Photovoltaic power station AGC frequency control method and system based on PSO-GSA algorithm

Abstract

The invention discloses a photovoltaic power station AGC frequency control method and system based on a PSO-GSA algorithm, and relates to the technical field of power grid frequency safety, and the method comprises the steps: building a photovoltaic power station AGC system, and constructing an active-frequency droop control model with dead zone delay; introducing the improved virtual inertia control into an active-frequency droop control model, and improving a universal gravitation search algorithm; and optimizing the parameters of the improved active-frequency droop control model through a PSO-GSA algorithm, and carrying out isolated network frequency adjustment on the AGC system of the photovoltaic power station according to the parameters of the control model. According to the method, parameters of the improved active-frequency droop control model with dead zone delay are optimized through the PSO-GSA algorithm, so that the problems of inertia reduction and insufficient frequency modulation capability of an isolated network power system are solved; the GSA algorithm is improved through the PSO algorithm, the frequency control precision and the system stability of the AGC system of the photovoltaic power station are improved, and the improved GSA algorithm also has efficient and global search optimization capability and has a great application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid frequency safety, and in particular to a photovoltaic power station AGC frequency control method and system based on a PSO-GSA algorithm. Background Art

[0002] In recent years, with the increase in the grid-connected capacity of photovoltaic power stations, the rotational inertia of the power system has gradually decreased, and the power system's ability to resist load changes has weakened, seriously threatening the safe and stable operation of the power system. Therefore, as the scale of photovoltaic power stations connected to the grid continues to increase and the grid frequency becomes more complex and changeable, the participation of photovoltaic power generation systems in frequency regulation has become a research hotspot.

[0003] In traditional power systems, the generation and consumption of electricity must always be balanced to maintain the stable operation of the power system, which means that the difference between the power generation and load demand in the system will cause changes in the grid frequency. In order to ensure the stability of the power system, it is of utmost importance to quickly adjust the grid frequency in response to any load changes. With the vigorous development of intelligent control technology, scholars at home and abroad have introduced intelligent control methods into linear control systems to improve the control effect. Among the existing methods for photovoltaic power stations to participate in grid frequency control, the traditional PID controller is a linear control algorithm, which has poor control effect on complex nonlinear systems; virtual synchronous machine control can simulate the behavior of traditional synchronous generators and control the grid frequency by adjusting parameters such as current and voltage, but the control algorithm and equipment are too complex, which increases the operation and maintenance costs and technical complexity; although the neural network controller performs well on the training data, the neural network is prone to overfitting on the training data and performs poorly on unseen data.

[0004] In summary, the existing technology for the study of photovoltaic power station frequency control has the problems of reduced inertia and insufficient frequency regulation capability. Therefore, in order to better solve the habitual and weak damping characteristics presented in the isolated grid system, as well as the susceptibility to disturbances and frequency instability problems, a photovoltaic power station AGC frequency control method and system based on PSO-GSA algorithm are proposed. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problems solved by the present invention are: the existing photovoltaic power station frequency control method has the problems of insufficient inertia reduction and frequency regulation capability, limitation of control algorithm, unstable frequency of isolated grid operation, and how to effectively improve the photovoltaic power station's ability to participate in grid frequency regulation to ensure safe and stable operation of the grid.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a photovoltaic power station AGC frequency control method based on a PSO-GSA algorithm, comprising establishing a photovoltaic power station AGC system, constructing an active power-frequency droop control model with a dead zone delay; introducing an improved virtual inertia control into the active power-frequency droop control model, and improving the gravitational search algorithm; optimizing the parameters of the improved active power-frequency droop control model through the PSO-GSA algorithm, and adjusting the isolated grid frequency of the photovoltaic power station AGC system according to the control model parameters.

[0008] As a preferred solution of the photovoltaic power station AGC frequency control method of the PSO-GSA algorithm described in the present invention, wherein: the establishment of the photovoltaic power station AGC system includes based on the current light intensity and temperature, assuming that the maximum power point tracking is used for control, the active output P pv , expressed as:

[0009]

[0010] in, Represents the rated power generation of the photovoltaic power station, α pv Indicates the photovoltaic temperature conversion power coefficient, T represents the current temperature, T ref Indicates the temperature reference value, s pv Indicates the current light intensity.

