Photovoltaic power generation system parameter identification method and system based on swarm intelligence optimization algorithm

Abstract

The invention discloses a photovoltaic power generation system parameter identification method and system based on a swarm intelligence optimization algorithm, and the method comprises the steps: obtaining the historical operation data of a photovoltaic power generation system, building a data set containing the influence of environmental factors, and constructing a photovoltaic power generation prediction model; initializing a parameter search space by adopting a particle swarm optimization algorithm, and simulating and calculating the output power of the photovoltaic system; establishing a nonlinear mapping relation between the output power of the photovoltaic system and the environmental factors, and calculating to obtain a predicted value of the output power of the system; evaluating the importance of each parameter on the system output power through a random forest algorithm, and screening out key parameters; and the optimized parameter value is substituted into the photovoltaic power generation prediction model, the system output power is calculated, the output power is compared with a measured value, and if the error between the output power and the measured value is within an allowable range, a final parameter identification result is output. According to the invention, the accuracy and efficiency of parameter identification of the photovoltaic power generation system are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a photovoltaic power generation system parameter identification method and system based on a swarm intelligence optimization algorithm. Background Art

[0002] In the photovoltaic power generation system, photovoltaic cells and inverters are two key components, and their performance directly affects the power generation efficiency of the entire system. However, the actual working parameters of these components often deviate from the theoretical design values, resulting in the system failing to achieve optimal performance. In order to accurately describe the actual operating state of the photovoltaic power generation system, it is necessary to establish an accurate mathematical model and identify the key parameters in the model. Traditional parameter identification methods, such as the least squares method, often do not work well when dealing with nonlinear and multimodal problems, and are prone to fall into local optimal solutions. The photovoltaic power generation system has the characteristics of strong nonlinearity and multivariable coupling, making parameter identification more difficult. In addition, the photovoltaic power generation system is also affected by environmental factors, such as temperature and irradiance. Changes in these factors will cause the drift of system parameters, further increasing the difficulty of parameter identification. Therefore, how to accurately identify the key parameters of the photovoltaic power generation system under complex environmental conditions is a technical problem that needs to be solved urgently. This requires an optimization algorithm that can perform global search in high-dimensional, nonlinear space, and at the same time has a certain degree of robustness to adapt to changes in environmental factors. Summary of the invention

[0003] In order to solve the technical problems existing in the above-mentioned prior art, the present invention proposes a photovoltaic power generation system parameter identification method and system based on a swarm intelligence optimization algorithm to improve the accuracy and efficiency of photovoltaic power generation system parameter identification.

[0004] On the one hand, to achieve the above-mentioned purpose, the present invention provides a photovoltaic power generation system parameter identification method based on a swarm intelligence optimization algorithm, comprising:

[0005] Acquire historical operation data of the photovoltaic power generation system, establish a data set including the influence of environmental factors, and construct a photovoltaic power generation prediction model through the data set including the influence of environmental factors;

[0006] Initializing the parameter search space by using a particle swarm optimization algorithm, setting hyperparameters, inputting the hyperparameters into the photovoltaic power generation prediction model, and simulating and calculating the output power of the photovoltaic system;

[0007] Through the support vector regression model, a nonlinear mapping relationship between the output power of the photovoltaic system and environmental factors is established. The parameter estimation value obtained by the particle swarm optimization algorithm is input into the support vector regression model to calculate the predicted value of the system output power. If the error between the predicted value and the measured value exceeds a preset threshold, the particle swarm search strategy is readjusted.

[0008] The importance of each parameter to the system output power is evaluated by a random forest algorithm, and key parameters are screened out. If the importance of a parameter among the key parameters is lower than a preset value, the key parameter is removed from the optimization process;

[0009] The optimized parameter values ​​are substituted into the photovoltaic power generation prediction model, the system output power is calculated, and the output power is compared with the measured value. If the error between the two is within the allowable range, the final parameter identification result is output.

