Photovoltaic array reconstruction method and system based on double-target optimization and computer device

Abstract

The application discloses a photovoltaic array reconstruction method and system based on double-target optimization and a computer device, wherein the method comprises the following steps: step 110, a photovoltaic array reconstruction model is established, the photovoltaic array reconstruction model comprises an optimization function with the target of maximizing output power, an optimization function with the target of minimizing switch switching times and a switch constraint condition; step 120, the photovoltaic array reconstruction model is iteratively optimized by using an SPEA2 algorithm to obtain a Pareto frontier; and step 130, a compromise solution is selected from the Pareto frontier by using a TOPSIS decision method, and then an optimal arrangement combination scheme of the photovoltaic array is obtained. In the process of performing photovoltaic array reconstruction by combining the SPEA2 algorithm and the TOPSIS decision method, the problems of maximizing output power and minimizing switch switching times are considered, switch loss can be reduced, and economic cost can be reduced.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic array reconfiguration technology, specifically to a photovoltaic array reconfiguration method, system, and computer equipment based on dual-objective optimization. Background Technology

[0002] Photovoltaic arrays are susceptible to uneven illumination due to shading from surrounding buildings, power lines, and other external factors. This reduces the total output power of the array and can even lead to hot spot effects or alter the overall electrical performance of the array. To address these issues, engineers proposed a photovoltaic array reconfiguration method, which involves altering the electrical connections between multiple photovoltaic modules within the array.

[0003] Currently, some scholars have proposed a photovoltaic array reconfiguration method based on the TCT structure. This method involves iteratively sorting photovoltaic arrays experiencing localized shading according to biological genetic laws, using circuit principles, and evaluating the output power value after each iteration until a stable state is reached. The array is then adjusted according to the current arrangement. However, this method also has certain drawbacks: while local compensation can effectively improve the total output power of the photovoltaic array, the implementation complexity of the introduced hardware circuitry is relatively high, and the array adjustment process requires significant time investment.

[0004] Another scholar proposed a photovoltaic array reconfiguration method based on a tiered inverter topology. This method involves first classifying different inverters, then separating shaded and unshaded photovoltaic modules and assigning them to inverters with different rated power ratings. This addresses the impact of parallel mismatch and localized shading. However, this method also has drawbacks: the large number of inverters used and the lack of consideration for switching frequency can lead to increased line losses, switch aging, and reduced economic efficiency. Summary of the Invention

[0005] This invention provides a photovoltaic array reconfiguration method, system, and computer device based on dual-objective optimization to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0006] This invention provides a photovoltaic array reconfiguration method based on dual-objective optimization, the method comprising:

[0007] Step 110: Establish a photovoltaic array reconfiguration model, which includes an optimization function aimed at maximizing output power, an optimization function aimed at minimizing the number of switching operations, and switching constraints.

[0008] Step 120: Iteratively optimize the photovoltaic array reconstruction model using the SPEA2 algorithm to obtain the Pareto front;

[0009] Step 130: Use the TOPSIS decision method to select a compromise solution from the Pareto front, and then obtain the optimal arrangement and combination scheme of the photovoltaic array.

[0010] Further, in step 110, the optimization function aiming to maximize output power and the optimization function aiming to minimize the number of switching operations are denoted as bi-objective optimization functions, and their corresponding expressions are:

[0011]

[0012] in:

[0013]

[0014] The switching constraint condition is:

[0015]

[0016] In the formula, f1 is the output power of the photovoltaic array, f2 is the number of switching operations corresponding to the photovoltaic array, and V our I is the total output voltage of the photovoltaic array. out Let n be the total output current of the photovoltaic array. row The number of rows of photovoltaic modules set in the photovoltaic array, n col The number of photovoltaic modules set for the photovoltaic array, where sign is the sign function. This refers to the current electrical switching state of a single photovoltaic module defined by the p-th row and q-th column of the photovoltaic array. The initial electrical switching state of a single photovoltaic module defined by the p-th row and q-th column of the photovoltaic array. and All are discrete values, I pq V is the output current of a single photovoltaic module defined by the p-th row and q-th column of the photovoltaic array. mp The total output voltage of all photovoltaic modules defined in row p of the photovoltaic array.