[0011] As a preferred solution of the photovoltaic power station AGC frequency control method of the PSO-GSA algorithm of the present invention, wherein: the construction of the active power-frequency droop control model with dead zone delay includes constructing the active power-frequency droop control model and setting the frequency modulation dead zone; the construction of the active power-frequency droop control model includes model establishment, which is expressed as:

[0012]

[0013] Δf=f ref -f

[0014] in, represents the installed capacity of the photovoltaic power station, K1 represents the droop gain, f ref represents the rated grid system frequency, f represents the grid system frequency, Δf represents the grid system frequency deviation, δ represents the modulation rate, and Δt represents the time interval; the setting of the frequency modulation dead zone includes setting the frequency modulation dead zone threshold to β, the frequency defaults to 0.06Hz, and when the grid frequency ≥ the threshold β, the frequency modulation control system starts the frequency modulation measures; the maximum load adjustment limit of the frequency modulation is not less than 10% of the rated capacity of the photovoltaic power station installed, and the modulation rate defaults to 3%.

[0015] As a preferred solution of the photovoltaic power station AGC frequency control method of the PSO-GSA algorithm described in the present invention, the improved virtual inertia control includes improving the system frequency deviation Δf in the original droop control to the maximum system frequency deviation Δf max , and set the maximum system frequency deviation Δf max As input for virtual inertial control.

[0016] As a preferred solution of the photovoltaic power station AGC frequency control method of the PSO-GSA algorithm described in the present invention, the improved gravitational search algorithm includes improving the gravitational search algorithm GSA from the correction of particle inertial mass, the elite retention strategy of particles and the global memory ability of particles according to the particle swarm optimization algorithm PSO; improving the gravitational search algorithm from the correction of particle inertial mass includes setting a weight for each particle, and the weight of the inertial mass of each particle is mathematically ω i (t), expressed as:

[0017]

[0018] Among them, N max 、N min Respectively represent the maximum and minimum values ​​of the weight, M max 、M min Respectively represent the maximum and minimum values ​​of inertial mass, M i (t) represents the inertial mass of particle i at time t; the improved inertial mass calculation formula for each particle is expressed as:

[0019] M i '(t)=ω i (t)×M i (t)

[0020] The improvement of the gravitational search algorithm based on the particle elite retention strategy includes assuming that the number of particles with inertial mass that plays an attractive role in the search space is K best , K best is a time function, initialize K best is K0, and as the iteration proceeds K best Gradually decrease, iterate until the end K best The remaining few particles attract other particles, and the expression is expressed as:

[0021]

[0022] in, It represents the magnitude of all forces of particle i in the d-dimensional search space at time t. represents the gravitational force of particle j on particle i. Assume that the total number of particles in a d-dimensional search space is N; the magnitude of all forces of particle i in the d-dimensional search space at time t It is expressed as:

[0023]

[0024] The gravitational force of particle j on particle i It is expressed as:

[0025]

[0026] Where G(t) represents the gravitational constant at time t, M aj (t) represents the inertial mass of the object, M pi (t) represents the inertial mass of the object being acted on, R ij (t) represents the Euclidean distance between particle i and particle j; the gravitational search algorithm is improved from the global memory capability of particles, including the introduction of the global memory capability of the particle swarm algorithm. The improved speed update formula is expressed as:

[0027]

[0028] in, They represent the speed, position, and acceleration of particle i in the d-dimensional search space at time t, respectively. b1 and b2 are constants in [0,1]. By adjusting the sizes of b1 and b2, the influence of the memory ability of the particle swarm algorithm on the gravitational search algorithm can be controlled to balance the influence of gravity and group memory on the algorithm.

[0029] As a preferred solution of the photovoltaic power station AGC frequency control method of the PSO-GSA algorithm described in the present invention, wherein: the optimized and improved active power-frequency droop control model parameters include initializing the position, speed and upper and lower limits of the weight of particles in the search space, and setting the number of algorithm iterations; the iteration step includes calculating the fitness value of each particle, updating the algorithm parameters at time t, calculating the size of the particle resultant force according to the elite retention strategy of the particles, calculating the acceleration of the particles, updating the particle speed according to the global memory ability of the particles, and updating the particle position; repeating the continuous iteration until the preset number of iterations is reached and stopping, and obtaining the optimal solution of the algorithm; assigning the particles optimized by the PSO-GSA algorithm to the parameters to be optimized of the improved active power-frequency droop control model; and distributing the power generation power instruction to the photovoltaic power station AGC system.