[0010] On the other hand, to achieve the above-mentioned purpose, the present invention also provides a photovoltaic power generation system parameter identification system based on a swarm intelligence optimization algorithm, comprising:

[0011] A data acquisition module, used to acquire historical operation data of the photovoltaic power generation system, establish a data set including the influence of environmental factors based on the historical operation data, and construct a photovoltaic power generation prediction model through the data set including the influence of environmental factors;

[0012] The parameter initialization module is used to initialize the parameter search space using the particle swarm optimization algorithm, set hyperparameters such as particle swarm size, number of iterations, and learning factor, and define the mean square error between the system output power and the measured power as the fitness function;

[0013] The model building module establishes a nonlinear mapping relationship between the output power of the photovoltaic system and environmental factors through a support vector regression model, inputs the parameter estimation value obtained by the particle swarm optimization algorithm into the support vector regression model, and calculates the predicted value of the system output power. If the error between the predicted value and the measured value exceeds a preset threshold, the particle swarm search strategy is readjusted;

[0014] A parameter evaluation module is used to evaluate the importance of each parameter to the system output power through a random forest algorithm, and screen out key parameters. If the importance of a parameter among the key parameters is lower than a preset value, the key parameter is removed from the optimization process;

[0015] The result output module is used to substitute the optimized parameter values ​​into the photovoltaic power generation prediction model, calculate the system output power, compare the output power with the measured value, and output the final parameter identification result if the error between the two is within the allowable range.

[0016] Compared with the prior art, the present invention has the following advantages and technical effects:

[0017] The method of the present invention obtains historical operation data to establish a data set including the influence of environmental factors, uses a particle swarm optimization algorithm to initialize the parameter search space, and combines a support vector regression algorithm to establish a nonlinear mapping relationship between output power and environmental factors; through an iterative optimization process, the particle swarm search strategy is continuously adjusted, and a random forest algorithm is used to evaluate the importance of parameters and screen key parameters, and finally the optimized parameter values ​​are obtained. These parameters are substituted into a mathematical model, the output power is calculated and compared with the measured value, and the final parameter identification result is output within the allowable error range. The present invention improves the accuracy and efficiency of parameter identification of photovoltaic power generation systems, and provides a reliable basis for system performance optimization and fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 A flow chart of a photovoltaic power generation system parameter identification method based on a swarm intelligence optimization algorithm according to an embodiment of the present invention;

[0020] Figure 2 This is a structural diagram of a photovoltaic power generation system parameter identification system based on a swarm intelligence optimization algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] This embodiment proposes a photovoltaic power generation system parameter identification method based on a swarm intelligence optimization algorithm. Figure 1 ,include:

[0024] Acquire historical operation data of the photovoltaic power generation system, establish a data set including the influence of environmental factors, and construct a photovoltaic power generation prediction model through the data set including the influence of environmental factors;

[0025] Initializing the parameter search space by using a particle swarm optimization algorithm, setting hyperparameters, inputting the hyperparameters into the photovoltaic power generation prediction model, and simulating and calculating the output power of the photovoltaic system;

[0026] Through the support vector regression model, a nonlinear mapping relationship between the output power of the photovoltaic system and environmental factors is established. The parameter estimation value obtained by the particle swarm optimization algorithm is input into the support vector regression model to calculate the predicted value of the system output power. If the error between the predicted value and the measured value exceeds a preset threshold, the particle swarm search strategy is readjusted.

[0027] The importance of each parameter to the system output power is evaluated by a random forest algorithm, and key parameters are screened out. If the importance of a parameter among the key parameters is lower than a preset value, the key parameter is removed from the optimization process;

[0028] The optimized parameter values ​​are substituted into the photovoltaic power generation prediction model, the system output power is calculated, and the output power is compared with the measured value. If the error between the two is within the allowable range, the final parameter identification result is output.

[0029] Specifically, this embodiment obtains historical operation data to establish a data set that includes the influence of environmental factors, uses a particle swarm optimization algorithm to initialize the parameter search space, and combines the support vector regression algorithm to establish a nonlinear mapping relationship between output power and environmental factors; through an iterative optimization process, the particle swarm search strategy is continuously adjusted, and the random forest algorithm is used to evaluate the importance of parameters and screen key parameters, and finally the optimized parameter values ​​are obtained. These parameters are substituted into the photovoltaic power generation prediction model, the output power is calculated and compared with the measured value, and the final parameter identification result is output within the allowable error range.

