Voltage control method and system based on adaptive particle swarm optimization and SMC

Abstract

The present invention discloses a voltage control method and system based on adaptive particle swarm optimization and SMC, and relates to the field of photovoltaic energy management system control technology. The method comprises the steps of: setting particle swarm optimization algorithm parameters; acquiring photovoltaic cell data, and calculating fitness according to the photovoltaic cell data; iteratively calculating the optimal voltage value using a particle swarm optimization algorithm with an adaptive adjustment factor selection strategy, wherein the adaptive adjustment factor selection strategy is used to dynamically adjust the parameter combination according to the fitness value of the current iteration to balance the efficiency of global search and local development; and adjusting the output voltage of the photovoltaic cell according to the optimal voltage value using a sliding mode controller. The present invention can combine the particle swarm optimization algorithm with an adaptive factor selection strategy, and at the same time couple the sliding mode control algorithm to jointly realize fast and efficient target photovoltaic voltage calculation.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic energy management system control technology, and in particular to a voltage control method and system based on adaptive particle swarm optimization and SMC. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The maximum power point tracking (MPPT) algorithm is a key technology for improving energy conversion efficiency in photovoltaic power generation systems. Its main goal is to ensure that the system always works at the maximum power point (MPP) by adjusting the working voltage of photovoltaic panels in real time, thereby improving the working efficiency of photovoltaic power generation systems. However, the MPPT algorithm faces many challenges in application, such as rapid changes in light intensity and temperature, partial shadows, and load fluctuations. Currently, the commonly used MPPT algorithms include classical algorithms, neural network algorithms, and intelligent optimization algorithms. Classical algorithms include perturbation observation method and conductance increment method. These algorithms generally have the problems of oscillation near the maximum power point, slow response speed when the environment changes, cloud cover, and photovoltaic arrays cannot track the maximum power point under shadow conditions. Neural network algorithms are limited in practical applications because they require a lot of resources for calculation and are difficult to tune parameters. Intelligent optimization algorithms, such as particle swarm optimization, have the advantages of strong adaptability, global optimization, robustness and stability in MPPT applications. These characteristics make intelligent optimization algorithms perform well in complex and dynamic photovoltaic power generation systems, improving the efficiency and stability of the system. Although intelligent optimization algorithms can achieve efficient photovoltaic power generation capabilities, there are still some problems. For example, although the existing intelligent optimization algorithm can accurately calculate the voltage at the maximum power point through mathematical optimization methods, there is still a considerable power loss in the actual power generation process. There are two reasons for this. First, the existing intelligent optimization algorithm may converge slowly due to improper parameter settings and too many iterations. Second, when the intelligent optimization algorithm calculates the voltage at the maximum power point and performs voltage tracking, the PID method is used. The PID method cannot provide a fast enough response speed, and may also cause system output overshoot when dealing with rapidly changing systems, affecting system stability.

[0004] In summary, how to achieve accurate calculation and rapid response of the voltage at the maximum power point of photovoltaic MPPT control has become a technical problem that needs to be solved urgently in the existing technology. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a voltage control method and system based on adaptive particle swarm optimization and SMC, which can combine the adaptive factor selection strategy particle swarm optimization (AFSS-PSO) algorithm and couple the sliding mode control (SMC) algorithm to jointly achieve fast and efficient target photovoltaic voltage tracking.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] A first aspect of the present invention provides a voltage control method based on adaptive particle swarm optimization and SMC, comprising the following steps:

[0008] Set the particle swarm optimization algorithm parameters;

[0009] Obtain photovoltaic cell data, and calculate fitness based on the photovoltaic cell data;

[0010] The optimal voltage value is iteratively calculated using a particle swarm optimization algorithm with an adaptive adjustment factor selection strategy, wherein the adaptive adjustment factor selection strategy is used to dynamically adjust the parameter combination according to the fitness value of the current iteration to balance the efficiency of global search and local development;

[0011] The output voltage of the photovoltaic cell is adjusted according to the optimal voltage value using a sliding mode controller.

[0012] Furthermore, the specific steps for setting the parameters of the particle swarm optimization algorithm are:

[0013] Determine the search range of the photovoltaic cell output voltage;

[0014] Divide the search range into several levels, and the range of each level is a sub-interval of the total search range;

[0015] Select an adaptive chaotic map and use the Logistic mapping method;

[0016] Set the initial values ​​and adjustment rules of adaptive parameters;

[0017] Within each level, the initial particle positions are generated using an adaptive chaotic map, and the initial particle velocities are randomly generated.

