Photovoltaic partial shading MPPT (Maximum Power Point Tracking) control method, system and equipment based on adaptive differential evolution and medium

Abstract

The invention discloses a photovoltaic partial shading MPPT (Maximum Power Point Tracking) control method, system and equipment based on adaptive differential evolution and a medium, and belongs to the technical field of MPPT control, and the method comprises the following steps: initializing control parameters of an adaptive differential evolution algorithm; generating a plurality of candidate solutions based on the control parameters; obtaining the output voltage and the output current of the photovoltaic array, and calculating a performance evaluation value corresponding to each candidate solution; executing an evolutionary optimization operation based on the performance evaluation value to generate a new candidate solution population; and judging whether an iteration termination condition is met or not, and if so, taking the candidate solution with the optimal current evaluation value as a control parameter of the direct-current converter. On the basis of the self-adaptive differential evolution method, the convergence speed can be increased, the searching capability of the global optimal value is enhanced, it is guaranteed that the photovoltaic array accurately operates at the global maximum power point under the complex working condition that the local shading superposition irradiation intensity frequently changes, and therefore the operation efficiency of a photovoltaic power generation system is improved.

Description

Technical Field

[0001] This invention relates to the field of MPPT control technology, specifically to a photovoltaic local shading MPPT control method, system, device, and medium based on adaptive differential evolution. Background Technology

[0002] The output power of a photovoltaic (PV) array varies due to factors such as sunlight and temperature. Under uniform illumination, the output characteristic curve of a PV array exhibits a single-peak characteristic, meaning there is a unique maximum power point (MPP). However, in practical applications, PV arrays often experience localized shading effects due to building obstructions, cloud movement, or dirt accumulation, leading to a significant decrease in their output power. This is because localized shading causes the power-voltage (PU) characteristic curve of the PV array to exhibit multi-peak characteristics. The PV array may operate at a local extreme point rather than the global maximum power point (GMPP), thus reducing its operating efficiency.

[0003] Traditional MPPT algorithms (such as the perturbation-observation method and the incremental conductance method) can achieve good MPPT control performance under uniform illumination conditions. However, under partial shading conditions, traditional methods are prone to getting trapped in local extrema and failing. In other words, they cannot meet the global optimization requirements in multi-peak scenarios. Furthermore, when partial shading is combined with rapid changes in irradiance, traditional MPPT algorithms may oscillate due to frequent perturbations, exacerbating misjudgments of GMPP. Therefore, to achieve accurate MPPT control of photovoltaic arrays under partial shading conditions, algorithms with stronger global search capabilities are needed.

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a local shading MPPT control method based on adaptive differential evolution suitable for two-stage grid-connected photovoltaic power generation systems, aiming to achieve accurate control of the photovoltaic array MPPT under local shading conditions and improve system efficiency. Summary of the Invention

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

[0006] Therefore, the technical problem solved by this invention is: how to enable a photovoltaic array to operate at the global maximum power point (GMPP) under partial shading conditions, thereby improving the operating efficiency of the photovoltaic power generation system under complex operating conditions.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a photovoltaic local shading MPPT control method based on adaptive differential evolution, comprising,

[0008] In a two-stage photovoltaic power generation system where the photovoltaic array is connected to the DC bus and then connected to the grid via a grid-connected inverter, the control parameters of the adaptive differential evolution algorithm are initialized.

[0009] Multiple candidate solutions are generated based on the control parameters, and the candidate solutions are used to represent the duty cycle of the DC converter in the photovoltaic system.

[0010] Obtain the output voltage and output current of the photovoltaic array, and calculate the performance evaluation value corresponding to each candidate solution based on the voltage and current values;

[0011] Based on the performance evaluation values, perform evolutionary optimization operations to generate a new population of candidate solutions;

[0012] Determine whether the iteration termination condition is met. If it is met, use the candidate solution with the best current evaluation value as the control parameter of the DC converter to achieve maximum power point tracking control of the photovoltaic array.