[0030] As a preferred solution of the photovoltaic power station AGC frequency control method of the PSO-GSA algorithm described in the present invention, wherein: the algorithm parameters updated at time t include updating the gravitational constant G(t) at time t, the optimal solution best(t), the worst solution worst(t) and the improved inertial mass M of each particle i '(t); Update the optimal solution best(t) at time t and the worst solution worst(t) at time t, expressed as:

[0031]

[0032] Among them, fit j (t) represents the fitness value of particle i at time t.

[0033] Another object of the present invention is to provide a photovoltaic power plant AGC frequency control system based on a PSO-GSA algorithm, which can solve the problem of insufficient photovoltaic power plant AGC response capability contained in the current technology by improving virtual inertial control.

[0034] As a preferred solution of the photovoltaic power station AGC frequency control system of the PSO-GSA algorithm described in the present invention, it includes: a construction module, an improvement module, and an optimization and adjustment module; the construction module is used to establish a photovoltaic power station AGC system and construct an active power-frequency droop control model with dead zone delay; the improvement module is used to introduce improved virtual inertia control into the active power-frequency droop control model and improve the universal gravitation search algorithm; the optimization and adjustment module is used to optimize the parameters of the improved active power-frequency droop control model through the PSO-GSA algorithm, and adjust the isolated network frequency of the photovoltaic power station AGC system according to the control model parameters.

[0035] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a photovoltaic power station AGC frequency control method of a PSO-GSA algorithm.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a photovoltaic power station AGC frequency control method using a PSO-GSA algorithm.

[0037] Beneficial effects of the present invention: The photovoltaic power station AGC frequency control method of the PSO-GSA algorithm provided by the present invention not only effectively avoids the problems caused by frequent switching of control modes near the frequency action dead zone by introducing dead zone delay and virtual inertia control, but also enhances the system's response capability to frequency disturbances and improves the system's dynamic frequency stability; by improving the universal gravitation search algorithm, not only the algorithm's convergence speed and global search capability are improved, but also the control model parameters are effectively optimized, and the system's dynamic frequency performance is improved; by optimizing the control model parameters, the photovoltaic power station can participate in the grid frequency regulation more effectively, alleviating the inertia reduction and The problem of insufficient frequency regulation capability; through the optimization capability of the PSO-GSA algorithm, not only the accuracy and stability of the control model parameters are ensured, but also the reliability and effectiveness of the photovoltaic power station participating in frequency regulation are improved; by optimizing the control model parameters, key indicators such as system frequency deviation, interconnection line power and regional control deviation are effectively reduced, thereby improving the stable operation performance of the power system; by combining the particle swarm optimization algorithm and the gravitational search algorithm, the shortcomings of the slow convergence speed of the GSA algorithm and the easy falling into the local optimum of the PSO algorithm are overcome, and the performance and robustness of the algorithm are improved; the present invention achieves better results in improving the stability, reliability and frequency regulation accuracy of the power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0039] Figure 1 The overall flow chart of a photovoltaic power station AGC frequency control method using a PSO-GSA algorithm provided in the first embodiment of the present invention.

[0040] Figure 2 A control flow chart of an improved active power-frequency droop control model of a photovoltaic power station AGC frequency control method based on a PSO-GSA algorithm provided in the first embodiment of the present invention.

[0041] Figure 3 The present invention provides a first embodiment of a PSO-GSA algorithm for providing an improved PSO-GSA algorithm flow chart of a photovoltaic power station AGC frequency control method.

[0042] Figure 4 This is an overall flow chart of a photovoltaic power station AGC frequency control system using a PSO-GSA algorithm provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a photovoltaic power station AGC frequency control method using a PSO-GSA algorithm, comprising:

[0045] S1: Establish an AGC system for a photovoltaic power station and construct an active power-frequency droop control model with dead zone delay.

[0046] Furthermore, the establishment of the AGC system for the photovoltaic power station includes the active output P based on the current light intensity and temperature, assuming that the maximum power point tracking is used for control. pv , expressed as:

[0047]

[0048] in, Represents the rated power generation of the photovoltaic power station, α pv Indicates the photovoltaic temperature conversion power coefficient, T represents the current temperature, T ref Indicates the temperature reference value, s pv Indicates the current light intensity.