[0017] Furthermore, the implementation process of step 120 includes:

[0018] Step 121: Determine all photovoltaic modules currently shaded in the photovoltaic array, and at the same time determine the basic parameters required by the SPEA2 algorithm, including the maximum number of iterations, population size, and external population size;

[0019] Step 122: Set the current iteration number to k = 0, and randomly initialize multiple initial permutation and combination schemes of the photovoltaic array in the photovoltaic array reconstruction model to obtain the current population P. k At the same time, create an empty current external population.

[0020] Step 123: In the k-th iteration, the fitness allocation strategy specified by the SPEA2 algorithm is used, combined with the continuous variable discretization formula and the bi-objective optimization function, to determine the current population P. k and current external population The precise fitness values ​​of all individuals are used to generate a new current external population through environmental selection as defined by the SPEA2 algorithm.

[0021] Step 124: Determine if k has reached the maximum number of iterations; if so, then change the new current external population. The non-dominated individuals in the group constitute the Pareto front; otherwise, proceed to step 125.

[0022] Step 125: Based on the new current external population A new current population P is generated using mating selection and crossover mutation as defined by the SPEA2 algorithm. k+1 Assign k+1 to k, then return to step 123.

[0023] Further, in step 123, the continuous variable discretization formula is applied to an optimization function aimed at minimizing the number of switch switching operations, and the corresponding expression is:

[0024]

[0025] Among them, X dis The first matrix is ​​composed of the electrical switching states of all photovoltaic modules on the photovoltaic array, and all elements inside the first matrix are discrete values. The second matrix is ​​composed of the electrical switching states of all photovoltaic modules defined in the q-th column of the photovoltaic array, and all elements inside the second matrix are continuous values, which are obtained by the SPEA2 algorithm through iterative processing; sort refers to the sorting function.

[0026] Furthermore, the implementation process of step 130 includes:

[0027] Step 131: Based on the Pareto front containing multiple undetermined permutations and combinations, create a decision matrix;

[0028] Step 132: Based on the given weight vector, determine the weighted decision matrix associated with the decision matrix;

[0029] Step 133: Determine the positive ideal solution and the negative ideal solution based on the weighted decision matrix, and then calculate the weighted decision matrix using the maximization of relative proximity as specified by the TOPSIS decision method as the screening criterion. Finally, determine the optimal permutation and combination scheme from the decision matrix based on the calculation results.

[0030] Furthermore, the optimal permutation and combination scheme is the optimal electrical connection relationship between all photovoltaic modules defined in each column of the photovoltaic array.

[0031] In addition, embodiments of the present invention also provide a photovoltaic array reconfiguration system based on dual-objective optimization, the system comprising:

[0032] A module is established to build a photovoltaic array reconfiguration model, which includes an optimization function aimed at maximizing output power, an optimization function aimed at minimizing the number of switching operations, and switching constraints.

[0033] The optimization module is used to iteratively optimize the photovoltaic array reconstruction model using the SPEA2 algorithm to obtain the Pareto front.

[0034] The selection module is used to select a compromise solution from the Pareto front using the TOPSIS decision method, thereby obtaining the optimal arrangement and combination scheme of the photovoltaic array.

[0035] In addition, embodiments of the present invention also provide a computer device, including:

[0036] One or more processors;

[0037] Memory;

[0038] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the photovoltaic array reconfiguration method based on bi-objective optimization as described above.

[0039] The present invention has at least the following beneficial effects: when the photovoltaic array is shaded, with the goal of maximizing output power and minimizing the number of switching operations, the SPEA2 algorithm with strong search capability and fast convergence speed is used to iteratively optimize the photovoltaic array reconstruction model, and then the TOPSIS decision method is used to obtain the optimal electrical connection relationship between all photovoltaic modules in each column of the photovoltaic array. This can reduce switching losses and lower economic costs. Attached Figure Description

[0040] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0041] Figure 1 This is a flowchart illustrating a photovoltaic array reconfiguration method based on dual-objective optimization in an embodiment of the present invention.