[0030] Furthermore, the establishment of a data set that includes the impact of environmental factors includes:

[0031] Obtain historical operating data of the photovoltaic power generation system, and use data acquisition equipment to collect and store voltage, current, temperature and irradiance parameters in real time to form a data set;

[0032] Preprocess the collected historical operating parameter data, including cleaning and normalization, removing outliers, and converting the data into a format suitable for modeling;

[0033] Based on the pre-processed historical operating parameter data, key characteristic parameters reflecting the operating status and environmental factors of the photovoltaic power generation system are extracted;

[0034] According to the key characteristic parameters, a correlation analysis method is adopted to analyze the correlation between the characteristic parameters, and a characteristic parameter subset, that is, the data set containing the influence of environmental factors, is screened.

[0035] Specifically, the historical operation data of the photovoltaic power generation system is obtained, and the parameters such as voltage, current, temperature and irradiance are collected and stored in real time by using data acquisition equipment to form a data set. According to the collected historical operation parameter data, the data is cleaned, normalized and processed by data preprocessing technology to remove outliers and convert the data into a format suitable for modeling. For the preprocessed historical operation parameter data, the feature engineering technology is used to extract the key characteristic parameters reflecting the operation status and environmental factors of the photovoltaic power generation system. According to the extracted characteristic parameters, the correlation analysis method is used to analyze the correlation between the characteristic parameters, and the characteristic parameter subset closely related to the photovoltaic power generation is screened out. For the screened characteristic parameter subset, machine learning algorithms such as support vector machine and random forest are used to establish a photovoltaic power generation prediction model including the influence of environmental factors. Using the established prediction model, the real-time collected photovoltaic power generation system operation parameters and environmental parameters are input to predict the power generation in the future, providing a basis for the optimization control of the power generation system. According to the predicted power generation, combined with the actual operation status of the photovoltaic power generation system, the intelligent optimization algorithm is used to dynamically optimize and adjust the operation parameters of the power generation system, improve the power generation efficiency, and ensure the stable operation of the system.

[0036] Furthermore, the particle swarm optimization algorithm is used to initialize the parameter search space, and the hyper parameters are set including:

[0037] Initialize the particle swarm according to the preset particle swarm size, where each particle represents a set of algorithm parameter combinations to be optimized;

[0038] For each particle, the parameters carried by the particle are input into the system model, and the system output power under the parameters is obtained by simulation and calculation;

[0039] Obtaining measured power data under the same parameters, and calculating the mean square error between the system output power and the measured power as the fitness value of the particle;

[0040] Determine whether the current number of iterations has reached the preset maximum number of iterations. If not, update the particle speed and position according to the learning factor and enter the next round of iterative optimization;

[0041] If the maximum number of iterations has been reached, a particle with the best fitness value is selected from the particle swarm, and the parameters carried by the particle with the best fitness value are used as the optimal parameter combination;

[0042] The optimal parameter combination is input into the system model, and the optimized system output power is obtained through simulation calculation. By comparing the mean square error between the system output power before and after optimization and the measured power, the effect of the optimization algorithm is evaluated to determine whether the hyperparameters need to be further adjusted and re-optimized.

[0043] Specifically, in the parameter optimization of photovoltaic power generation system, each particle represents a set of algorithm parameter combinations to be optimized, such as inverter efficiency, maximum power point tracking algorithm parameters, etc. During initialization, in this embodiment, the particle swarm size is set to 50, and each particle is randomly distributed in the parameter space. For each particle, the parameters it carries are input into the system model for simulation. For example, the parameter combination carried by a particle is: inverter efficiency 95%, maximum power point tracking algorithm voltage step size 0.5V. The system output power under this parameter is obtained by simulation calculation to be 10kW. At the same time, the measured power data of the actual photovoltaic system under the same conditions is obtained, assuming it is 9.8kW. The mean square error between the simulated output power and the measured power is calculated, and 0.04 is obtained as the fitness value of the particle. During the iterative optimization process, the maximum number of iterations is set to 100. In each iteration, the speed and position of the particle are updated according to the learning factor. The learning factor usually includes an individual learning factor and a social learning factor, which control the tendency of the particle to move to the individual optimal position and the global optimal position respectively.