[0013] As a preferred embodiment of the photovoltaic local shading MPPT control method based on adaptive differential evolution described in this invention, the generation of multiple candidate solutions includes: within a preset search space, generating multiple candidate solutions representing the duty cycle of the DC converter in a random manner based on the control parameters, and constructing an initial population;

[0014] The control parameters include parameters for setting the initial population size, search boundary parameters for limiting the range of candidate solution values, and related settings for defining the duty cycle representation format.

[0015] As a preferred embodiment of the photovoltaic local shading MPPT control method based on adaptive differential evolution described in this invention, the calculation of the performance evaluation value corresponding to each candidate solution includes setting the working state of the DC converter according to the duty cycle corresponding to each candidate solution, obtaining the output voltage and output current of the photovoltaic array in the working state, and taking the product of the two as the performance evaluation value of the candidate solution.

[0016] As a preferred embodiment of the photovoltaic partial shading MPPT control method based on adaptive differential evolution described in this invention, the evolutionary optimization operation includes: performing a mutation operation on candidate solutions in the current population to generate mutated solutions; performing a cross operation between the mutated solutions and the original candidate solutions according to preset rules to generate experimental solutions; and using a selection strategy to determine the next generation population based on the performance evaluation values ​​of the experimental solutions and the original candidate solutions.

[0017] As a preferred embodiment of the photovoltaic local shading MPPT control method based on adaptive differential evolution described in this invention, the scaling factor used in the mutation operation adopts a dynamically decreasing adaptive adjustment strategy according to the current iteration number. The adjustment process is based on the linear interpolation between the preset maximum scaling factor and the minimum scaling factor, and the value of the scaling factor is gradually reduced as the iteration number increases.

[0018] As a preferred embodiment of the photovoltaic local shading MPPT control method based on adaptive differential evolution described in this invention, the crossover probability used in the crossover operation adopts a dynamically decreasing adaptive adjustment strategy according to the current iteration number. The adjustment process is based on the linear interpolation between the preset maximum crossover probability and the minimum crossover probability, and the crossover probability value is gradually reduced as the iteration number increases.

[0019] The beneficial effects of this preferred technical solution are as follows: by dynamically decreasing the crossover probability in the differential evolution algorithm based on the number of iterations, the population diversity of the algorithm in the early stage is effectively enhanced, and the local search capability of the solution space is improved in the later convergence process. Thus, in the complex photovoltaic local shading environment, it is easier to escape the local optimum and improve the convergence accuracy and stability of maximum power point tracking (MPPT).

[0020] As a preferred embodiment of the photovoltaic local shading MPPT control method based on adaptive differential evolution described in this invention, the step of determining whether the iteration termination condition is met includes: determining whether the current iteration number has reached the set maximum iteration number; if not, calculating the relative rate of change between the current iteration's optimal performance evaluation value and the previous iteration's optimal performance evaluation value; when the relative rate of change is less than a preset threshold, counting continuously once; if the continuous counting reaches a preset number of rounds, terminating the iteration and outputting the current optimal candidate solution.

[0021] The beneficial effects of this preferred technical solution are as follows: by setting a dual iteration termination judgment mechanism, that is, combining the judgment criteria of the maximum number of iterations and the relative change rate of the performance evaluation value, it can avoid tracking failure caused by premature convergence, and terminate the calculation in time when the performance improvement slows down, reduce invalid iterations, improve the operating efficiency and real-time performance of the MPPT control method, and enhance its engineering applicability in actual photovoltaic power generation systems.

[0022] This invention provides a photovoltaic local shading MPPT control system based on adaptive differential evolution.