[0049] It should be noted that constructing an active power-frequency droop control model with a dead zone delay includes constructing an active power-frequency droop control model and setting a frequency modulation dead zone; the constructing an active power-frequency droop control model includes model establishment, which is expressed as:

[0050]

[0051] Δf=f ref -f

[0052] in, represents the installed capacity of the photovoltaic power station, K1 represents the droop gain, f ref represents the rated grid system frequency, f represents the grid system frequency, Δf represents the grid system frequency deviation, δ represents the modulation rate, and Δt represents the time interval; the setting of the frequency modulation dead zone includes setting the frequency modulation dead zone threshold to β, the frequency defaults to 0.06Hz, and when the grid frequency ≥ the threshold β, the frequency modulation control system starts the frequency modulation measures; the maximum load adjustment limit of the frequency modulation is not less than 10% of the rated capacity of the photovoltaic power station installed, and the modulation rate defaults to 3%.

[0053] It should also be noted that the primary frequency control response of the new energy station should be coordinated with the AGC, that is, the active power control target value of the new energy station should take into account the interlocking or superposition logic between the AGC command value and the primary frequency control active power command value.

[0054] It should also be noted that the establishment of an AGC system for a photovoltaic power station is to regard the photovoltaic power station as a controllable power source. The AGC system is established to receive dispatch instructions and perform frequency regulation, thereby improving the frequency regulation efficiency of the system. The dead zone delay is introduced to avoid the problems caused by frequent switching of control modes near the frequency action dead zone. The frequency regulation dead zone threshold is set. When the grid frequency deviation exceeds the threshold, the frequency regulation measures are initiated, which can improve the stability and control accuracy of the system.

[0055] S2: Introduce the improved virtual inertia control into the active power-frequency droop control model and improve the universal gravitation search algorithm.

[0056] Furthermore, the improved virtual inertia control includes improving the system frequency deviation Δf in the original droop control to the maximum system frequency deviation Δf max , and set the maximum system frequency deviation Δf max As input for virtual inertial control.

[0057] It should be noted that the improved gravitational search algorithm includes improving the gravitational search algorithm GSA from the perspective of particle inertial mass correction, particle elite retention strategy, and particle global memory ability based on the particle swarm optimization algorithm PSO; improving the gravitational search algorithm from the perspective of particle inertial mass correction includes setting a weight for each particle, and the weight of the inertial mass of each particle is mathematically ω i (t), expressed as:

[0058]

[0059] Among them, N max 、N min Respectively represent the maximum and minimum values ​​of the weight, M max 、M min Respectively represent the maximum and minimum values ​​of inertial mass, M i (t) represents the inertial mass of particle i at time t; the improved inertial mass calculation formula for each particle is expressed as:

[0060] M i '(t)=ω i (t)×M i (t)

[0061] The improvement of the gravitational search algorithm based on the particle elite retention strategy includes assuming that the number of particles with inertial mass that plays an attractive role in the search space is K best , K best is a time function, initialize K best is K0, and as the iteration proceeds K best Gradually decrease, iterate until the end K best The remaining few particles attract other particles, and the expression is expressed as:

[0062]

[0063] in, It represents the magnitude of all forces of particle i in the d-dimensional search space at time t. represents the gravitational force of particle j on particle i. Assume that the total number of particles in a d-dimensional search space is N; the magnitude of all forces of particle i in the d-dimensional search space at time t It is expressed as:

[0064]

[0065] The gravitational force of particle j on particle i It is expressed as:

[0066]

[0067] Where G(t) represents the gravitational constant at time t, M aj (t) represents the inertial mass of the object, M pi (t) represents the inertial mass of the object being acted on, R ij (t) represents the Euclidean distance between particle i and particle j; the gravitational search algorithm is improved from the global memory capability of particles, including the introduction of the global memory capability of the particle swarm algorithm. The improved speed update formula is expressed as:

[0068]

[0069] in, They represent the speed, position, and acceleration of particle i in the d-dimensional search space at time t, respectively. b1 and b2 are constants in [0,1]. By adjusting the sizes of b1 and b2, the influence of the memory ability of the particle swarm algorithm on the gravitational search algorithm can be controlled to balance the influence of gravity and group memory on the algorithm.

