewable energy and the modernization of the power grid. At present, the oscillation problem in the virtual synchronous generator control strategy has been widely explored and studied. Compared with the traditional synchronous generator, the virtual synchronous machine can flexibly adjust the moment of inertia J and the damping coefficient D through its advanced control algorithm. By adjusting the values of J and D, the stability of the entire power system can be significantly improved, thereby enhancing the system's response ability to emergencies and ensuring the reliability of the power grid operation.

[0003] At present, the oscillation suppression methods of energy storage converters are becoming increasingly diverse, mainly divided into two categories: the method of solving the fixed values of J and D by the optimization algorithm offline and the method of setting the fixed piecewise function adaptive control of J and D. The method of solving the fixed values of J and D by the optimization algorithm offline usually uses neural networks, grey wolf algorithms, and sparrow algorithms to solve a set of J and D values that make the active power and frequency output of the system have the best performance. The defect of this method is that it cannot dynamically adjust the values of J and D, and the overall stability effect of the energy storage converter is poor. Although the method of setting the fixed piecewise function control of J and D can dynamically adjust the values of J and D during the disturbance suppression process, the J and D values calculated according to the fixed piecewise function of J and D are not necessarily the optimal solutions within a J and D value change period of the virtual synchronous generator (VSG), which will also cause poor dynamic performance of the active power and frequency output of the energy storage converter system. Summary of the Invention

[0004] In view of this, the present invention aims to provide an optimized control method and related device for an energy storage converter based on a fusion genetic-particle swarm algorithm to solve the above-mentioned deficiencies existing in the existing oscillation suppression methods of energy storage converters.

[0005] To achieve the above object, the technical solutions provided by the present invention are as follows:

[0006] In the first aspect, the present invention provides an optimized control method for an energy storage converter based on a fusion genetic-particle swarm algorithm, including the following steps:

[0007] Establish a simulation model of the virtual synchronous machine, write the second-order motion equation of the synchronous machine, and obtain the selection principles of the moment of inertia and the damping coefficient under different angular frequency deviations and change rates; the selection principles are obtained according to the change rules summarized when adjusting the moment of inertia and the damping coefficient to suppress the changes of the angular frequency deviation and the angular frequency change rate in the second-order motion equation of the synchronous machine;

[0008] According to the selection principles of the moment of inertia and damping coefficient under different angular frequency deviations and change rates, a control strategy function of the energy storage converter related to the angular frequency deviation and angular frequency change rate is constructed by using a set of parameters.

[0009] Based on the simulation model, the fusion genetic-particle swarm algorithm is used to optimize the parameters of the energy storage converter control strategy function, and a set of optimal solutions are obtained and substituted into the parameters in the function to obtain the control strategy function of the moment of inertia and damping coefficient, and the oscillation suppression control under the low-frequency oscillation of the virtual synchronous machine is carried out.

[0010] Furthermore, the control strategy function of the energy storage converter is as follows:

[0011]

[0012]

[0013] In the formula, and are the moment of inertia and damping coefficient respectively, and are the moment of inertia and damping coefficient when the virtual synchronous machine system is in a stable condition respectively, - is the adjustment coefficient, and are the angular frequency deviation threshold and angular frequency change rate deviation threshold respectively, is the angular frequency deviation, is the angular frequency change rate.

[0014] Furthermore, according to the importance requirements of the overshoot and response time in different scenarios, the - size relationship of the adjustment coefficient is set, where the adjustment coefficient weight of is increased to adapt to the adjustment scenario where the required moment of inertia and damping coefficient are adjusted smoothly.