[0023] To address the aforementioned technical problems, this invention provides the following technical solution: a photovoltaic partial shading MPPT control system based on adaptive differential evolution, comprising: a parameter initialization module for setting control parameters of an adaptive differential evolution algorithm in a two-stage photovoltaic power generation system; a candidate solution generation module for generating multiple candidate solutions representing the duty cycle of a DC-DC converter based on the control parameters; a data acquisition and evaluation module for acquiring the output voltage and output current of the photovoltaic array and calculating the performance evaluation value corresponding to each candidate solution based on the voltage and current; an optimization calculation module for performing differential mutation and crossover operations based on the performance evaluation value to form a new population of candidate solutions; a control judgment and output module for judging whether the iteration termination condition is met and outputting the current optimal candidate solution; and an MPPT control module for using the optimal candidate solution as the control parameters of the DC-DC converter to achieve maximum power point tracking control of the photovoltaic array.

[0024] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the photovoltaic local shading MPPT control method based on adaptive differential evolution.

[0025] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the photovoltaic local shading MPPT control method based on adaptive differential evolution.

[0026] The beneficial effects of this invention are as follows: The photovoltaic local shading maximum power point tracking control method based on the adaptive differential evolution algorithm can effectively improve the algorithm's global optimization ability in complex search spaces. By dynamically adjusting the scaling ratio and crossover probability of the mutation factor during the iteration process, the diversity of the population and the adaptability of the evolutionary direction are significantly enhanced, thereby rapidly approaching and locking the global optimum in nonlinear PU characteristic curves with multiple local extrema.

[0027] Compared to traditional perturbation observation methods or incremental conductance methods, this method exhibits stronger robustness and convergence efficiency, effectively avoiding getting trapped in local optima. Even in scenarios with dynamic shading changes caused by building obstruction, cloud cover variations, or component contamination, especially those with frequent fluctuations in irradiance, it can maintain stable operation, enabling the photovoltaic array to continuously track the global maximum power point.

[0028] This invention not only improves the response speed and accuracy of MPPT control under multi-peak output conditions, but also enhances the adaptability of the control system to environmental disturbances, thereby significantly improving the operating efficiency and energy utilization of photovoltaic power generation systems in complex application scenarios. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used 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 those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 The above is a flowchart of a photovoltaic local shading MPPT control method based on adaptive differential evolution, provided as an embodiment of the present invention.

[0031] Figure 2 This is a structural diagram of a two-stage grid-connected photovoltaic power generation system based on an adaptive differential evolution photovoltaic local shading MPPT control method, provided as an embodiment of the present invention.

[0032] Figure 3 The diagram below illustrates the principle of an adaptive differential evolution-based MPPT control method for photovoltaic local shading, as provided in one embodiment of the present invention. Detailed Implementation

[0033] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0034] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a photovoltaic local shading MPPT control method based on adaptive differential evolution, comprising:

[0035] Step 101: In a two-stage photovoltaic power generation system where the photovoltaic array is connected to the DC bus and then to the grid via a grid-connected inverter, initialize the control parameters of the adaptive differential evolution algorithm. Step 102: Generate multiple candidate solutions based on the control parameters. These candidate solutions represent the duty cycle of the DC converter in the photovoltaic system. Step 103: Obtain the output voltage and current of the photovoltaic array, and calculate the performance evaluation value corresponding to each candidate solution based on the voltage and current values. Step 104: Perform evolutionary optimization based on the performance evaluation values ​​to generate a new population of candidate solutions. Step 105: Determine whether the iteration termination condition is met. If it is, use the candidate solution with the best current evaluation value as the control parameter of the DC converter to achieve maximum power point tracking control of the photovoltaic array.

[0036] In step 102, generating multiple candidate solutions includes: within a preset search space, generating multiple candidate solutions representing the duty cycle of the DC-DC converter in a random manner based on the control parameters, and constructing an initial population; wherein, the control parameters include parameters for setting the initial population size, search boundary parameters for limiting the range of candidate solution values, and related settings for defining the duty cycle representation format.

[0037] In step 103, calculating the performance evaluation value corresponding to each candidate solution includes setting the working state of the DC converter according to the duty cycle corresponding to each candidate solution, obtaining the output voltage and output current of the photovoltaic array in the working state, and taking the product of the two as the performance evaluation value of the candidate solution.

