Photovoltaic cell high-precision modeling method fusing leader mechanism P system

Abstract

The invention discloses a photovoltaic cell high-precision modeling method fused with a leader mechanism P system. The method comprises the following specific steps: 1) collecting actual working current and working voltage of photovoltaic cell operation; 2) analyzing a photovoltaic cell principle, establishing a photovoltaic cell model, and taking a root-mean-square error of estimated output and actual output of the model as a target function; 3) combining the leader mechanism of the chimpanzee optimization algorithm with a membrane structure, a rewriting rule, a selection rule, a communication rule and an object set of a standard P system (membrane calculation model) to form a P system optimization algorithm (LPOA) fused with the leader mechanism; 4) minimizing the objective function through an LPOA optimization algorithm, identifying unknown parameters of the photovoltaic cell model, obtaining optimal parameters and forming a mathematical model; 5) performing current and power data fitting according to the obtained optimal parameters and models; and 6) applying other algorithms to the photovoltaic cell model to obtain an optimal parameter combination and an optimal error value, and comparing the optimal parameter combination and the optimal error value with the LPOA.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy, and particularly relates to a high-precision modeling method for photovoltaic cells integrating a leadership mechanism P system. Background Art

[0002] In recent years, with the development of the green economy, the photovoltaic power generation industry has witnessed huge growth. Photovoltaic cells are an important part of a photovoltaic power generation system, but the output characteristics of the photovoltaic power generation system are greatly affected by the output characteristics of the photovoltaic cells. By identifying the parameters that reflect the internal characteristics of the photovoltaic cells, not only can the I-V equation be determined, so as to predict the output current and power of the photovoltaic cells using the obtained I-V equation, but also by analyzing the changes in these parameters, the reasons for the occurrence of faults in the photovoltaic cells can be further studied and improved. Therefore, it is very meaningful to identify the internal parameters of the photovoltaic cells.

[0003] Currently, the methods for identifying the model parameters of photovoltaic cells are generally divided into three categories: analytical method, numerical calculation method, and optimization algorithm estimation method. The analytical method can achieve fast solution, but the problem of poor solution adaptability has not been well solved. In particular, the approximate processing in this method will reduce the accuracy of the solution. The numerical calculation method is overly dependent on the selection of the initial value. If the error is too large, the initial value has to be reselected each time, and then its convergence is observed. The error in the solution process will also increase with the increase of the identified parameters. The optimization algorithm has the advantages of few constraint conditions and strong non-linear identification ability, and has been widely used in the parameter identification of battery models. However, most of the classical intelligent algorithms have problems such as slow convergence speed and being trapped in local minimum points, resulting in low overall identification accuracy. The P system (membrane computing model) is a new type of heuristic intelligent algorithm framework. By using its membrane structure with parallel computing ability and designing rewrite rules and communication rules, the search efficiency and search accuracy can be improved. Summary of the Invention

[0004] Aiming at the deficiencies of the existing algorithms, a high-precision modeling method for photovoltaic cells integrating a leadership mechanism P system is proposed.

[0005] The high-precision modeling method for photovoltaic cells integrating a leadership mechanism P system according to the present invention includes the following steps:

[0006] 1) Collect the actual working current and working voltage of the photovoltaic cell during operation;

[0007] analyze the principle of the photovoltaic cell, establish a photovoltaic cell model, and use the root mean square error between the estimated output and the actual output of the model as the objective function;

[0008] 3) Combine the leadership mechanism of the chimp optimization algorithm with the membrane structure, rewriting rules, selection rules, communication rules, and object set of the standard P system (membrane computing model) to form a P system optimization algorithm (LPOA) that integrates the leadership mechanism;

[0009] 4) Minimize the objective function through the LPOA optimization algorithm, optimize the unknown parameter combinations of the photovoltaic cell model, obtain the optimal parameters, and form a mathematical model;

[0010] 5) Perform data fitting of current and power according to the obtained optimal parameters and model.