[0015] Furthermore, based on the simulation model, the fusion genetic-particle swarm algorithm is used to optimize the parameters of the energy storage converter control strategy function, including:

[0016] Generate an initial population, and set { , , , , , , , , , } as 10 dimensions in a particle, and randomly initialize the position and velocity of the particle;

[0017] Substitute the parameters represented by each particle in 10 dimensions into the established simulation model of the virtual synchronous machine to run, obtain the simulation operation data of the virtual synchronous machine, and calculate the fitness value of each particle;

[0018] After evaluating the fitness of the particles, select the particles. According to the fitness value, select excellent individuals to enter the next generation population and perform crossover and mutation operations;

[0019] Take the newly generated particles as the particles in the particle swarm, perform position and velocity update operations, and record the historical optimal position and the global optimal position of each particle at the same time. Determine whether the termination condition is reached; if the termination condition is satisfied, end the algorithm, otherwise return to the fitness evaluation step to continue iterative optimization.

[0020] Furthermore, the simulation operation data of the virtual synchronous machine includes frequency change data and active power change data. The fitness function of the particle is as follows:

[0021]

[0022] In the formula, is the fitness value of the particle, is the maximum value reached by the frequency during the frequency fluctuation process, is the maximum value reached by the active power during the active power fluctuation process, is the active power value stably reached after the system disturbance, is the time for the system to return to stability after the disturbance, is the time point of the system disturbance.

[0023] Furthermore, in the mutation operation, a double mutation strategy is adopted for mutation. The double mutation strategy is as follows:

[0024]

[0025] In the formula, is the mutation vector, is the weight factor of the double mutation strategy, is the mutation factor, , , , , and are the target vectors randomly selected from the current population, is the best target vector.

[0026] Furthermore, the selection principles of the moment of inertia and damping coefficient under different angular frequency deviations and change rates include:

[0027] When the angular frequency deviation and the angular frequency change rate and When both are greater than zero, both the moment of inertia and the damping coefficient increase;

[0028] When the angular frequency deviation is greater than zero, and the angular frequency change rate and are both less than zero, both the moment of inertia and the damping coefficient decrease;

[0029] When the angular frequency deviation and the angular frequency change rate are both less than zero, and is greater than zero, both the moment of inertia and the damping coefficient increase;

[0030] When the angular frequency deviation and are both less than zero, and the angular frequency change rate is greater than zero, both the moment of inertia and the damping coefficient decrease.

[0031] In a second aspect, the present invention provides an energy storage converter optimization control device based on a fusion genetic - particle swarm algorithm, including:

[0032] A selection principle determination module, configured to establish a simulation model of a virtual synchronous machine, write the second - order motion equation of the synchronous machine, and obtain the selection principles of the moment of inertia and the damping coefficient under different angular frequency deviations and change rates; the selection principles are obtained according to the change rules summarized when adjusting the moment of inertia and the damping coefficient in the second - order motion equation of the synchronous machine to suppress the changes of the angular frequency deviation and the angular frequency change rate;

[0033] A control strategy function construction module, configured to construct a control strategy function of the energy storage converter related to the angular frequency deviation and the angular frequency change rate of the moment of inertia and the damping coefficient by using a set of parameters according to the selection principles of the moment of inertia and the damping coefficient under different angular frequency deviations and change rates;

[0034] An algorithm optimization and control module, configured to optimize the parameters of the energy storage converter control strategy function based on the simulation model by using the fusion genetic - particle swarm algorithm, obtain a set of optimal solutions, substitute them into the parameters in the function, obtain the control strategy function of the moment of inertia and the damping coefficient, and perform oscillation suppression control under low - frequency oscillation of the virtual synchronous machine.

[0035] In a third aspect, the present invention provides a computer device, which includes a processor and a memory:

[0036] The memory is used to store a computer program and send the instructions of the computer program to the processor;

[0037] The processor executes an energy storage converter optimization control method based on a fusion genetic - particle swarm algorithm as described in the first aspect according to the instructions of the computer program.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, an energy storage converter optimization control method based on a fusion genetic-particle swarm algorithm as in the first aspect is implemented.