[0038] In step 104, the evolutionary optimization operation includes: performing a mutation operation on the candidate solutions in the current population to generate mutated solutions; performing a crossover operation between the mutated solutions and the original candidate solutions according to preset rules to generate experimental solutions; and using a selection strategy to determine the next generation population based on the performance evaluation values ​​of the experimental solutions and the original candidate solutions.

[0039] The scaling factor used in the mutation operation adopts a dynamically decreasing adaptive adjustment strategy based on the current iteration number. The adjustment process is based on the linear interpolation between the preset maximum scaling factor and the minimum scaling factor, and the value of the scaling factor is gradually reduced as the iteration number increases.

[0040] The crossover probability used in the crossover operation adopts a dynamically decreasing adaptive adjustment strategy based on the current iteration number. The adjustment process is based on the linear interpolation between the preset maximum crossover probability and the minimum crossover probability, and the crossover probability value is gradually reduced as the iteration number increases.

[0041] In step 105, determining whether the iteration termination condition is met includes determining whether the current iteration count has reached the set maximum iteration count; if not, calculating the relative rate of change between the current iteration's optimal performance evaluation value and the previous iteration's optimal performance evaluation value; when the relative rate of change is less than a preset threshold, counting once consecutively; if the consecutive counting reaches a preset number of rounds, terminating the iteration and outputting the current optimal candidate solution.

[0042] In a preferred embodiment of the present invention, the control parameters are set as follows: the initial population size in the adaptive differential evolution algorithm is set to a fixed value, such as 30 individuals; the upper and lower boundaries of the search space are set to the minimum allowable value of the DC converter duty cycle of 0.2 and the maximum allowable value of 0.9, respectively; simultaneously, the duty cycle is represented by a floating-point real number to ensure the continuity of search accuracy. These parameters are pre-configured before the photovoltaic system is put into operation, and are suitable for stable scenarios with standard illumination and small load fluctuations.

[0043] The advantages of this preferred technical solution are: it simplifies the controller design and deployment process by fixing the initialization parameters, reduces the difficulty of system debugging, and provides good convergence performance and stability under normal operating conditions, which is conducive to the rapid implementation of projects and the integration of controller hardware.

[0044] In an optional embodiment of the present invention, the control parameters can be dynamically generated from the historical operating data of the photovoltaic array. The initial population size is automatically adjusted according to the fluctuation range of photovoltaic power; the search boundary expands or contracts based on the current irradiance and temperature variation; and the duty cycle representation can be switched to a fixed-point format or quantized at approximately integer multiples according to the control resolution to adapt to different precision control requirements. This parameter setting method performs self-learning in the early stages of system operation and can be updated in real time during operation.

[0045] In a preferred embodiment of the present invention, the performance evaluation value is calculated as follows: based on the measured values ​​of the current output voltage and output current of the photovoltaic array, it is calculated by multiplying the values ​​by the instantaneous values ​​within the real-time acquisition period, i.e., performance evaluation value = output voltage * output current. This value represents the photovoltaic output power corresponding to the current duty cycle and is used to measure the merits of the candidate solution under the current operating state.

[0046] The advantages of this preferred technical solution are: the instantaneous power calculation method does not require long-term data accumulation, has a fast response speed, is suitable for working conditions with frequent fluctuations in light intensity or complex shading conditions, and helps to improve the real-time adaptability of the MPPT control system in non-stable environments.

[0047] In an optional embodiment of the present invention, the performance evaluation value can be estimated using a weighted average power over a short period of time. Specifically, multiple sets of voltage and current values ​​are continuously collected within a control cycle, and the multiple product results are summed and normalized using a time-weighted or exponentially decaying method to obtain a smoothed power estimate. This method can reduce the impact of measurement errors on the evaluation results and is suitable for control environments with frequent power grid disturbances or limited sampling accuracy.