[0042] Figure 2 This is a comparative schematic diagram of the photovoltaic array before and after performing optimal reconstruction in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the structural composition of a photovoltaic array reconfiguration system based on dual-objective optimization in an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0045] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0046] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a photovoltaic array reconfiguration method based on dual-objective optimization provided by an embodiment of the present invention. The method includes the following steps:

[0047] Step 110: Establish a photovoltaic array reconfiguration model, which includes an optimization function aimed at maximizing output power, an optimization function aimed at minimizing the number of switching operations, and switching constraints.

[0048] In this embodiment of the invention, the optimization function aiming to maximize output power and the optimization function aiming to minimize the number of switching operations are denoted as bi-objective optimization functions, and their corresponding expressions are:

[0049]

[0050] Among them, the parameter I is solved according to Kirchhoff's current law. out And the parameter V is solved according to Kirchhoff's voltage law.out The corresponding calculation formula is as follows:

[0051]

[0052] The switching constraint condition is:

[0053]

[0054] In the formula, f1 is the output power of the photovoltaic array, f2 is the number of switching operations corresponding to the photovoltaic array, and V our I is the total output voltage of the photovoltaic array. out Let n be the total output current of the photovoltaic array. row The number of rows of photovoltaic modules set in the photovoltaic array, n col The number of photovoltaic modules set for the photovoltaic array, where sign is the sign function. This refers to the current electrical switching state of a single photovoltaic module defined by the p-th row and q-th column of the photovoltaic array. The initial electrical switching state of a single photovoltaic module defined by the p-th row and q-th column of the photovoltaic array. and All are discrete values, I pq I is the output current of a single photovoltaic module defined by the p-th row and q-th column of the photovoltaic array. (p+1)q V is the output current of a single photovoltaic module defined by the (p+1)th row and the qth column of the photovoltaic array. mp The total output voltage of all photovoltaic modules defined in row p of the photovoltaic array.

[0055] In this embodiment of the invention, parameter I pq and parameter I (p+1)q The following formula can be used for calculation:

[0056]

[0057] Where I represents the output current of a single photovoltaic module currently being measured, G represents the irradiance level received by a single photovoltaic module currently being measured, and G0 represents the standard irradiance level, i.e., 1000 W / m². 2 I Lstc K represents the photocurrent generated by a photovoltaic module under standard test conditions. SC T is the short-circuit temperature coefficient. c T0 represents the ambient temperature at the location of the single photovoltaic module being measured, and T0 is the standard temperature, i.e., 25°C.

[0058] In this embodiment of the invention, parameter V mpDepending on the total output current of the photovoltaic modules in the p-th row of the photovoltaic array, after obtaining the output current of each photovoltaic module in the p-th row using the above formula for solving I, the output current with the smallest value is directly defined as the total output current of the photovoltaic modules in the p-th row; more specifically, the parameter V mp The voltage value can be determined using pre-defined rules, and the implementation process of these rules includes the following:

[0059] Step A1: Assuming the photovoltaic array has dimensions M×M, rearrange the M rows of photovoltaic modules in ascending order of total output current; starting from i=1, set the total output voltage of the rearranged first row of photovoltaic modules to M×V. m V m This refers to the maximum output voltage of a single photovoltaic module under rated conditions.

[0060] Step A2: Assign i+1 to i, and determine whether the total output current of the rearranged PV module in row i is equal to the total output current of the rearranged PV module in row (i-1). If yes, set the total output voltage of the rearranged PV module in row i to be equal to the total output voltage of the rearranged PV module in row (i-1), and then jump to step A4. If no, proceed to step A3.

[0061] Step A3: Set the total output voltage of the rearranged photovoltaic modules in the i-th row to (M-i+1)×V. m ;

[0062] Step A4: Determine if i equals M; if yes, end the loop; if no, return to step A2.

[0063] Step 120: Use the SPEA2 algorithm to iteratively optimize the photovoltaic array reconstruction model to obtain the Pareto front.