[0039] In summary, the present invention provides an energy storage converter optimization control method and related devices based on the fusion of genetic-particle swarm algorithm, establishes a virtual synchronous machine simulation model, analyzes the second-order motion equation of the synchronous machine to obtain the selection principle of the moment of inertia and the damping coefficient, and then constructs the relevant control strategy function, and uses the fusion of genetic-particle swarm algorithm to optimize the control strategy function parameters. This enables the method to implement differentiated adjustment coefficient configuration according to the specific requirements of overshoot and response time in different power system scenarios, effectively improve the control strategy adaptability, and reduce the risk of system instability; at the same time, give full play to the global search ability of the genetic algorithm and the fast convergence characteristics of the particle swarm algorithm, accurately optimize the control strategy function parameters, enhance the stable regulation ability of the energy storage converter in the power system, achieve effective suppression of the low-frequency oscillation of the virtual synchronous machine, and improve the stability and dynamic regulation performance of the energy storage converter. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 An implementation flow chart of an energy storage converter optimization control method based on a fusion genetic-particle swarm algorithm provided in an embodiment of the present invention;

[0042] Figure 2 A comparison diagram of the frequency optimization control results of the method of the present invention provided in an embodiment of the present invention and the results of the J and D fixed piecewise function calculation method;

[0043] Figure 3 A comparison diagram of the results of optimizing active power control by the method of the present invention provided in an embodiment of the present invention and the results of the J and D fixed piecewise function calculation method;

[0044] Figure 4 A block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] To make the objectives, features, and advantages of the present invention more obvious and understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below 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 fall within the protection scope of the present invention.

[0046] An embodiment of the present invention provides an optimized control method for an energy storage converter based on a hybrid genetic-particle swarm algorithm, including the following steps:

[0047] Step 1: Establish a simulation model of a virtual synchronous machine, list the second-order motion equation of the synchronous machine, and obtain the selection principles of the moment of inertia and damping coefficient under different angular frequency deviations and change rates; the selection principles are obtained according to the change rules summarized when adjusting the moment of inertia and damping coefficient in the second-order motion equation of the synchronous machine to suppress the changes in angular frequency deviation and angular frequency change rate.

[0048] It should be noted that the virtual synchronous generator (VSG) is a technology that simulates the operating characteristics of a traditional synchronous generator. By establishing its mathematical model and power small-signal model to further construct a simulation model, its operating conditions under different working conditions can be simulated in a computer environment. This model can reflect various electrical characteristics and dynamic responses of the virtual synchronous machine, providing a basis for subsequent analysis and control. The second-order motion equation of the synchronous machine is the basic equation describing the rotor motion of the synchronous generator.

[0049] In this step, by analyzing the second-order motion equation of the synchronous machine, the change rules of angular frequency deviation and angular frequency change rate when adjusting the moment of inertia and damping coefficient are studied. According to these rules, the reasonable selection principles of the moment of inertia and damping coefficient under different angular frequency deviations and change rates are summarized. For example, when the angular frequency deviation is large, it may be necessary to increase the moment of inertia to suppress the rapid change of the angular frequency; when the angular frequency change rate is large, it may be necessary to increase the damping coefficient to reduce oscillations.

[0050] Step 2: According to the selection principles of the moment of inertia and damping coefficient under different angular frequency deviations and change rates, use a set of parameters to construct a control strategy function of the energy storage converter related to the moment of inertia, damping coefficient, angular frequency deviation, and angular frequency change rate.

[0051] It should be noted that according to the selection principles of the moment of inertia and damping coefficient under different angular frequency deviations and change rates obtained in Step 1, a set of appropriate parameters are selected to construct a control strategy function of the energy storage converter. This function uses a set of parameters to describe the mathematical relationship between the moment of inertia, damping coefficient, angular frequency deviation, and angular frequency change rate.