[0011] The membrane structure adopted by the high-precision modeling method of the photovoltaic cell with a P system integrating the leadership mechanism is the most basic nested membrane structure in the P system, as Figure 1 shown; by integrating the nested membrane structure, the individual update operator based on the leadership mechanism is used as the rewriting rule inside the membrane, and it is set to search and update from the innermost membrane to the outermost membrane in sequence. The specific flowchart of LPOA is as Figure 2 shown;

[0012] The model structure diagram of the photovoltaic cell adopted by the high-precision modeling method of the photovoltaic cell with a P system integrating the leadership mechanism is as Figure 3 shown, and its I-V equation is:

[0013]

[0014] In the formula, I is the model output current, I ph is the photocurrent, I sd is the reverse saturation current of the diode, n is the diode quality factor, R s is the series resistance of the battery, R sh is the parallel resistance of the battery, T is the absolute temperature of the battery, k is the Boltzmann constant, and q is the charge of an electron; the formula of the objective function (root mean square error function RMSE(x)) is as follows:

[0015]

[0016] In the formula, M is the number of sample data; x = (I ph , I sd , R s , R sh , n) are the parameters to be identified: photocurrent I ph , reverse saturation current of the diode I sd , diode quality factor n, series resistance of the battery R s , parallel resistance of the battery R sh .

[0017] The described high-precision modeling method of a photovoltaic cell integrating a leadership mechanism P system. The leadership mechanism is to select 4 individuals with the top fitness values from the candidate solutions (in the initial stage): (xa ， x b ,x c ,x d ). The leadership group updates the other solutions within the candidate solutions through a memory mechanism (as shown in Equation (4)), and the leadership group updates through an adaptive step size weight (the required parameter calculation is shown in Equation (3)), and its update formula is shown in Equation (5);

[0018]

[0019] where r1 and r2 are random numbers between (0, 1), t is the current iteration number, mod represents the remainder operation (i.e., taking the remainder of m i (t) / 1), T is the maximum iteration number, w is the required step size weight, (f,m i ,a i ,c i ) are the parameters required for updating the leadership group (x a ,x b ,x c ,x d ), where i = (1, 2, 3, 4) corresponds to the serial number of the leadership group;

[0020]

[0021] where q1 and q2 are random numbers between (0, 1), x best is the individual solution with the best fitness value in the current candidate solution set, and s is a constant;

[0022]

[0023] (f,m i ,a i ,c i ) will change adaptively with the iteration, so that an appropriate leadership group can be found as much as possible in each iteration, improving the search direction of each layer of membrane, so that the overall algorithm can improve the convergence speed without losing the search accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 : Schematic diagram of the nested membrane structure;

[0025] Figure 2 : LPOA flow chart;

[0026] Figure 3 : Model structure diagram of the photovoltaic cell adopted by the present invention;

[0027] Figure 4 : Fitting characteristic curves of the LPOA optimization algorithm: (a) I-V characteristic curve, (b) P-V characteristic curve;

[0028] Figure 5 : Abstract drawing: Implementation process of the modeling method for a photovoltaic cell model based on LPOA;

[0029] Figure 6 : Comparison of optimal results. Specific implementation manners

[0030] The present invention will be further described in detail below with reference to the drawings and specific implementation manners: For the high-precision modeling method of a photovoltaic cell integrating a leadership mechanism P system, the implementation plan is as follows:

[0031] 1) Design an objective function (root mean square error function RMSE(x)) by adopting the I-V equation;

[0032] 2) Initialize the number of membrane layers m of LPOA n , the number of candidate solutions L in each membrane layer, the communication ratio P c , the maximum number of iterations T, the parameters (f, m i , a i , c i ) required to find the leadership group, calculate the fitness of the candidate solution set from the innermost membrane, and select the top four individuals in terms of fitness (x a , x b , x c , x d ) as the initial leadership population;

[0033] 3) Update the candidate solution set within the membrane according to formulas (3), (4), and (5);

[0034] 4) Select the top P c *L candidate solution sets and merge them with the candidate solutions of the next membrane layer, and repeat the operation in 3) until the outermost membrane;

[0035] 5) Repeat the operations in 3) and 4). When the termination condition is met, output the best parameter combination;

[0036] 6) Substitute into the model to obtain the fitted I-V characteristic curve and P-V characteristic curve (as shown in Figure 4 );

[0037] 7) Apply other algorithms to the photovoltaic cell model of the present invention to obtain the best parameter combination and the best error value, and compare them with LPOA. The results are as shown in Figure 6 .