[0048] In a preferred embodiment of the present invention, the evolutionary optimization operation includes three basic processes: mutation, crossover, and selection. The mutation operation uses a differential mutation strategy based on the best individual in the current population to generate a mutated solution with enhanced perturbation. The crossover operation generates a trial solution based on the gene locus exchange between the mutated solution and the original individual. The selection operation compares the performance evaluation value corresponding to the trial solution and the original individual, and retains the one with the better evaluation value to enter the next generation of the population.

[0049] The advantages of this preferred technical solution are as follows: by introducing the current best individual to participate in the differential mutation process, the directionality and convergence efficiency of the search process can be improved; combined with the selection mechanism of the best-preserving strategy, it can continuously explore the global optimal solution while ensuring the stability of the algorithm, and is suitable for multi-peak search problems under local shading.

[0050] In an optional embodiment of the present invention, the evolutionary optimization operation may further include an adaptive perturbation control strategy for individual distribution density: when the population is concentrated in a specific area, the differential perturbation amplitude is automatically increased to avoid getting trapped in local optima; conversely, the perturbation step size is reduced to improve the local search accuracy of the solution space. Furthermore, a dynamic adjustment mechanism for the acceptance probability of experimental solutions may be introduced to adapt to the search needs at different stages.

[0051] Example 2, refer to Figures 1-3 As an embodiment of the present invention, based on the previous embodiment, a photovoltaic local shading MPPT control method based on adaptive differential evolution is provided, comprising:

[0052] In step 101, the parameters of the adaptive differential evolution algorithm are initialized, including: population size N, maximum number of iterations T. max The search uses an upper bound U and a lower bound L. The population size N is set to 5, and the maximum number of iterations T is... max Set to 20, the upper search bound U and the lower search bound L correspond to the upper and lower limits of the DC / DC duty cycle, respectively set to U = 0.95 and L = 0.05.

[0053] In step 102, an initial solution for the population is generated using a random method, as shown in formula (1).

[0054]

[0055] In the formula, X represents the solution vector; the subscript i of X represents the i-th individual in the population, i.e., i = 1, 2, ..., N, and the value of the individual is the duty cycle of the DC / DC converter; the superscript 0 of X represents the initial solution; r ∈ [0 1] is a normally distributed random number.

[0056] In step 103, the fitness value of each individual is calculated according to formula (2), and the optimal individual is determined.

[0057] f(·)=U pv ·I pv (2)

[0058] In the formula, f(·) represents the fitness function; U pv I is the output voltage of the photovoltaic array. pv This represents the output current of the photovoltaic array. U pv and I pv It can be measured by sensors.

[0059] In step 104, the individual is mutated using formula (3) to generate a mutation vector.

[0060]

[0061] In the formula, t is the current iteration number, t = 0, 1, ..., T max ; The optimal individual for the current iteration number. For the individual after mutation at the current iteration number, r1≠r2 is the index of a randomly selected individual; F t is the scaling factor for the current iteration number, which adopts the adaptive adjustment strategy shown in formula (4).

[0062]

[0063] In the formula, F max The maximum scaling factor is set to 0.9; F min The minimum scaling factor is 0.4.

[0064] In step 104, the individuals are cross-operated using formula (5) to generate experimental vectors.

[0065]

[0066] In the formula, The individual generated after the crossover of the current iteration number; rand(0, 1) is a random number in the range (0, 1); i rand Index for randomly selected individuals; CR t The crossover probability for the current iteration number is given by formula (6), which employs an adaptive adjustment strategy.

[0067]

[0068] In the formula, CR max To maximize the crossover probability, we set it to 1; CR min The minimum crossover probability is set to 0.1.

[0069] The next generation of individuals is selected using the greedy strategy shown in formula (7).

[0070]

[0071] In the formula, f(·) represents the fitness function, and its formula is shown in equation (2). Step (7) Determine whether the current iteration number is equal to the maximum iteration number T. max If no, proceed to the next step; if yes, proceed to step (9).