[0064] It should be noted that the SPEA2 algorithm is actually an improved second-generation SPEA algorithm, which combines a precise fitness assignment strategy, density estimation technique, and enhanced truncation method, thus accelerating the convergence speed of the algorithm. SPEA stands for Strength Pareto Evolutionary Algorithm.

[0065] In this embodiment of the invention, the implementation process of step 120 includes the following:

[0066] Step 121: Determine all photovoltaic modules in the photovoltaic array currently shaded, and simultaneously determine the basic parameters required for the SPEA2 algorithm, including the maximum number of iterations k. max Population size N and external population size

[0067] Step 122: Set the current iteration number to k = 0, and randomly initialize multiple initial permutation and combination schemes of the photovoltaic array in the photovoltaic array reconstruction model to obtain the current population P. k At the same time, create an empty current external population.

[0068] Step 123: In the k-th iteration, the fitness allocation strategy specified by the SPEA2 algorithm is used, combined with the continuous variable discretization formula and the bi-objective optimization function, to determine the current population P. k and current external population The precise fitness values ​​of all individuals are used to generate a new current external population through environmental selection as defined by the SPEA2 algorithm.

[0069] Step 124: Determine whether k has reached the maximum number of iterations k. max If so, then the new current external population will be... The non-dominated individuals in the group constitute the Pareto front; otherwise, proceed to step 125.

[0070] Step 125: Based on the new current external population A new current population P is generated using mating selection and crossover mutation as defined by the SPEA2 algorithm. k+1 Assign k+1 to k, then return to step 123.

[0071] In step 123 above, the continuous variable discretization formula is applied to the optimization function with the objective of minimizing the number of switch switching operations, and the corresponding expression is:

[0072]

[0073] Among them, X dis The first matrix is ​​composed of the electrical switching states of all photovoltaic modules on the photovoltaic array. This first matrix has the same dimension as the photovoltaic array, and all elements within the first matrix are discrete values. The parameters mentioned in the optimization function, which aims to minimize the number of switching operations, are then considered. and parameters All of them are elements inside the first matrix; The second matrix is ​​composed of the electrical switching states of all photovoltaic modules defined in the q-th column of the photovoltaic array, and all elements inside the second matrix are continuous values, which are obtained by the SPEA2 algorithm through iterative processing; sort refers to the sorting function, and the embodiment of the present invention adopts an ascending order.

[0074] Taking a 5×5 photovoltaic array as an example, the application of the above continuous variable discretization formula is explained as follows:

[0075]

[0076] Therefore, it can be seen that matrix x con It consists of five second matrices The above continuous variable discretization formula is composed of sequentially applying matrix x. con Each column in (i.e., each second matrix) After sorting the data in ascending order, extract the sequence number corresponding to each data point to form the first matrix X. dis .

[0077] In this embodiment of the invention, the specific implementation process of step 123 includes the following:

[0078] Step B1: Based on the bi-objective optimization function, create the corresponding fitness functions as follows: minF1 = -maxf1 and minF2 = minf2, where F1 is the first fitness value associated with parameter f1 and F2 is the second fitness value associated with parameter f2;

[0079] Step B2: Based on the fitness function, perform the following steps on the current population P. k and current external population Each individual i is assigned an intensity S(i), representing the number of individuals that can be controlled. The corresponding calculation formula is as follows: Where |·| represents the size. The Pareto dominance relationship is determined by comparing the first fitness values ​​of individual i with those of other individuals j, as well as the second fitness values ​​of individual i with those of other individuals j.

[0080] Step B3: Determine the original fitness value corresponding to individual i.

[0081] Step B4: Move individual i to the current population P k and current external population Sort all other individuals in the dataset in ascending order by their distances. Let the distance between individual i and its t-th neighbor be denoted as . in At this point, the exact fitness value corresponding to individual i is determined.

[0082] Step B5: Set the current population P k and current external population All individuals satisfying F(i)≤1 (i.e., all non-dominant individuals) are copied to the new current external population. When the new current external population The size equals When this occurs, it indicates that environmental selection is complete; when a new current external population... The size is smaller than If so, proceed to step B6; when a new current external population... The size is greater than If so, proceed to step B7;

[0083] Step B6: Set the current population P k and current external population After sorting all individuals satisfying F(i)>1 (i.e., all dominated individuals) in ascending order, select a few individuals at the top of the sorted list and copy them to the new current external population. So that its size is equal to This indicates that the environment selection is complete.