[0070] It should also be noted that by introducing virtual inertia control to simulate the inertia characteristics of traditional synchronous generators, the system's response to frequency disturbances is enhanced, and the system frequency deviation Δf in the original droop control is improved to the maximum system frequency deviation Δf max , and Δf maxAs the input of virtual inertial control, it is equivalent to adding a memory function in the control system, so that the system can adjust more quickly and effectively according to the historical frequency deviation information.

[0071] It should also be noted that a weight is set for each particle by correcting the inertial mass of the particle, and the inertial mass is dynamically adjusted according to the number of iterations, making the algorithm more adaptable during the search process; the particles with larger inertial mass in the search space are retained through the particle elite retention strategy, so that the particles can play a guiding role in accelerating convergence in subsequent iterations; the global memory capability of the PSO algorithm is introduced through the global memory capability of the particles, so that the particles can perform more effective searches based on the historical optimal solution information.

[0072] S3: The improved active power-frequency droop control model parameters are optimized by the PSO-GSA algorithm, and the isolated grid frequency of the PV power station AGC system is adjusted according to the control model parameters.

[0073] Furthermore, optimizing the parameters of the improved active power-frequency droop control model includes initializing the position, speed and upper and lower limits of the weight of the particles in the search space, and setting the number of algorithm iterations; the iterative steps include calculating the fitness value of each particle, updating the algorithm parameters at time t, calculating the size of the particle resultant force according to the particle elite retention strategy, calculating the acceleration of the particle, updating the particle speed according to the global memory ability of the particle, and updating the particle position; repeating the iteration until the preset number of iterations is reached and stopping to obtain the optimal solution of the algorithm; assigning the particles optimized by the PSO-GSA algorithm to the parameters to be optimized of the improved active power-frequency droop control model; and allocating the power generation command to the AGC system of the photovoltaic power station.

[0074] It should be noted that updating the algorithm parameters at time t includes updating the gravitational constant G(t), the optimal solution best(t), the worst solution worst(t), and the improved inertial mass M of each particle at time t. i '(t); Update the optimal solution best(t) at time t and the worst solution worst(t) at time t, expressed as:

[0075]

[0076] Among them, fit j (t) represents the fitness value of particle i at time t.

[0077] It should also be noted that the optimal parameters obtained by optimizing the PSO-GSA algorithm are assigned to the improved active power-frequency droop control model. The model will calculate the power generation command based on the parameters and send the command to the PV power station AGC system. After receiving the power generation command, the PV power station AGC system will adjust the output power of the PV power station according to the command, thereby achieving effective regulation of the grid frequency.

[0078] Embodiment 2 is an embodiment of the present invention, which provides a photovoltaic power station AGC frequency control method based on a PSO-GSA algorithm. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0079] First of all, in order to verify the effectiveness of a PSO-GSA algorithm for photovoltaic power station AGC frequency control method, this experiment built an experimental platform simulating the photovoltaic power station AGC system, and designed two control strategies for comparison:

[0080] The traditional PID control strategy uses the traditional PID controller to adjust the frequency of the PV power station AGC system as the control group.

[0081] Based on the control strategy of the PSO-GSA algorithm, according to the method of the present invention, a photovoltaic power station AGC system is established, and an active power-frequency droop control model with dead zone delay is constructed, an improved virtual inertia control is introduced, the system frequency deviation in the original droop control is improved to the maximum system frequency deviation, and the maximum system frequency deviation is used as the input of the virtual inertia control, the gravitational search algorithm is improved from the correction of particle inertia mass, the elite retention strategy of particles and the global memory ability trigger of particles, the PSO-GSA algorithm is used to optimize the parameters of the improved active power-frequency droop control model, and the photovoltaic power station AGC system is adjusted for isolated grid frequency according to the optimized parameters.

[0082] Secondly, the experimental platform mainly includes photovoltaic power station simulation module, AGC system simulation module, frequency regulation module and data acquisition module.

[0083] The photovoltaic power station simulation module simulates the output power of the photovoltaic power station and calculates the active output of the photovoltaic power station based on parameters such as light intensity and temperature.

[0084] The AGC system simulation module receives frequency adjustment instructions and controls the output power of the photovoltaic power station by simulating the control logic of the AGC system.

[0085] The frequency regulation module calculates the frequency regulation instructions according to different control strategies and sends the regulation instructions to the AGC system.

[0086] The data acquisition module collects frequency, power and other data during the experiment for analysis and summary after the experiment.