[0072] Determine if the difference between the optimal fitness value of the current iteration and the optimal fitness value of the previous iteration is less than a preset threshold ε, as shown in equation (8). If yes, proceed to the next step; otherwise, proceed to step (3).

[0073]

[0074] In the formula, τ is the number of iterations, τ = 1, 2, ..., T max ; This represents the optimal fitness value for the current iteration number. This is the optimal fitness value after the previous iteration. ε is set to 0.01.

[0075] Determine whether the changes in irradiance and temperature values ​​at the current iteration number compared to those at the previous iteration number exceed preset values, as shown in equation (9). If the condition is met, begin the search for a new Global Maximum Power Point (GMPP) and return to step (2); if the condition is not met, proceed to the next step.

[0076]

[0077] In the formula, Irr represents the irradiance value, with units of W / m². 2 Tem represents the temperature value in °C. Both values ​​are obtained from the sensor. δ and θ are preset constants, set to δ = 100 W / m. 2 And θ = 10℃.

[0078] Output the optimal solution.

[0079] Determine whether to force exit the MPPT program. If yes, proceed to the next step; otherwise, proceed to step: determine whether the changes in irradiance and temperature values ​​at the current iteration number compared to the previous iteration number exceed preset values. The algorithm ends.

[0080] It should be noted that the combination of the implementation examples and application scenarios is mainly reflected by formulas (1) and (9). Formula (1) indicates that the population of differential evolution is the duty cycle of the DC / DC converter. Changing the duty cycle can change the output voltage and current of the photovoltaic module, thereby realizing the GMPP search process. Formula (9) requires continuous calculation using the irradiance Irr value and temperature Tem value collected by the sensor to determine whether the GMPP search objective has been achieved. The other steps are the iterative process of the differential evolution algorithm itself.

[0081] Example 3 is an embodiment of the present invention, which provides a photovoltaic partial shading MPPT control system based on adaptive differential evolution, including: a parameter initialization module for setting control parameters of the adaptive differential evolution algorithm in a two-stage photovoltaic power generation system; a candidate solution generation module for generating multiple candidate solutions representing the duty cycle of the DC-DC converter based on the control parameters; a data acquisition and evaluation module for acquiring the output voltage and output current of the photovoltaic array and calculating the performance evaluation value corresponding to each candidate solution based on the voltage and current; an optimization calculation module for performing differential mutation and crossover operations based on the performance evaluation value to form a new population of candidate solutions; a control judgment and output module for judging whether the iteration termination condition is met and outputting the current best-performing candidate solution; and an MPPT control module for using the best candidate solution as the control parameters of the DC-DC converter to realize the maximum power point tracking control of the photovoltaic array.

[0082] This embodiment also provides an electronic device applicable to a photovoltaic partial shading MPPT control method based on adaptive differential evolution, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the photovoltaic partial shading MPPT control method based on adaptive differential evolution as proposed in the above embodiment.

[0083] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a photovoltaic local shading MPPT control method based on adaptive differential evolution as proposed in the above embodiment.

[0084] The storage medium proposed in this embodiment and the method for implementing a photovoltaic local shading MPPT control based on adaptive differential evolution proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0085] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A photovoltaic local shading MPPT control method based on adaptive differential evolution, characterized in that: include, In a two-stage photovoltaic power generation system where the photovoltaic array is connected to the DC bus and then connected to the grid via a grid-connected inverter, the control parameters of the adaptive differential evolution algorithm are initialized. Multiple candidate solutions are generated based on the control parameters, and the candidate solutions are used to represent the duty cycle of the DC converter in the photovoltaic system. Obtain the output voltage and output current of the photovoltaic array, and calculate the performance evaluation value corresponding to each candidate solution based on the voltage and current values; Based on the performance evaluation values, perform evolutionary optimization operations to generate a new population of candidate solutions; Determine whether the iteration termination condition is met. If it is met, use the candidate solution with the best current evaluation value as the control parameter of the DC converter to achieve maximum power point tracking control of the photovoltaic array.