[0044] If the mean square error after optimization is significantly reduced, it means that the optimization effect is good. If the effect is not ideal, you can consider adjusting the hyperparameters, such as increasing the particle swarm size, increasing the number of iterations, or adjusting the learning factor, and then re-optimizing. The advantage of this optimization method is that it can quickly find an approximate optimal solution in a complex parameter space, and is suitable for nonlinear, multivariable optimization problems such as photovoltaic power generation systems. Through continuous iteration and optimization, the power generation efficiency and stability of the system can be significantly improved, thereby realizing the intelligent control and management of photovoltaic power generation systems.

[0045] Furthermore, the nonlinear mapping relationship between the output power of the photovoltaic system and the environmental factors is established, including:

[0046] Obtain historical data sets containing PV system output power, ambient temperature, and irradiance data, preprocess the data, remove outliers and missing values, and normalize the data;

[0047] Based on the preprocessed data set, a support vector regression model is used to build a nonlinear regression model with ambient temperature and irradiance as input features and photovoltaic system output power as output target, and the hyperparameters of the model are optimized by cross-validation method.

[0048] The trained support vector regression model is applied to the new ambient temperature and irradiance data to predict the output power of the photovoltaic system under the new environmental conditions.

[0049] Specifically, a historical data set containing data such as photovoltaic system output power, ambient temperature and irradiance is obtained, and the data is preprocessed to remove outliers and missing values, and the data is normalized. According to the preprocessed data set, a support vector regression algorithm is used to construct a nonlinear regression model with ambient temperature and irradiance as input features and photovoltaic system output power as output target. In the training process of the support vector regression model, the hyperparameters of the model are optimized by the cross-validation method, including the kernel function type, penalty coefficient and tolerance error, so as to improve the generalization performance of the model. The trained support vector regression model is applied to new ambient temperature and irradiance data to predict the output power of the photovoltaic system under the environmental conditions, and the predicted results are compared with the actual output power to evaluate the prediction accuracy of the model. If the prediction accuracy of the model does not reach the preset threshold, the hyperparameters of the support vector regression model are adjusted and the model is retrained; otherwise, the trained model is saved for subsequent photovoltaic system output power prediction. The ambient temperature and irradiance prediction data for the future period of time in the area where the photovoltaic system is located are obtained, and they are input into the trained support vector regression model to obtain the output power prediction value of the photovoltaic system in the time period. According to the predicted value of the photovoltaic system output power, the operation strategy of the photovoltaic system is optimized, including battery charging and discharging control, load scheduling, etc., to improve the energy utilization efficiency and economic benefits of the photovoltaic system.

[0050] Furthermore, the parameter estimation value obtained by the particle swarm optimization algorithm is input into the support vector regression model to calculate the system output power prediction value. If the error between the prediction value and the measured value exceeds a preset threshold, the particle swarm search strategy is readjusted, including:

[0051] Obtain a pre-established support vector regression model and a preset error threshold;

[0052] For the system to be predicted, the particle swarm optimization algorithm is used to estimate the parameters and obtain the parameter estimation value;

[0053] Inputting the parameter estimation value into the support vector regression model to calculate the predicted value of the system output power;

[0054] Obtain the measured value of the system output power and calculate the error between the predicted value and the measured value;

[0055] Determine whether the error exceeds the preset threshold. If so, adjust the search strategy of the particle swarm algorithm.

[0056] According to the adjusted search strategy, the particle swarm optimization algorithm is re-executed to obtain new parameter estimates;

[0057] The new parameter estimation value is input into the support vector regression model, and the updated power prediction value is calculated until the prediction value meets the error threshold requirement.