[0084] Step B7: Use the truncation algorithm to extract from the new current external population Remove individual i that meets the given conditions from the population. For all individuals k within the given condition, the following relationship should be satisfied: as well as In the formula Let i be the distance between individual i and its w-th neighboring individual; through this iterative process of judgment and removal, until the new current external population is reached. The size equals This indicates that the environment selection is complete.

[0085] In this embodiment of the invention, the specific implementation process of step 125 is as follows: in the new current external population After selecting individuals to be added to the mating pool using the binary tournament algorithm, crossover and mutation operations are performed on the individuals in the mating pool according to pre-set crossover and mutation probabilities to generate N new individuals, thus obtaining the new current population P. k+1 .

[0086] Step 130: Select a compromise solution from the Pareto front using the TOPSIS decision method to obtain the optimal arrangement of the photovoltaic array; where TOPSIS stands for Technique for Order Preference by Similarity to an Ideal Solution.

[0087] In this embodiment of the invention, the implementation process of step 130 includes the following:

[0088] Step 131: Based on the Pareto front containing multiple undetermined permutations and combinations (total number m), create the decision matrix as follows:

[0089] Y = (y ij ) m×n i = 1, 2, ..., m, j = 1, 2, ..., n

[0090] Where n is the number of attributes contained in each undetermined permutation and combination scheme, and y ij Let j be the attribute value contained within the i-th undetermined permutation and combination scheme;

[0091] Step 132: Based on the given weight vector, determine the weighted decision matrix associated with the decision matrix;

[0092] Step 133: Determine the positive ideal solution and the negative ideal solution based on the weighted decision matrix, and then calculate the weighted decision matrix using the maximization of relative proximity as specified by the TOPSIS decision method as the screening criterion. Finally, determine the optimal permutation and combination scheme from the decision matrix based on the calculation results. Each attribute value contained in the optimal permutation and combination scheme refers to the optimal electrical connection relationship between all photovoltaic modules defined in a corresponding column on the photovoltaic array.

[0093] In this embodiment of the invention, the specific implementation process of step 132 includes the following:

[0094] First, the decision matrix is ​​normalized to obtain:

[0095]

[0096] Secondly, given the weight vector W = [ω1, ω2, ..., ω n ] T Where ω1+ω2+…+ω n =1, therefore the weighted decision matrix is ​​determined as follows:

[0097]

[0098] Where R is the normalized matrix corresponding to the decision matrix Y, r ij For parameter y ij The corresponding normalization result, ω j Let be the j-th weight in the weight vector W, representing the weight of the j-th attribute in any undetermined permutation and combination scheme, and V be the weighted decision matrix associated with the decision matrix Y. ij For parameter y ijThe corresponding weighted result.

[0099] In this embodiment of the invention, the specific implementation process of step 133 includes the following:

[0100] Step C1: From the perspective of considering benefits, determine the positive ideal solution and the negative ideal solution based on the weighted decision matrix V, as follows:

[0101]

[0102] Alternatively, from a cost-considering perspective, the positive and negative ideal solutions can be determined based on the weighted decision matrix V, as follows:

[0103]

[0104] Among them, A * For the positive ideal solution, Let A be the positive ideal solution. * The j-th attribute value contained, A - For a negative ideal solution, Let A be the negative ideal solution. - The j-th attribute value contained therein;

[0105] Step C2: Calculate the positive ideal solution A for each undetermined permutation and combination scheme. * The Euclidean distance between them and its distance to the negative ideal solution A - The Euclidean distance between them is calculated using the following formula:

[0106]

[0107] in, For the i-th undetermined permutation and combination scheme to the positive ideal solution A * The Euclidean distance between them Let A be the Euclidean distance between the i-th undetermined permutation and combination scheme and the negative ideal solution A-.