Claims

1. A high-precision modeling method for photovoltaic cells integrating a leadership mechanism P system, characterized by including The following steps: 1) Collect the actual working current and working voltage of the photovoltaic cell during operation; 2) Analyze the principle of the photovoltaic cell, establish a photovoltaic cell model, and use the root mean square error between the estimated output and the actual output of the model as the objective function; specifically as follows: first establish the I-V equation of the photovoltaic cell based on the detected measured current and measured voltage, determine the identified parameters, solve the root mean square error function RMSE(x) using the measured data of the photovoltaic cell and use it as the optimization objective function; 3) Combine the leadership mechanism of the chimp optimization algorithm with the membrane structure, rewriting rules, selection rules, communication rules, and object set of the standard P system (membrane computing model) to form a P system optimization algorithm (LPOA) that integrates the leadership mechanism; 4) Minimize the objective function through the LPOA optimization algorithm to identify the unknown parameters of the photovoltaic cell model, obtain the optimal parameters, and form a mathematical model; 5) Perform data fitting of current and power according to the obtained optimal parameters and model.

2. A high-precision modeling method for photovoltaic cells integrating a leadership mechanism P system according to claim 1, characterized in that The expression of the I-V equation of the battery described in step 2) is as follows: Wherein, I is the output current of the model, I ph is the photocurrent, I sd is the reverse saturation current of the diode, n is the diode quality factor, R s is the series resistance of the battery, R sh is the parallel resistance of the battery, T is the absolute temperature of the battery, k is the Boltzmann constant, q is the electric charge of an electron; the formula for the root mean square error function RMSE(x) is as follows: Where M is the number of sample data; x=(I ph ,I sd ,R s ,R sh ,n) is the parameter that needs to be identified: photocurrent I ph , diode reverse saturation current I sd , diode quality factor n, battery series resistance R s , battery parallel resistance R sh .

3. A high-precision modeling method for a photovoltaic cell integrating a leadership mechanism P system according to claim 1, characterized in that The membrane structure adopted in step 3) is the most basic nested membrane structure in membrane computing. In each layer of the membrane, a leadership mechanism with adaptive step-size weights and a memory mechanism is adopted (initially, four individuals are selected from the candidate solutions as the leadership group, and the subsequent solution set adjusts the search direction of the algorithm according to the leadership group; when the algorithm starts to search, the number of membrane layers m of LPOA is initialized n , the number of candidate solutions L in each layer of the membrane, the communication ratio P c , the maximum number of iterations T, the parameters required to find the leadership group (f, m i , a i , c i ) as shown in formula (3), where i = (1, 2, 3, 4) corresponds to the serial number of the leadership group; where r1 and r2 are random numbers between (0, 1), t is the current iteration number, and mod represents the remainder operation (i.e., taking the remainder of m i (t) / 1); the adaptive step size weight is shown in formula (4): The algorithm starts from the innermost membrane and selects the top 4 solutions (x a , x b , x c , x d ) with better fitness of the candidate solutions in the innermost membrane as the initial leading group. The subsequent update of the leading group is shown in formula (5): Update the positions of other candidate solutions in the search space in combination with the memory mechanism (as shown in formula (6)), and update (f, m i , a i , c i ). Select the relatively better P c *L candidate solutions according to the communication ratio to enter the lower layer membrane for search; if the termination condition is met, output the optimal solution of the surface membrane; where q1 and q2 are random numbers between (0, 1), and x best is the individual solution with the best fitness in the current candidate solution set, and s is a constant.

4. A high-precision modeling method for a photovoltaic cell integrating a leadership mechanism P system according to claim 1, characterized in that The measured current and measured voltage described in step 1) are collected by a measurement data acquisition unit for obtaining the output current and output voltage of the photovoltaic cell system under a certain temperature and light intensity; and the output current and output voltage optimize the objective function through the LPOA optimization algorithm with a preset delay dynamic step size mechanism by the objective function optimization unit; and the optimal parameters analyze and extract the optimal parameters of the mathematical model of the photovoltaic cell system from the optimization result of the objective function by the parameter output unit.

5. A high-precision modeling method for a photovoltaic cell integrating a leadership mechanism P system according to claim 1, characterized in that The selection and communication rules described in step 3) act together. The algorithm starts searching from the innermost membrane, selects the top P c *L individual solutions enter the next layer of the membrane to search and update together with the candidate solutions in the next layer of the membrane (until the search terminates at the outermost membrane). At the outermost membrane, it is judged whether the termination condition is reached. If not, it returns to the innermost membrane to continue searching and updating. When the termination condition is satisfied, the optimal solution of the outermost membrane is output as the best parameter of the model.

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