[0058] Specifically, in this embodiment, a particle is defined as a set of parameters of the support vector regression model, such as kernel function parameters, penalty factors, etc. Each particle represents a set of possible parameter values, and the best parameter combination is found through iterative optimization. Assuming that the initial particle swarm contains 50 particles, each particle contains 5 parameters, and the optimal parameter estimation value is obtained after 100 iterations. After the estimated parameter values ​​are input into the support vector regression model, the output power under specific environmental conditions can be predicted. For example, using the optimized parameters, the model predicts that the output power under specific environmental conditions is 30°C and the irradiance is 900W / m 2 When , the system output power is 550kW. To evaluate the prediction accuracy, it is necessary to compare the predicted value with the measured value. Assuming that the actual measured output power is 545kW, the prediction error is 5kW. The setting of the preset error threshold depends on the specific application requirements. In this embodiment, the threshold is set to 10kW or 2% of the output power, whichever is smaller. If the prediction error exceeds the threshold, it is necessary to adjust the search strategy of the particle swarm algorithm. The adjustment method includes increasing the number of particles, modifying the inertia weight or learning factor, etc. For example, increase the number of particles from 50 to 100, or adjust the global learning factor from 2.0 to 2.2 to enhance the global search capability of the algorithm. By adjusting the search strategy, re-execute the particle swarm optimization algorithm. Assume that after adjustment, the new parameter estimation makes the predicted output power 547kW, and the error with the measured value of 545kW is reduced to 2kW, which meets the preset error requirement. This iterative optimization process not only improves the prediction accuracy, but also enhances the model's adaptability to different environmental conditions. This method of combining support vector regression and particle swarm optimization has many advantages. First, it can handle nonlinear relationships in photovoltaic systems and improve prediction accuracy. Second, by dynamically adjusting parameters, the model can adapt to changes in system performance over time, such as efficiency fluctuations caused by equipment aging or seasonal changes. Finally, high-precision power prediction helps optimize the operation strategy of photovoltaic systems, such as battery charge and discharge management and load scheduling, thereby improving energy utilization efficiency and economic benefits.

[0059] Furthermore, the importance of each parameter to the system output power is evaluated by the random forest algorithm, including:

[0060] Construct training data sets and test data sets through different input parameters and corresponding output power data;

[0061] The training data set is trained using the random forest algorithm to obtain a random forest model, and the model is evaluated using the test data set to calculate the prediction accuracy of the model;

[0062] The importance of different input parameters is evaluated using the random forest model to obtain the importance value of each parameter;

[0063] According to a preset importance threshold, parameters whose importance values ​​are greater than or equal to the importance threshold are determined as key parameters;

[0064] Determine whether there is a parameter whose importance value is lower than the importance threshold among the key parameters, and if so, remove the parameter from the key parameter list;

[0065] According to the key parameters obtained by screening, an optimization model is constructed, and the key parameters are optimized and solved by using a genetic algorithm to obtain the optimal values ​​of each key parameter;

[0066] The optimal values ​​of the key parameters are input into the photovoltaic power generation prediction model to obtain the output power of the system, and the output power is compared with the output power before optimization to evaluate the optimization effect.

[0067] Furthermore, the genetic algorithm is used to optimize the key parameters and obtain the optimal values ​​of each key parameter, including:

[0068] Determine the optimization objective function, incorporate the screened key parameters into the objective function as optimization variables, set the value range and constraint conditions of the optimization variables, and build a complete optimization model;

[0069] Convert the constructed optimization model into a form that can be solved by the genetic algorithm, and determine the various parameter settings of the genetic algorithm;

[0070] The initial population is randomly generated, each individual corresponds to a set of optimized variable values, and the individual fitness is calculated according to the optimization objective function;

[0071] Perform selection, crossover, and mutation genetic operations on the current population to generate new offspring populations and continuously iterate and optimize;

[0072] When the iteration termination condition is met, the individual with the highest fitness is selected from the final population, and the value of the optimization variable corresponding to the individual with the highest fitness is the optimal solution;

[0073] The optimal solution obtained by the genetic algorithm is used as the optimal value of the key parameters.