[0018] Furthermore, the specific steps of obtaining photovoltaic cell data and calculating fitness based on the photovoltaic cell data are as follows:

[0019] Real-time sampling of the output voltage and output current of photovoltaic cells;

[0020] Calculate the output power of the photovoltaic cell using the collected voltage and current data;

[0021] The output voltage sampled by the photovoltaic cell is directly mapped to the current position of the particle, and the calculated output power is used as the fitness function.

[0022] Furthermore, during the iterative calculation process of the particle swarm optimization algorithm, the optimization iteration goal is to maximize the fitness function by adjusting the position of the particles, and the objective function is the maximum value of the output power.

[0023] Furthermore, the specific steps of iteratively calculating the optimal voltage value using the particle swarm optimization algorithm with an adaptive adjustment factor selection strategy are as follows:

[0024] Adjust the inertia weight according to the current iteration number and group diversity;

[0025] The cognitive coefficient and social coefficient are adjusted according to the rate of change of the current optimal fitness value;

[0026] Update the particle's velocity and position using the updated parameters to ensure that the particle's position is within the search range;

[0027] After iterating until the convergence condition is met or the set number of times is reached, the optimal value of the individual particle is obtained;

[0028] Update the global optimal value according to the individual optimal value.

[0029] Furthermore, the global optimal value is taken as the maximum power point position, and the photovoltaic cell output voltage corresponding to the maximum power point position is the optimal voltage value that maximizes the output power.

[0030] Furthermore, the specific steps of using the sliding mode controller to adjust the output voltage of the photovoltaic cell according to the optimal voltage value are as follows:

[0031] inputting the optimal voltage value into the sliding mode controller as a preset target voltage value;

[0032] The sliding mode controller calculates a control signal for adjusting the output voltage of the photovoltaic cell based on the current real-time state variable of the photovoltaic cell and the preset target voltage value.

[0033] Furthermore, the control signal is a pulse width modulation signal, and the duty cycle of the pulse width modulation signal is dynamically adjusted according to the deviation between the preset target voltage and the actual voltage to ensure that the photovoltaic cell can quickly and stably reach the target voltage value.

[0034] Furthermore, the generated pulse width modulation signal regulates the output voltage of the photovoltaic cell by controlling the switching state of the IGBT.

[0035] A second aspect of the present invention provides a voltage control system based on adaptive particle swarm optimization and SMC, comprising:

[0036] A parameter setting module, configured to set the particle swarm optimization algorithm parameters;

[0037] A data acquisition module is configured to acquire photovoltaic cell data and calculate fitness based on the photovoltaic cell data;

[0038] An adaptive optimization module is configured to iteratively calculate an optimal voltage value using a particle swarm optimization algorithm with an adaptive adjustment factor selection strategy, wherein the adaptive adjustment factor selection strategy is used to dynamically adjust a parameter combination according to a fitness value of a current iteration to balance the efficiency of global search and local development;

[0039] The voltage control module is configured to adjust the output voltage of the photovoltaic cell according to the optimal voltage value by using a sliding mode controller.

[0040] One or more of the above technical solutions have the following beneficial effects:

[0041] The present invention discloses a voltage control method and system based on adaptive particle swarm optimization and SMC, which uses AFSS-PSO algorithm to calculate the optimal voltage value, dynamically adjusts the algorithm parameters through adaptive factor selection strategy, optimizes the search efficiency of particle swarm, and can find the maximum power point faster in complex environmental conditions. AFSS-PSO adopts a multi-group collaboration strategy, and different particle swarms can share information, reduce search blind spots, and improve search accuracy and success rate. It reduces the time to search for MPP.

[0042] The present invention also adopts a sliding mode control (SMC) method. SMC has a fast response speed and can quickly adjust when the system state deviates from the sliding mode surface, ensuring that the system state quickly approaches and remains near the target MPP voltage. Moreover, SMC has strong robustness to system parameter changes and external interference, especially in complex and changeable environmental conditions, and can ensure the stability and control accuracy of the system.

[0043] The present invention adopts the method of combining AFSS-PSO with SMC, which can significantly improve the overall performance of the photovoltaic system in the maximum power point tracking process by optimizing the search strategy and enhancing the control robustness. It enhances the adaptability of the system in a dynamically changing environment and improves the stability and efficiency of the system under complex working conditions.