[0052] Step 3: Based on the simulation model, use the hybrid genetic-particle swarm optimization algorithm to optimize the parameters of the energy storage converter control strategy function, obtain a set of optimal solutions and substitute them into the parameters in the function to obtain the control strategy function of the moment of inertia and damping coefficient, and perform oscillation suppression control under the low-frequency oscillation of the virtual synchronous machine.

[0053] It should be noted that based on the virtual synchronous machine simulation model established in Step 1, the hybrid genetic-particle swarm optimization algorithm is used to optimize the parameters of the energy storage converter control strategy function constructed in Step 2. The hybrid genetic-particle swarm optimization algorithm combines the global search ability of the genetic algorithm and the fast convergence characteristics of the particle swarm optimization algorithm. Substitute the optimized optimal parameters into the control strategy function to obtain the final control strategy function of the moment of inertia and damping coefficient. In actual operation, according to the real-time angular frequency deviation and change rate, use this control strategy function to dynamically adjust the moment of inertia and damping coefficient, so as to effectively suppress the low-frequency oscillation of the virtual synchronous machine.

[0054] This embodiment provides an optimized control method for an energy storage converter based on a hybrid genetic-particle swarm optimization algorithm. By applying the genetic-particle swarm optimization algorithm to the optimization process of the control strategy function parameters of the moment of inertia J and damping coefficient D, this method combines the global search ability of the genetic algorithm and the fast convergence characteristics of the particle swarm optimization algorithm. The genetic algorithm avoids the algorithm falling into a local optimal solution in the early stage through its crossover and mutation operations.

[0055] Please refer to Figure 1 , Figure 1 is the implementation process of an optimized control method for an energy storage converter based on a hybrid genetic-particle swarm optimization algorithm proposed based on the above embodiment. The following will introduce other embodiments of the present invention in combination with Figure 1 the implementation steps in

[0056] In one embodiment, the selection principles of the moment of inertia and damping coefficient under different angular frequency deviations and change rates are shown in the following table:

[0057] Table 1 Selection principles of moment of inertia and damping coefficient under different angular frequency deviations and change rates under the selection principles of moment of inertia and damping coefficient

[0058]

[0059] In one embodiment, the energy storage converter control strategy functions are as follows (1) and (2):

[0060] (1)

[0061] (2)

[0062] In the formula, and are the moment of inertia and damping coefficient, respectively, and are the moment of inertia and damping coefficient of the virtual synchronous machine system when the working condition is stable, - is the adjustment coefficient, and They are the angular frequency deviation threshold and the angular frequency change rate deviation threshold respectively.

[0063] In one embodiment, according to the importance of overshoot and response time in different scenarios, - The size relationship of the adjustment coefficient is set to control the needs of different scenarios; The waveform has a smoother change characteristic, increasing The adjustment coefficient weight can adapt to the adjustment scenario where the J and D coefficients need to be adjusted more smoothly.

[0064] This embodiment implements differentiated adjustment coefficient configuration to achieve oscillation suppression control in view of the specific requirements of overshoot and response time in different scenarios during power system operation. By carefully analyzing the requirements of various power grid conditions on the dynamic performance of the system, the size relationship of each adjustment coefficient can be set and then optimized to optimize the adjustment process of the moment of inertia J and the damping coefficient D, thereby effectively improving the resilience of the control strategy and reducing the risk of instability caused by overreaction of the system.

[0065] In one embodiment, based on the simulation model, the parameters of the energy storage converter control strategy function are optimized using a fusion genetic-particle swarm algorithm, including:

[0066] Generate the initial population and set { , , , , , , , , , } is the 10 dimensions of a particle, and the position and velocity of the particle are randomly initialized;

[0067] Substitute the parameters represented by the 10 dimensions of each particle into the established simulation model of the virtual synchronous machine to run it, obtain the simulation running data of the virtual synchronous machine and calculate the fitness value of each particle;

[0068] After evaluating the particle fitness, the particles are selected. According to the fitness value, excellent individuals are selected to enter the next generation population and perform crossover and mutation operations;

[0069] Take the newly generated particles as the particles in the particle swarm, perform the update operations of position and velocity, and record the historical optimal position of each particle and the global optimal position at the same time. Then judge whether the termination condition is reached. If the termination condition is satisfied, end the algorithm; otherwise, return to the fitness evaluation step to continue iterative optimization.