[0074] Specifically, the selection operation can adopt the roulette method, and the individual with higher output power has a greater probability of being selected. The crossover operation can use arithmetic crossover, such as taking the average of the two parent inclinations as the offspring inclination. The mutation operation can randomly adjust the parameters in a small amplitude near the current value, such as increasing or decreasing the inclination by 1-2 degrees. The iteration termination condition can be reaching the maximum number of iterations (such as 100 generations) or there is no significant improvement in the optimal solution for multiple consecutive generations. The optimal solution finally obtained may be an inclination of 32° and an azimuth of 175°, at which time the system output power reaches the maximum value. The advantage of this optimization method is that it can quickly find an approximate optimal solution in a complex parameter space, and is particularly suitable for nonlinear and multi-peak optimization problems. The optimal parameters obtained by the genetic algorithm can directly guide the installation of solar panels and improve the overall efficiency of the system. At the same time, this method can also be applied to other fields, such as the layout optimization of wind turbines, parameter tuning of chemical production processes, etc., reflecting its wide applicability and practical value.

[0075] Furthermore, the final parameter identification results are output including:

[0076] According to the optimized parameter values, the photovoltaic power generation prediction model is used to calculate the output power estimation value of the photovoltaic system;

[0077] Obtain the measured output power data of the photovoltaic system under the same conditions as a reference value;

[0078] Determine whether the error between the estimated output power and the measured output power is within a preset allowable range. If so, proceed to the next step; otherwise, return to the first step to re-optimize the parameters.

[0079] According to the comparison result of the estimated output power and the measured output power, it is determined whether the current optimized parameter value is reliable. If it is reliable, it is output as the final parameter identification result.

[0080] This embodiment also provides a photovoltaic power generation system parameter identification system based on a swarm intelligence optimization algorithm. Figure 2 ,include:

[0081] A data acquisition module, used to acquire historical operation data of the photovoltaic power generation system, establish a data set including the influence of environmental factors based on the historical operation data, and construct a photovoltaic power generation prediction model through the data set including the influence of environmental factors;

[0082] The parameter initialization module is used to initialize the parameter search space using the particle swarm optimization algorithm, set hyperparameters such as particle swarm size, number of iterations, and learning factor, and define the mean square error between the system output power and the measured power as the fitness function;

[0083] The model building module establishes a nonlinear mapping relationship between the output power of the photovoltaic system and environmental factors through a support vector regression model, inputs the parameter estimation value obtained by the particle swarm optimization algorithm into the support vector regression model, and calculates the predicted value of the system output power. If the error between the predicted value and the measured value exceeds a preset threshold, the particle swarm search strategy is readjusted;

[0084] A parameter evaluation module is used to evaluate the importance of each parameter to the system output power through a random forest algorithm, and screen out key parameters. If the importance of a parameter among the key parameters is lower than a preset value, the key parameter is removed from the optimization process;

[0085] The result output module is used to substitute the optimized parameter values ​​into the photovoltaic power generation prediction model, calculate the system output power, compare the output power with the measured value, and output the final parameter identification result if the error between the two is within the allowable range.

[0086] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A photovoltaic power generation system parameter identification method based on swarm intelligence optimization algorithm, characterized in that: include: Acquire historical operation data of the photovoltaic power generation system, establish a data set including the influence of environmental factors, and construct a photovoltaic power generation prediction model through the data set including the influence of environmental factors; Initializing the parameter search space by using a particle swarm optimization algorithm, setting hyperparameters, inputting the hyperparameters into the photovoltaic power generation prediction model, and simulating and calculating the output power of the photovoltaic system; Through the support vector regression model, a nonlinear mapping relationship between the output power of the photovoltaic system and environmental factors is established. The parameter estimation value obtained by the particle swarm optimization algorithm is input into the support vector regression model to calculate the predicted value of the system output power. If the error between the predicted value and the measured value exceeds a preset threshold, the particle swarm search strategy is readjusted. The importance of each parameter to the system output power is evaluated by a random forest algorithm, and key parameters are screened out. If the importance of a parameter among the key parameters is lower than a preset value, the key parameter is removed from the optimization process; The optimized parameter values ​​are substituted into the photovoltaic power generation prediction model, the system output power is calculated, and the output power is compared with the measured value. If the error between the two is within the allowable range, the final parameter identification result is output.