[0044] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0046] Figure 1 This is a flow chart of a voltage control method based on adaptive particle swarm optimization and SMC in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0047] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0048] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or their combinations;

[0049] Embodiment 1:

[0050] Embodiment 1 of the present invention provides a voltage control method based on adaptive particle swarm optimization and SMC, such as Figure 1 As shown in the figure, firstly, the particles in the search area are initialized, then the voltage and current of the photovoltaic cell are collected, the strategy is selected according to the current fitness calculation factor, the individual optimal value is calculated according to the particle optimization algorithm, and the global optimal value is further updated. It is iterated until the preset conditions are met, and the MPP is output. The photovoltaic cell is controlled to reach the target voltage through the SMC algorithm.

[0051] The specific steps include:

[0052] Step 1: Set the particle swarm optimization algorithm parameters.

[0053] In a specific implementation, first, the relevant parameters of the multi-swarm particle swarm optimization algorithm (AFSS-PSO) with adaptive factor selection strategy are set in detail, including the number of particles, inertia weight, cognitive coefficient, social coefficient, acceleration coefficient, maximum number of iterations, etc.

[0054] During the initialization process, the search area, i.e. the range of possible output voltages of the photovoltaic cell, must also be defined. Subsequently, the positions and velocities of all particles are randomly initialized within the preset search area, so that each particle represents a potential output voltage value of the photovoltaic cell.

[0055] Specifically, in order to improve the distribution quality of initial particles, thereby improving the search efficiency and convergence speed of the particle swarm algorithm (PSO), this embodiment establishes a new initialization method, namely, hierarchical random-adaptive chaotic initialization (SR-ACI), the steps are:

[0056] Step 1.1: Determine the search range for the PV cell output voltage.

[0057] In this embodiment, according to the voltage output range of the photovoltaic panel, the search area is defined as volt.

[0058] Step 1.2: Divide the search range into several levels, and the range of each level is a sub-interval of the total search range.

[0059] In this embodiment, the search range is divided into 40 levels, and the range of each level is 10 volts.

[0060] Step 1.3: Select a suitable adaptive chaotic map and use the Logistic mapping method.

[0061] Step 1.4: Use a linear or nonlinear decreasing parameter adjustment strategy to set the initial value and adjustment rule of the adaptive parameters.

[0062] In this example, the number of particle swarms is defined as 50, the acceleration factor is 0.5, and the maximum number of iterations is 50.

[0063] Step 1.5: Within each level, use adaptive chaotic mapping to generate initial particle positions and randomly generate initial particle velocities.

[0064] In this example, the positions and velocities of all particles are randomly initialized in the preset search area so that each particle represents a potential output voltage value of the photovoltaic cell. The particle swarm in the AFSS-PSO algorithm is divided into 5, including 2 basic subgroups s1 and s2, 1 adaptive subgroup s3, and 2 new exploration subgroups s4 and s5. The basic subgroups s1 and s2 are responsible for the basic search work, the adaptive subgroup s3 performs optimized search after receiving the information of the basic subgroup, and the new exploration subgroups s4 and s5 explore new search areas based on the previous subgroups.

[0065] Step 2: Obtain photovoltaic cell data and calculate fitness based on the photovoltaic cell data.

[0066] Step 2.1: Sample the output voltage and output current of the photovoltaic cell in real time.

[0067] In each iteration of the AFSS-PSO algorithm, the output voltage and output current of the photovoltaic cell are first sampled in real time. The frequency and accuracy of the sampling data need to be determined according to the specific application requirements to ensure the accuracy and reliability of the data. In this example, the sampling frequency is 2000Hz.

[0068] Step 2.2: Calculate the output power of the photovoltaic cell using the collected voltage and current data.

[0069] Step 2.3: Map the output voltage sampled by the photovoltaic cell directly to the current position of the particle, and use the calculated output power as the fitness function.

[0070] The calculated output power is used as the fitness function, as shown in Formula 1.

[0071] .

[0072] in, is the output power, is the output voltage, is the output current.

[0073] During the iterative calculation process of the particle swarm optimization algorithm of this embodiment, the optimization iteration goal is to maximize the fitness function by adjusting the positions of particles, and the objective function is the maximum value of the output power.