[0087] During the experiment, the installed capacity of the photovoltaic power station was set to 10MW, the rated grid system frequency was 20Hz, the frequency modulation dead zone threshold was 0.0.6Hz, the maximum load adjustment limit of frequency modulation was 1MW, the adjustment rate was 3%, the number of particles in the PSO-GSA algorithm parameters was 50, and the number of iterations was 100.

[0088] In simulation scenario one, the grid load suddenly increases, causing the grid frequency to drop.

[0089] In simulation scenario two, the grid load suddenly decreases, causing the grid frequency to increase.

[0090] Finally, the experimental data shown in Table 1 below are obtained.

[0091] Table 1 Experimental data of two control strategies in different scenarios

[0092]

[0093] It can be seen from the experimental data that in terms of frequency deviation control, the control strategy of the method of the present invention can control the frequency deviation more effectively than the PID control strategy, so that the frequency deviation can be restored to the set value more quickly; in terms of frequency adjustment time, the control strategy of the present invention can significantly shorten the frequency adjustment time, so that the grid frequency can be stabilized more quickly; in terms of frequency overshoot, the control strategy of the present invention can effectively reduce the frequency overshoot and avoid large fluctuations in grid frequency; in terms of the change in interconnection line power, the control strategy of the present invention can reduce the change in interconnection line power and reduce the impact on other grids; in terms of the change in regional control deviation signal, the control strategy of the present invention can reduce the change in regional control deviation signal and improve the accuracy of grid frequency control.

[0094] Example 3, reference Figure 4 , which is an embodiment of the present invention, provides a photovoltaic power station AGC frequency control system based on a PSO-GSA algorithm, including a construction module, an improvement module, and an optimization and adjustment module.

[0095] The construction module is used to establish the AGC system of the photovoltaic power station and construct an active-frequency droop control model with dead zone delay; the improvement module is used to introduce the improved virtual inertia control into the active-frequency droop control model and improve the gravitational search algorithm; the optimization and adjustment module is used to optimize the parameters of the improved active-frequency droop control model through the PSO-GSA algorithm, and perform isolated grid frequency adjustment on the AGC system of the photovoltaic power station according to the control model parameters.

[0096] If the function 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, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0097] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0098] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0099] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0100] It should be noted that 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A photovoltaic power station AGC frequency control method based on PSO-GSA algorithm, characterized in that: include: Establish a photovoltaic power station AGC system and construct an active power-frequency droop control model with dead zone delay; Introduce improved virtual inertia control into the active power-frequency droop control model and improve the gravitational search algorithm; The improved active power-frequency droop control model parameters are optimized by the PSO-GSA algorithm, and the isolated grid frequency is adjusted for the AGC system of the photovoltaic power station according to the control model parameters.

2. The photovoltaic power station AGC frequency control method of the PSO-GSA algorithm according to claim 1, characterized in that: The photovoltaic power station AGC system is established based on the current light intensity and temperature. Assuming that the maximum power point tracking is used for control, the active output P pv , expressed as: in, Represents the rated power generation of the photovoltaic power station, α pv Indicates the photovoltaic temperature conversion power coefficient, T represents the current temperature, T ref Indicates the temperature reference value, s pv Indicates the current light intensity.

3. The photovoltaic power station AGC frequency control method of the PSO-GSA algorithm according to claim 2, characterized in that: The constructing of the active power-frequency droop control model with dead zone delay includes constructing the active power-frequency droop control model and setting the frequency modulation dead zone; The construction of the active power-frequency droop control model includes model establishment, which is expressed as: Δf=f ref -f in, represents the installed capacity of the photovoltaic power station, K1 represents the droop gain, f ref represents the rated grid system frequency, f represents the grid system frequency, Δf represents the grid system frequency deviation, δ represents the adjustment rate, and Δt represents the time interval; The setting of the frequency regulation dead zone includes setting the frequency regulation dead zone threshold to β, the frequency defaults to 0.06 Hz, and when the grid frequency ≥ the threshold β, the frequency regulation control system starts the frequency regulation measures; The maximum load adjustment limit of frequency regulation shall not be less than 10% of the rated capacity of the photovoltaic power station, and the default adjustment rate is 3%.

4. The photovoltaic power station AGC frequency control method of the PSO-GSA algorithm according to claim 3, characterized in that: The improved virtual inertia control includes improving the system frequency deviation Δf in the original droop control to the maximum system frequency deviation Δf max , and set the maximum system frequency deviation Δf max As input for virtual inertial control.