[0070] In a further embodiment, the simulation operation data of the virtual synchronous machine includes frequency change data and active power change data. The fitness function of the particle is as follows:

[0071] (3)

[0072] In the formula, is the fitness value of the particle, is the maximum value that the frequency reaches during the frequency fluctuation process, is the maximum value that the active power reaches during the active power fluctuation process, is the active power value that the system stably reaches after the system disturbance, is the time when the system returns to stability after the disturbance, is the time point of the system disturbance.

[0073] In a further embodiment, in the mutation operation, a double mutation strategy is adopted for mutation. The double mutation strategy is as follows:

[0074] (4)

[0075] In the formula, is the mutation vector, is the weight factor of the double mutation strategy, is the mutation factor, 、 、 、 、 and are the target vectors randomly selected from the current population, and the index r 1 -r 3 are mutually exclusive integers randomly selected from the range of {1, 2, …, N}, and are all different from the base index i, is the best target vector.

[0076] In this embodiment, a double mutation strategy, namely DE / rand / 1 and DE / rand-to-best / 1, is adopted during the mutation process. This strategy not only maintains the diversity of the population but also ensures the effective tracking of the optimal solution by the algorithm. The present invention makes full use of the good global search ability of the genetic algorithm in multi-parameter optimization problems and the local search advantages of the particle swarm algorithm to precisely optimize the parameters of the control strategy function of J and D, further enhancing the stable regulation ability of the energy storage converter in the power system.

[0077] The method proposed in the above embodiment is verified by combining with an example as follows.

[0078] In this example, a grid-connected energy storage converter simulation model is established on MATLAB / SIMULINK. The power is set to 1.725 MW, and a three-level ANPC circuit, virtual synchronous machine control (VSG control), and SVPWM wave modulation are adopted. When the simulation model is running, at t = 0.3 s, the given active power is increased from 1.644 MW to 1.737 MW. The J and D energy storage converter control strategy functions obtained by optimizing with the fusion genetic-particle swarm optimization algorithm are used to suppress oscillations, and the dynamic effects of the output frequency and active power are good. The comparison diagram of the output results of this method and the results calculated by the J and D fixed piecewise function calculation method is shown in the appendix Figure 2 and the appendix Figure 3 as shown.

[0079] Based on the same inventive concept, an embodiment of the present application also provides an energy storage converter optimization control device based on a fusion genetic-particle swarm algorithm for implementing the above-mentioned energy storage converter optimization control method based on a fusion genetic-particle swarm algorithm. The implementation solution provided by this device to solve problems is similar to the implementation solution described in the above method. Therefore, the specific limitations in the following embodiment of the energy storage converter optimization control device based on a fusion genetic-particle swarm algorithm can refer to the limitations on the energy storage converter optimization control method based on a fusion genetic-particle swarm algorithm in the above text, and will not be repeated here.

[0080] An embodiment of the present invention also provides an energy storage converter optimization control device based on a fusion genetic-particle swarm algorithm, including:

[0081] A selection principle determination module, configured to establish a simulation model of a virtual synchronous machine, write the second-order motion equation of the synchronous machine, and obtain the selection principles of the moment of inertia and damping coefficient under different angular frequency deviations and change rates; the selection principles are obtained according to the change rules summarized when adjusting the moment of inertia and damping coefficient to suppress the changes of the angular frequency deviation and angular frequency change rate in the second-order motion equation of the synchronous machine;

[0082] A control strategy function construction module, which is used to construct a control strategy function of an energy storage converter related to angular frequency deviation and angular frequency change rate of inertia and damping coefficient according to the selection principles of inertia and damping coefficient under different angular frequency deviations and change rates, by using a set of parameters.