[0074] The positions and velocities of particles in the basic subgroups s1 and s2 are determined by the following equations:

[0075] ,

[0076] .

[0077] In the formula, represents the position of the particle, Represents the velocity of the particle. represents the sequence number of the particle in the subgroup, Represents the current iteration number, , Represents two basic subgroups, represents the inertia weight factor, and are group coefficients, representing cognitive coefficient and social coefficient respectively, and represent random numbers between [0,1], represents the optimal position in subgroups s1 and s2. is the optimal position among all subgroups.

[0078] The position and velocity of particles in the adaptive subgroup s3 are determined by the following formula:

[0079] ,

[0080] .

[0081] In the formula, , and are the fitness values ​​of the basic subgroups s1 and s2 respectively, and Represent random numbers between [0,1].

[0082] The position and velocity of particles in the exploration subgroup s4 are determined by the following formula:

[0083] ,

[0084] .

[0085] In the formula, , as well as satisfy .

[0086] The position and velocity of particles in the exploration subgroup s5 are determined by the following formula:

[0087] ,

[0088] .

[0089] Step 3: Use the particle swarm optimization algorithm with an adaptive adjustment factor selection strategy to iteratively calculate the optimal voltage value.

[0090] Among them, the adaptive adjustment factor selection strategy is used to dynamically adjust the parameter combination to balance the efficiency of global search and local development according to the fitness value of the current iteration, combined with the current search state and historical performance of the algorithm. Specifically, in order to better balance the efficiency of global search and local development, this embodiment couples the adjustment rules of dynamic inertia weight and dynamic group coefficient, and proposes a strategy of adaptive inertia and adaptive group coefficient. The steps are:

[0091] Step 3.1: Dynamically adjust the inertia weight: adjust the inertia weight according to the current number of iterations and group diversity .in, It is expressed by the following formula:

[0092] .

[0093] In the formula, and Represent the maximum and minimum limits of the inertia weight factor, represents the maximum number of iterations, and is the parameter of the control factor

[0094] Step 3.2: Dynamically adjust cognitive and social coefficients: Adjust the cognitive coefficient according to the current iteration number and social coefficient .

[0095] ,

[0096] .

[0097] In the formula, and They are and The initial value of .

[0098] Step 3.3: Update particle velocity and position: Update the particle velocity and position using the updated parameters to ensure that the particle position is within the search range.

[0099] Step 3.4: Evaluate particles and update individual optimal values ​​of particles: After iterating until the convergence condition is met or the set number of times is reached, the individual optimal value of the particle is obtained.

[0100] In a specific implementation, in each iteration, the basic particle group, the adaptive subgroup, and the exploration subgroup are evaluated in order. There is information interaction between different subgroups. First, the basic subgroups s1 and s2 are used. The fitness value of each particle in the subgroup is evaluated in order, that is, the output power at the current position is calculated. The fitness value of the current particle is compared with the best fitness value in the historical record of the particle. If the current fitness value is better than the historical best fitness value, the individual optimal value (Pbest) of the particle is updated. The updated Pbest represents the optimal solution found by the particle in the search process so far.

[0101] The specific steps are:

[0102] Step 3.4.1: Fitness value calculation, in each iteration, the basic subgroups s1 and s2, the adaptive subgroup s3, and the newly explored subgroups s4 and s5 are sequentially visited. And each particle in each particle group is sequentially visited; the position vector of the current particle (current_position_i) is obtained, where (i) represents the index of the particle; the fitness value (current_fitness_i) of the position vector (current_position_i) of the current particle is calculated using the objective function (f(current_position_i)). The fitness value reflects the performance of the particle at the current position.

[0103] Step 3.4.2: Comparison of historical best fitness values, obtain the individual best position (best_position_i) and the corresponding individual best fitness value (best_fitness_i) of the particle from the particle's historical records; compare the current fitness value (current_fitness_i) with the individual best fitness value (best_fitness_i); if the current fitness value (current_fitness_i) is better than the individual best fitness value (best_fitness_i), continue with the update step. In this example, it is a power maximization problem, and the condition is (current_fitness_i>best_fitness_i).

[0104] Step 3.4.3: Update the individual optimal fitness value, update the individual optimal fitness value of the particle (best_fitness_i) to the current fitness value (current_fitness_i); update the individual optimal position of the particle (best_position_i) to the current position vector (current_position_i).