5. The photovoltaic power station AGC frequency control method of the PSO-GSA algorithm according to claim 4, characterized in that: The improved gravitational search algorithm includes improving the gravitational search algorithm GSA from the aspects of particle inertial mass correction, particle elite retention strategy and particle global memory ability according to the particle swarm optimization algorithm PSO; The improvement of the gravitational search algorithm from the correction of particle inertial mass includes setting a weight for each particle. The weight of the inertial mass of each particle is mathematically ω i (t), expressed as: Among them, N max 、N min Respectively represent the maximum and minimum values ​​of the weight, M max 、M min Respectively represent the maximum and minimum values ​​of inertial mass, M i (t) represents the inertial mass of particle i at time t; The improved calculation formula for the inertial mass of each particle is expressed as: M i '(t)=ω i (t)×M i (t) The improvement of the gravitational search algorithm based on the particle elite retention strategy includes assuming that the number of particles with inertial mass that plays an attractive role in the search space is K best , K best is a time function, initialize K best is K0, and as the iteration proceeds K best Gradually decrease, iterate until the end K best The remaining few particles attract other particles, and the expression is expressed as: Among them, F i d (t) represents the magnitude of all forces on particle i at time t in the d-dimensional search space, represents the gravitational force of particle j on particle i, assuming that the total number of particles in a d-dimensional search space is N; The magnitude of all forces F of particle i in the d-dimensional search space at time t i d (t), expressed as: The gravitational force of particle j on particle i It is expressed as: Where G(t) represents the gravitational constant at time t, M aj (t) represents the inertial mass of the object, M pi (t) represents the inertial mass of the object being acted on, R ij (t) represents the Euclidean distance between particle i and particle j; The improvement of the gravitational search algorithm from the global memory ability of particles includes the introduction of the global memory ability of the particle swarm algorithm. The improved speed update formula is expressed as: in, They represent the velocity, position, and acceleration of particle i in the d-dimensional search space at time t, respectively. b1 and b2 are constants in [0,1]; By adjusting the size of b1 and b2, the influence of the memory capacity of the particle swarm algorithm on the gravitational search algorithm is controlled, and the influence of gravity and group memory on the algorithm is balanced.

6. The photovoltaic power station AGC frequency control method of the PSO-GSA algorithm according to claim 5, characterized in that: The parameters of the optimized and improved active power-frequency droop control model include initializing the position, speed and upper and lower limits of weights of particles in the search space, and setting the number of algorithm iterations; The iterative steps include calculating the fitness value of each particle, updating the algorithm parameters at time t, calculating the size of the particle force according to the particle elite retention strategy, calculating the acceleration of the particle, updating the particle speed according to the particle's global memory ability, and updating the particle position; Repeat the iteration until the preset number of iterations is reached and the optimal solution of the algorithm is obtained; The particles optimized by the PSO-GSA algorithm are assigned to the parameters to be optimized of the improved active power-frequency droop control model; Distribute power generation instructions to the photovoltaic power station AGC system.

7. The photovoltaic power station AGC frequency control method of the PSO-GSA algorithm according to claim 6, characterized in that: The algorithm parameters for updating the time t include updating the gravitational constant G(t), the optimal solution best(t), the worst solution worst(t), and the improved inertial mass M of each particle at the time t. i '(t); Update the best solution best(t) at time t and the worst solution worst(t) at time t, expressed as: Among them, fit j (t) represents the fitness value of particle i at time t.

8. A system for a photovoltaic power station AGC frequency control method using the PSO-GSA algorithm according to any one of claims 1 to 7, characterized in that: Including construction module, improvement module, optimization and adjustment module; The construction module is used to establish an AGC system for a photovoltaic power station and to construct an active power-frequency droop control model with a dead zone delay; The improvement module is used to introduce improved virtual inertia control into the active power-frequency droop control model to improve the universal gravitation search algorithm; The optimization and regulation module is used to optimize the parameters of the improved active power-frequency droop control model through the PSO-GSA algorithm, and to regulate the isolated grid frequency of the photovoltaic power station AGC system according to the control model parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the photovoltaic power station AGC frequency control method of the PSO-GSA algorithm according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the photovoltaic power station AGC frequency control method of the PSO-GSA algorithm according to any one of claims 1 to 7 are implemented.

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