[0083] An algorithm optimization and control module, which is used to optimize the parameters of the energy storage converter control strategy function based on a simulation model by using a hybrid genetic-particle swarm optimization algorithm, obtain a set of optimal solutions and substitute them into the parameters in the function to obtain the control strategy function of inertia and damping coefficient, and perform oscillation suppression control under low-frequency oscillation of a virtual synchronous machine.

[0084] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0085] Refer to Figure 4 In addition, an embodiment of the present invention further provides a computer device, including: a memory, a processor, and a computer program stored on the memory. When the computer program is executed on the processor, it implements an energy storage converter optimization control method based on a hybrid genetic-particle swarm optimization algorithm as described in any one of the above methods.

[0086] The computer device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 4 merely examples of computer devices, which do not constitute a limitation on computer devices, and may include more or fewer components than shown in the figure, or combine some components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0087] The so-called processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0088] In some embodiments, the memory may be an internal storage unit of the computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., equipped on the computer device. Further, the memory may also include both the internal storage unit and the external storage device of the computer device. The memory is used to store an operating system, application programs, a BootLoader, data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is to be output.

[0089] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements an optimized control method for an energy storage converter based on a fusion genetic-particle swarm algorithm as described in any one of the above methods.

[0090] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present application, a computer program can be used to instruct the relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0091] In the above embodiments, the descriptions of the various embodiments have their own focuses. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0093] In the embodiments disclosed in the present application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical, or other form.

[0094] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An energy storage converter optimization control method based on fusion genetic-particle swarm algorithm, characterized in that: The steps include: A simulation model of a virtual synchronous machine is established, the second-order motion equation of the synchronous machine is written, and the selection principle of the moment of inertia and the damping coefficient under different angular frequency deviations and change rates is obtained; the selection principle is obtained according to the change law summarized when the moment of inertia and the damping coefficient are adjusted in the second-order motion equation of the synchronous machine to suppress the change of the angular frequency deviation and the angular frequency change rate; According to the selection principle of the moment of inertia and the damping coefficient under the different angular frequency deviations and change rates, a set of parameters is used to construct the energy storage converter control strategy function related to the moment of inertia and the damping coefficient and the angular frequency deviation and the angular frequency change rate; Based on the simulation model, the parameters of the energy storage converter control strategy function are optimized using a fusion genetic-particle swarm algorithm to obtain a set of optimal solutions and substitute the parameters in the function to obtain the control strategy function of the moment of inertia and the damping coefficient, and perform oscillation suppression control under low-frequency oscillation of the virtual synchronous machine.

2. The energy storage converter optimization control method based on fusion genetic-particle swarm algorithm according to claim 1 is characterized in that: The energy storage converter control strategy function is as follows: In the formula, and are the moment of inertia and the damping coefficient respectively, and are the moment of inertia and damping coefficient of the virtual synchronous machine system when the working condition is stable, - is the adjustment coefficient, and are the angular frequency deviation threshold and the angular frequency change rate deviation threshold, respectively. is the angular frequency deviation, is the rate of change of angular frequency.

3. The energy storage converter optimization control method based on fusion genetic-particle swarm algorithm according to claim 2 is characterized in that: According to the importance of overshoot and response time in different scenarios, set - The size relationship of the adjustment coefficient, among which, the increase The adjustment coefficient weights are adjusted to adapt to the adjustment scenario where the moment of inertia and the damping coefficient are adjusted smoothly.