[0105] Step 3.4.4: Update the global optimal value: Update the global optimal value according to the individual optimal value. After the individual optimal values ​​of all particles are updated, further compare all Pbest values ​​and select the value with the highest fitness as the current global optimal value (Gbest). The update of Gbest means that the optimal solution currently found in the entire particle swarm has been confirmed. This step ensures that the algorithm can record and use the global information in the particle swarm and continue to move towards a better solution.

[0106] Step 3.4.5: Check whether all subgroups have been evaluated. If there are subgroups that have not been evaluated, proceed to the evaluation of the next subgroup. Repeat steps 3.4.1-3.4.4 in the evaluation process. If the evaluation is complete, proceed to step 3.4.6.

[0107] Step 3.4.6: Confirm whether the termination condition (maximum number of iterations or convergence to the maximum value) has been reached. If the termination condition has not been reached, proceed to the next iteration. Repeat steps 3.4.1-3.4.5. If the termination condition has been reached, proceed to step 3.5

[0108] Step 3.5: Output MPP position: The global optimal value is taken as the maximum power point position. The photovoltaic cell output voltage corresponding to the maximum power point position is the optimal voltage value that maximizes the output power.

[0109] In a specific implementation, when the algorithm reaches a preset maximum number of iterations or reaches other termination conditions (such as the fitness value converges to a certain threshold), the optimization process is terminated and the current global optimal value is output as the maximum power point position. The photovoltaic cell output voltage corresponding to the MPP position is the optimal voltage value that maximizes the output power. The output MPP position will provide a control reference for the actual photovoltaic power generation system to achieve maximum power point tracking and optimize energy conversion efficiency.

[0110] Step 4: Use the sliding mode controller (SMC) to adjust the output voltage of the photovoltaic cell according to the optimal voltage value calculated by the adaptive particle swarm algorithm.

[0111] The sliding mode controller has strong robustness and anti-interference. When affected by external disturbances (such as load changes and power supply fluctuations), it can make the system state converge quickly to the sliding mode surface, thereby eliminating the negative impact of the disturbance. Combining the sliding mode controller with the improved particle swarm algorithm can give full play to their respective advantages. AFSS-PSO can accurately and quickly calculate the optimal voltage value at the maximum power point of the photovoltaic, and the sliding mode controller can ensure that the voltage remains stable near the maximum power point on this basis. The combination of the two not only improves the efficiency, robustness and response speed of the system, but also enhances the adaptability and stability of the system in a changing environment.

[0112] Step 4.1: Input the optimal voltage value into the sliding mode controller as the preset target voltage value.

[0113] Step 4.2: The sliding mode controller calculates a control signal for adjusting the output voltage of the photovoltaic cell based on the current real-time state variable of the photovoltaic cell and a preset target voltage value.

[0114] In a specific embodiment, the control signal is a pulse width modulation signal, and the generated pulse width modulation signal adjusts the output voltage of the photovoltaic cell by controlling the switching state of the IGBT. The duty cycle of the pulse width modulation signal is dynamically adjusted according to the deviation between the preset target voltage and the actual voltage to ensure that the photovoltaic cell can quickly and stably reach the target voltage value.

[0115] The sliding surface of the sliding mode controller is designed as follows:

[0116] .

[0117] In the formula, is the sliding surface, , To control the parameters, and They are the preset target voltage and actual voltage respectively.

[0118] Since the switch control signal of IGBT is only 0 and 1, the control rate of the sliding mode controller can be designed as:

[0119] .

[0120] Taking into account the frequency limit of the actual process, the control rate can be designed as:

[0121] .

[0122] In the formula, A custom very small number.

[0123] Embodiment 2:

[0124] Embodiment 2 of the present invention provides a voltage control system based on adaptive particle swarm optimization and SMC, including:

[0125] A parameter setting module, configured to set the particle swarm optimization algorithm parameters;

[0126] A data acquisition module is configured to acquire photovoltaic cell data and calculate fitness based on the photovoltaic cell data;

[0127] An adaptive optimization module is configured to iteratively calculate an optimal voltage value using a particle swarm optimization algorithm with an adaptive adjustment factor selection strategy, wherein the adaptive adjustment factor selection strategy is used to dynamically adjust a parameter combination according to a fitness value of a current iteration to balance the efficiency of global search and local development;

[0128] The voltage control module is configured to adjust the output voltage of the photovoltaic cell according to the optimal voltage value by using a sliding mode controller.