2. The photovoltaic power generation system parameter identification method based on swarm intelligence optimization algorithm according to claim 1 is characterized in that: The establishment of the data set including the impact of environmental factors includes: Obtain historical operating data of the photovoltaic power generation system, and use data acquisition equipment to collect and store voltage, current, temperature and irradiance parameters in real time to form a data set; Preprocess the collected historical operating parameter data, including cleaning and normalization, removing outliers, and converting the data into a format suitable for modeling; Based on the pre-processed historical operating parameter data, key characteristic parameters reflecting the operating status and environmental factors of the photovoltaic power generation system are extracted; According to the key characteristic parameters, a correlation analysis method is adopted to analyze the correlation between the characteristic parameters, and a characteristic parameter subset, that is, the data set containing the influence of environmental factors, is screened.

3. The photovoltaic power generation system parameter identification method based on swarm intelligence optimization algorithm according to claim 1 is characterized in that: The particle swarm optimization algorithm is used to initialize the parameter search space, and the hyper parameters are set including: Initialize the particle swarm according to the preset particle swarm size, where each particle represents a set of algorithm parameter combinations to be optimized; For each particle, the parameters carried by the particle are input into the system model, and the system output power under the parameters is obtained by simulation and calculation; Obtaining measured power data under the same parameters, and calculating the mean square error between the system output power and the measured power as the fitness value of the particle; Determine whether the current number of iterations has reached the preset maximum number of iterations. If not, update the particle speed and position according to the learning factor and enter the next round of iterative optimization; If the maximum number of iterations has been reached, a particle with the best fitness value is selected from the particle swarm, and the parameters carried by the particle with the best fitness value are used as the optimal parameter combination.

4. The photovoltaic power generation system parameter identification method based on swarm intelligence optimization algorithm according to claim 3 is characterized in that: Inputting the optimal parameter combination into the photovoltaic power generation prediction model includes: By comparing the mean square error between the system output power before and after optimization and the measured power, the effect of the optimization algorithm can be evaluated to determine whether the hyperparameters need to be adjusted and re-optimized.

5. The photovoltaic power generation system parameter identification method based on swarm intelligence optimization algorithm according to claim 1 is characterized in that: Establishing the nonlinear mapping relationship between the output power of the photovoltaic system and environmental factors includes: Obtain historical data sets containing PV system output power, ambient temperature, and irradiance data, preprocess the data, remove outliers and missing values, and normalize the data; Based on the preprocessed data set, a support vector regression model is used to build a nonlinear regression model with ambient temperature and irradiance as input features and photovoltaic system output power as output target, and the hyperparameters of the model are optimized by cross-validation method. The trained support vector regression model is applied to the new ambient temperature and irradiance data to predict the output power of the photovoltaic system under the new environmental conditions.

6. The photovoltaic power generation system parameter identification method based on swarm intelligence optimization algorithm according to claim 5 is characterized in that: The parameter estimation value obtained by the particle swarm optimization algorithm is input into the support vector regression model to calculate the system output power prediction value. If the error between the prediction value and the measured value exceeds a preset threshold, the particle swarm search strategy is readjusted, including: Obtain a pre-established support vector regression model and a preset error threshold; For the system to be predicted, the particle swarm optimization algorithm is used to estimate the parameters and obtain the parameter estimation value; Inputting the parameter estimation value into the support vector regression model to calculate the predicted value of the system output power; Obtain the measured value of the system output power and calculate the error between the predicted value and the measured value; Determine whether the error exceeds the preset threshold. If so, adjust the search strategy of the particle swarm algorithm. According to the adjusted search strategy, the particle swarm optimization algorithm is re-executed to obtain new parameter estimates; The new parameter estimation value is input into the support vector regression model, and the updated power prediction value is calculated until the prediction value meets the error threshold requirement.