4. The energy storage converter optimization control method based on fusion genetic-particle swarm algorithm according to claim 2 is characterized in that: Based on the simulation model, the parameters of the energy storage converter control strategy function are optimized using a fusion genetic-particle swarm algorithm, including: Generate the initial population and set { , , , , , , , , , } is the 10 dimensions of a particle, and the position and velocity of the particle are randomly initialized; Substituting the parameters represented by the 10 dimensions of each particle into the simulation model of the established virtual synchronous machine for operation, obtaining the simulation operation data of the virtual synchronous machine and calculating the fitness value of each particle; After evaluating the particle fitness, the particles are selected. According to the fitness value, excellent individuals are selected to enter the next generation population and perform crossover and mutation operations; The newly generated particles are taken as particles in the particle swarm, and the position and speed are updated. At the same time, the historical optimal position of each particle and the optimal position of the swarm are recorded to determine whether the termination condition is met; if the termination condition is met, the algorithm ends, otherwise it returns to the fitness evaluation step to continue iterative optimization.

5. The energy storage converter optimization control method based on fusion genetic-particle swarm algorithm according to claim 4 is characterized in that: The simulation operation data of the virtual synchronous machine includes frequency change data and active power change data, and the fitness function of the particle is as follows: In the formula, is the fitness value of the particle, is the maximum value reached by the frequency during the frequency fluctuation process, is the maximum value of active power during active power fluctuation. is the active power value that the system reaches stably after disturbance, is the time it takes for the system to return to stability after a disturbance, is the time point of the system disturbance.

6. The energy storage converter optimization control method based on fusion genetic-particle swarm algorithm according to claim 4 is characterized in that: In the mutation operation, a double mutation strategy is used for mutation, and the double mutation strategy is as follows: In the formula, is the mutation vector, is the weight factor of the double mutation strategy, is the variation factor, , , , , and is the target vector randomly selected from the current population, is the optimal target vector.

7. The energy storage converter optimization control method based on fusion genetic-particle swarm algorithm according to claim 1 is characterized in that: The selection principles of the moment of inertia and damping coefficient under different angular frequency deviations and change rates include: When the angular frequency deviation , angular frequency change rate and When both are greater than zero, the moment of inertia and the damping coefficient both increase; When the angular frequency deviation is greater than zero, the angular frequency change rate and When both are less than zero, the moment of inertia and the damping coefficient both decrease; When the angular frequency deviation and the angular frequency change rate are less than zero, When it is greater than zero, the moment of inertia and the damping coefficient both increase; When the angular frequency deviation and are all less than zero, the angular frequency change rate When it is greater than zero, both the moment of inertia and the damping coefficient decrease.

8. An energy storage converter optimization control device based on fusion genetic-particle swarm algorithm, characterized in that: include: A selection principle determination module is used to establish a simulation model of a virtual synchronous machine, list the second-order motion equation of the synchronous machine, and obtain the selection principle of the moment of inertia and the damping coefficient under different angular frequency deviations and change rates; the selection principle is obtained based on the change law summarized when the moment of inertia and the damping coefficient are adjusted in the second-order motion equation of the synchronous machine to suppress the change of the angular frequency deviation and the angular frequency change rate; A control strategy function construction module, for constructing a control strategy function of an energy storage converter in which the moment of inertia and the damping coefficient are related to the angular frequency deviation and the angular frequency change rate using a set of parameters according to the selection principle of the moment of inertia and the damping coefficient under the different angular frequency deviations and change rates; The algorithm optimization and control module is used to optimize the parameters of the energy storage converter control strategy function based on the simulation model using a fusion genetic-particle swarm algorithm, obtain a set of optimal solutions and substitute them into the parameters in the function, obtain the control strategy function of the moment of inertia and the damping coefficient, and perform oscillation suppression control under low-frequency oscillation of the virtual synchronous machine.

9. A computer device, characterized in that: The device comprises a processor and a memory: The memory is used to store a computer program and send instructions of the computer program to the processor; The processor executes an energy storage converter optimization control method based on a fusion genetic-particle swarm algorithm as described in any one of claims 1 to 7 according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for optimizing and controlling an energy storage converter based on a fusion genetic-particle swarm algorithm as described in any one of claims 1 to 7 is implemented.

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