[0129] The steps involved in the above embodiments 2 and 3 correspond to the method embodiment 1. For the specific implementation methods, please refer to the relevant description part of embodiment 1.

[0130] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0131] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A voltage control method based on adaptive particle swarm optimization and SMC, characterized in that: The following steps are involved: Set the particle swarm optimization algorithm parameters; Obtain photovoltaic cell data, and calculate fitness based on the photovoltaic cell data; The optimal voltage value is iteratively calculated using a particle swarm optimization algorithm with an adaptive adjustment factor selection strategy, wherein the adaptive adjustment factor selection strategy is used to dynamically adjust the parameter combination according to the fitness value of the current iteration to balance the efficiency of global search and local development; The output voltage of the photovoltaic cell is adjusted according to the optimal voltage value by using a sliding mode controller; The specific steps of iteratively calculating the optimal voltage value using the particle swarm optimization algorithm with an adaptive adjustment factor selection strategy are as follows: Adjust the inertia weight according to the current iteration number and group diversity; The cognitive coefficient and social coefficient are adjusted according to the rate of change of the current optimal fitness value; Update the particle's velocity and position using the updated parameters to ensure that the particle's position is within the search range; After iterating until the convergence condition is met or the set number of times is reached, the optimal value of the individual particle is obtained; Update the global optimal value according to the individual optimal value; Dynamically adjust inertia weight: adjust inertia weight according to the current number of iterations and group diversity ;in, It is expressed by the following formula: ； In the formula, and Represent the maximum and minimum limits of the inertia weight factor, represents the maximum number of iterations, and is the parameter of the control factor; Dynamically adjust cognitive and social coefficients: adjust cognitive coefficients according to the current iteration number and social coefficient ; ； ； In the formula, and They are and The initial value of The particle swarm in the algorithm is divided into five, including two basic subgroups s1 and s2, one adaptive subgroup s3, and two new exploration subgroups s4 and s5. The basic subgroups s1 and s2 are responsible for basic search work, the adaptive subgroup s3 performs optimized search after receiving the information of the basic subgroup, and the new exploration subgroups s4 and s5 explore new search areas based on the previous subgroups. The positions and velocities of particles in the basic subgroups s1 and s2 are determined by the following equations: ， ， In the formula, represents the position of the particle, represents the velocity of the particle, represents the sequence number of the particle in the subgroup, Represents the current iteration number, , Represents two basic subgroups, represents the inertia weight factor, and are group coefficients, representing cognitive coefficient and social coefficient respectively, and represent random numbers between [0,1], represents the optimal position in subgroups s1 and s2, is the optimal position among all subgroups, The position and velocity of particles in the adaptive subgroup s3 are determined by the following formula: ， ， In the formula, , and are the fitness values ​​of the basic subgroups s1 and s2 respectively, and Represent random numbers between [0,1] respectively; The position and velocity of particles in the exploration subgroup s4 are determined by the following formula: ， ， In the formula, , as well as satisfy , The position and velocity of particles in the exploration subgroup s5 are determined by the following formula: ， 。 2. The voltage control method based on adaptive particle swarm optimization and SMC as claimed in claim 1, characterized in that: The specific steps to set the parameters of the particle swarm optimization algorithm are: Determine the search range of the photovoltaic cell output voltage; Divide the search range into several levels, and the range of each level is a sub-interval of the total search range; Select an adaptive chaotic map and use the Logistic mapping method; Set the initial values ​​and adjustment rules of adaptive parameters; Within each level, the initial particle positions are generated using an adaptive chaotic map, and the initial particle velocities are randomly generated.

3. The voltage control method based on adaptive particle swarm optimization and SMC as claimed in claim 1, characterized in that: The specific steps to obtain photovoltaic cell data and calculate fitness based on the photovoltaic cell data are as follows: Real-time sampling of the output voltage and output current of photovoltaic cells; Calculate the output power of the photovoltaic cell using the collected voltage and current data; The output voltage sampled by the photovoltaic cell is directly mapped to the current position of the particle, and the calculated output power is used as the fitness function.

4. The voltage control method based on adaptive particle swarm optimization and SMC as claimed in claim 1, characterized in that: During the iterative calculation process of the particle swarm optimization algorithm, the optimization iteration goal is to maximize the fitness function by adjusting the position of the particles, and the objective function is the maximum value of the output power.