2. The photovoltaic local shading MPPT control method based on adaptive differential evolution as described in claim 1, characterized in that: The generation of multiple candidate solutions includes: within a preset search space, generating multiple candidate solutions representing the duty cycle of the DC-DC converter in a random manner based on the control parameters, and constructing an initial population; wherein, the control parameters include parameters for setting the initial population size, search boundary parameters for limiting the range of candidate solution values, and relevant settings for defining the duty cycle representation format.

3. The photovoltaic local shading MPPT control method based on adaptive differential evolution as described in claim 2, characterized in that: The calculation of the performance evaluation value corresponding to each candidate solution includes setting the working state of the DC converter according to the duty cycle corresponding to each candidate solution, obtaining the output voltage and output current of the photovoltaic array in the working state, and taking the product of the two as the performance evaluation value of the candidate solution.

4. The photovoltaic local shading MPPT control method based on adaptive differential evolution as described in claim 3, characterized in that: The evolutionary optimization operation includes performing a mutation operation on candidate solutions in the current population to generate mutated solutions; The mutated solution is cross-operated with the original candidate solution according to preset rules to generate an experimental solution; Based on the performance evaluation values ​​of the experimental solution and the original candidate solutions, a selection strategy is adopted to determine the next generation population.

5. The photovoltaic local shading MPPT control method based on adaptive differential evolution as described in claim 4, characterized in that: The scaling factor used in the mutation operation adopts a dynamically decreasing adaptive adjustment strategy based on the current iteration number. The adjustment process is based on the linear interpolation between the preset maximum scaling factor and the minimum scaling factor, and the value of the scaling factor is gradually reduced as the iteration number increases.

6. The photovoltaic local shading MPPT control method based on adaptive differential evolution as described in claim 4, characterized in that: The crossover probability used in the crossover operation adopts a dynamically decreasing adaptive adjustment strategy based on the current iteration number. The adjustment process is based on the linear interpolation between the preset maximum crossover probability and the minimum crossover probability, and the crossover probability value is gradually reduced as the iteration number increases.

7. The photovoltaic local shading MPPT control method based on adaptive differential evolution as described in claim 4, characterized in that: The determination of whether the iteration termination condition is met includes determining whether the current iteration count has reached the set maximum iteration count; If not reached, calculate the relative rate of change between the current iteration's best performance evaluation value and the previous iteration's best performance evaluation value; When the relative rate of change is less than a preset threshold, count once consecutively. If the continuous counting reaches a preset number of rounds, the iteration terminates and the current optimal candidate solution is output.

8. A photovoltaic partial shading MPPT control system based on adaptive differential evolution, employing the photovoltaic partial shading MPPT control method based on adaptive differential evolution as described in any one of claims 1 to 7, characterized in that, Includes: a parameter initialization module, used to set the control parameters of the adaptive differential evolution algorithm in a two-stage photovoltaic power generation system; The candidate solution generation module is used to generate multiple candidate solutions representing the duty cycle of the DC-DC converter based on the control parameters. The data acquisition and evaluation module is used to acquire the output voltage and output current of the photovoltaic array, and calculate the performance evaluation value corresponding to each candidate solution based on the voltage and current. The optimization calculation module is used to perform differential mutation and crossover operations based on the performance evaluation value to form a new candidate solution population; The control judgment and output module is used to determine whether the iteration termination condition is met and output the current best performing candidate solution; The MPPT control module is used to use the optimal candidate solution as the control parameter of the DC converter to realize the maximum power point tracking control of the photovoltaic array.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the photovoltaic local shading MPPT control method based on adaptive differential evolution as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the photovoltaic local shading MPPT control method based on adaptive differential evolution as described in any one of claims 1 to 7.

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