7. The photovoltaic power generation system parameter identification method based on swarm intelligence optimization algorithm according to claim 1 is characterized in that: The importance of each parameter to the system output power evaluated by the random forest algorithm includes: Construct training data sets and test data sets through different input parameters and corresponding output power data; The training data set is trained using the random forest algorithm to obtain a random forest model, and the model is evaluated using the test data set to calculate the prediction accuracy of the model; The importance of different input parameters is evaluated using the random forest model to obtain the importance value of each parameter; According to a preset importance threshold, parameters whose importance values ​​are greater than or equal to the importance threshold are determined as key parameters; Determine whether there is a parameter whose importance value is lower than the importance threshold among the key parameters, and if so, remove the parameter from the key parameter list; According to the key parameters obtained by screening, an optimization model is constructed, and the key parameters are optimized and solved by using a genetic algorithm to obtain the optimal values ​​of each key parameter; The optimal values ​​of the key parameters are input into the photovoltaic power generation prediction model to obtain the output power of the system, and the output power is compared with the output power before optimization to evaluate the optimization effect.

8. The photovoltaic power generation system parameter identification method based on swarm intelligence optimization algorithm according to claim 7 is characterized in that: The key parameters are optimized and solved by using genetic algorithm, and the optimal values ​​of each key parameter are obtained, including: Determine the optimization objective function, incorporate the screened key parameters into the objective function as optimization variables, set the value range and constraint conditions of the optimization variables, and build a complete optimization model; Convert the constructed optimization model into a form that can be solved by genetic algorithm, and determine the various parameter settings of genetic algorithm; The initial population is randomly generated, each individual corresponds to a set of optimized variable values, and the individual fitness is calculated according to the optimization objective function; Perform selection, crossover, and mutation genetic operations on the current population to generate new offspring populations and continuously iterate and optimize; When the iteration termination condition is met, the individual with the highest fitness is selected from the final population, and the value of the optimization variable corresponding to the individual with the highest fitness is the optimal solution; The optimal solution obtained by the genetic algorithm is used as the optimal value of the key parameters.

9. The photovoltaic power generation system parameter identification method based on swarm intelligence optimization algorithm according to claim 1 is characterized in that: The final parameter identification results are as follows: According to the optimized parameter values, the photovoltaic power generation prediction model is used to calculate the output power estimation value of the photovoltaic system; Obtain the measured output power data of the photovoltaic system under the same conditions as a reference value; Determine whether the error between the estimated output power and the measured output power is within a preset allowable range. If so, proceed to the next step; otherwise, return to the first step to re-optimize the parameters. According to the comparison result of the estimated output power and the measured output power, it is determined whether the current optimized parameter value is reliable. If it is reliable, it is output as the final parameter identification result.

10. A photovoltaic power generation system parameter identification system based on swarm intelligence optimization algorithm, characterized in that: include: A data acquisition module, used to acquire historical operation data of the photovoltaic power generation system, establish a data set including the influence of environmental factors based on the historical operation data, and construct a photovoltaic power generation prediction model through the data set including the influence of environmental factors; The parameter initialization module is used to initialize the parameter search space using the particle swarm optimization algorithm, set hyperparameters such as particle swarm size, number of iterations, and learning factor, and define the mean square error between the system output power and the measured power as the fitness function; The model building module establishes a nonlinear mapping relationship between the output power of the photovoltaic system and environmental factors through a support vector regression model, inputs the parameter estimation value obtained by the particle swarm optimization algorithm into the support vector regression model, and calculates the predicted value of the system output power. If the error between the predicted value and the measured value exceeds a preset threshold, the particle swarm search strategy is readjusted; A parameter evaluation module is used to evaluate the importance of each parameter to the system output power through a random forest algorithm, and screen out key parameters. If the importance of a parameter among the key parameters is lower than a preset value, the key parameter is removed from the optimization process; The result output module is used to substitute the optimized parameter values ​​into the photovoltaic power generation prediction model, calculate the system output power, compare the output power with the measured value, and output the final parameter identification result if the error between the two is within the allowable range.

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