5. The voltage control method based on adaptive particle swarm optimization and SMC as claimed in claim 1, characterized in that: The global optimal value is taken as the maximum power point position, and the photovoltaic cell output voltage corresponding to the maximum power point position is the optimal voltage value that maximizes the output power.

6. The voltage control method based on adaptive particle swarm optimization and SMC as claimed in claim 1, characterized in that: The specific steps of using the sliding mode controller to adjust the output voltage of the photovoltaic cell according to the optimal voltage value are: inputting the optimal voltage value into the sliding mode controller as a preset target voltage value; The sliding mode controller calculates a control signal for adjusting the output voltage of the photovoltaic cell based on the current real-time state variable of the photovoltaic cell and the preset target voltage value.

7. The voltage control method based on adaptive particle swarm optimization and SMC as claimed in claim 6, characterized in that: The control signal is a pulse width modulation signal, and the duty cycle of the pulse width modulation signal is dynamically adjusted according to the deviation between the preset target voltage and the actual voltage to ensure that the photovoltaic cell can quickly and stably reach the target voltage value.

8. The voltage control method based on adaptive particle swarm optimization and SMC as claimed in claim 7, characterized in that: The generated pulse width modulation signal regulates the output voltage of the photovoltaic cell by controlling the switching state of the IGBT.

9. Voltage control system based on adaptive particle swarm optimization and SMC, characterized in that: include: A parameter setting module, configured to set the particle swarm optimization algorithm parameters; A data acquisition module is configured to acquire photovoltaic cell data and calculate fitness based on the photovoltaic cell data; An adaptive optimization module is configured to iteratively calculate an optimal voltage value using a particle swarm optimization algorithm with an adaptive adjustment factor selection strategy, wherein the adaptive adjustment factor selection strategy is used to dynamically adjust a parameter combination according to a fitness value of a current iteration to balance the efficiency of global search and local development; A voltage control module is configured to adjust the output voltage of the photovoltaic cell according to an optimal voltage value using a sliding mode controller; The specific steps of iteratively calculating the optimal voltage value using the particle swarm optimization algorithm with an adaptive adjustment factor selection strategy are as follows: Adjust the inertia weight according to the current iteration number and group diversity; The cognitive coefficient and social coefficient are adjusted according to the rate of change of the current optimal fitness value; Update the particle's velocity and position using the updated parameters to ensure that the particle's position is within the search range; After iterating until the convergence condition is met or the set number of times is reached, the optimal value of the individual particle is obtained; Update the global optimal value according to the individual optimal value; Dynamically adjust inertia weight: adjust inertia weight according to the current number of iterations and group diversity ;in, It is expressed by the following formula: ； In the formula, and Represent the maximum and minimum limits of the inertia weight factor, represents the maximum number of iterations, and is the parameter of the control factor; Dynamically adjust cognitive and social coefficients: adjust cognitive coefficients according to the current iteration number and social coefficient ; ； ； In the formula, and They are and The initial value of The particle swarm in the algorithm is divided into five, including two basic subgroups s1 and s2, one adaptive subgroup s3, and two new exploration subgroups s4 and s5. The basic subgroups s1 and s2 are responsible for basic search work, the adaptive subgroup s3 performs optimized search after receiving the information of the basic subgroup, and the new exploration subgroups s4 and s5 explore new search areas based on the previous subgroups. The positions and velocities of particles in the basic subgroups s1 and s2 are determined by the following equations: ， ， In the formula, represents the position of the particle, represents the velocity of the particle, represents the sequence number of the particle in the subgroup, Represents the current iteration number, , Represents two basic subgroups, represents the inertia weight factor, and are group coefficients, representing cognitive coefficient and social coefficient respectively, and represent random numbers between [0,1], represents the optimal position in subgroups s1 and s2, is the optimal position among all subgroups, The position and velocity of particles in the adaptive subgroup s3 are determined by the following formula: ， ， In the formula, , and are the fitness values ​​of the basic subgroups s1 and s2 respectively, and Represent random numbers between [0,1] respectively; The position and velocity of particles in the exploration subgroup s4 are determined by the following formula: ， ， In the formula, , as well as satisfy , The position and velocity of particles in the exploration subgroup s5 are determined by the following formula: ， 。

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