[0108] Step C3: Calculate the relative closeness of each undetermined permutation and combination scheme to the ideal scheme. The corresponding calculation formula is as follows:

[0109]

[0110] in, This represents the relative closeness between the i-th undetermined permutation / combination scheme and the ideal scheme;

[0111] Step C4: Extract the most closely related permutation and combination scheme from the m undetermined permutations and combinations, and define it as the optimal permutation and combination scheme.

[0112] It should be noted that after obtaining the optimal permutation and combination scheme by performing step 130 above, the method further includes: changing the series-parallel-cross-connection structure between the photovoltaic modules on the photovoltaic array by using a switching matrix to conform to the optimal electrical connection relationship between all photovoltaic modules defined in each column of the photovoltaic array, thereby achieving the purpose of dispersing the shadows on the photovoltaic array; the technician reconstructs a 10×10 photovoltaic array according to the embodiment of the present invention, and the implementation effect is as follows... Figure 2 As shown, the shadows on each row of photovoltaic modules in this photovoltaic array can be effectively dispersed. The shading effect of this photovoltaic array is determined by the irradiance level (unit: W / m²). 2 The lower the illumination level, the more severe the shadowing.

[0113] In this embodiment of the invention, when the photovoltaic array is shaded, the SPEA2 algorithm, which has strong search capabilities and fast convergence speed, is used to iteratively optimize the photovoltaic array reconstruction model with the goal of maximizing output power and minimizing the number of switching operations. Then, the TOPSIS decision method is used to obtain the optimal electrical connection relationship between all photovoltaic modules defined in each column of the photovoltaic array. This can reduce switching losses and lower economic costs.

[0114] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the structural composition of a photovoltaic array reconfiguration system based on dual-objective optimization provided in an embodiment of the present invention. The system includes the following components:

[0115] Module 210 is used to establish a photovoltaic array reconfiguration model, which includes an optimization function aimed at maximizing output power, an optimization function aimed at minimizing the number of switching operations, and switching constraints.

[0116] Optimization module 220 is used to iteratively optimize the photovoltaic array reconstruction model using the SPEA2 algorithm to obtain the Pareto front;

[0117] The selection module 230 is used to select a compromise solution from the Pareto front using the TOPSIS decision method, thereby obtaining the optimal arrangement and combination scheme of the photovoltaic array.

[0118] The content of the above method embodiments is applicable to this system embodiment. The functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are the same as those in the above method embodiments. The functional principles implemented in this system embodiment will not be repeated here.

[0119] In addition, embodiments of the present invention also provide a computer device, including:

[0120] One or more processors;

[0121] Memory;

[0122] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the photovoltaic array reconfiguration method based on bi-objective optimization as described above.

[0123] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and the processor connects to other parts of the entire computer device using various interfaces and lines.

[0124] The memory can be used to store the application programs and / or modules. The processor implements various functions of the computer device by running or executing the application programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein: the program storage area is used to store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.); the data storage area is used to store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart-media-card (SMC), secure-digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0125] Although the description of this application has been quite detailed and particularly focused on several of the described embodiments, it is not intended to limit itself to any of these details or embodiments or any particular embodiment. Rather, it should be considered as effectively covering the intended scope of this application by referring to the appended claims and taking into account the broad possible interpretations of these claims provided by the prior art. Furthermore, the foregoing description of this application with respect to embodiments foreseeable by the inventors is intended to provide a useful description, and non-substantial modifications to this application that have not yet been foreseen may still represent equivalent modifications.

Claims

1. A photovoltaic array reconfiguration method based on dual-objective optimization, characterized in that, The method includes: Step 110: Establish a photovoltaic array reconfiguration model. The photovoltaic array reconfiguration model includes an optimization function with the goal of maximizing output power, an optimization function with the goal of minimizing the number of switching operations, and switching constraints. The optimization function with the goal of maximizing output power and the optimization function with the goal of minimizing the number of switching operations are denoted as bi-objective optimization functions. Step 120: Iteratively optimize the photovoltaic array reconstruction model using the SPEA2 algorithm to obtain the Pareto front; Step 130: Use the TOPSIS decision method to select a compromise solution from the Pareto front, and then obtain the optimal arrangement and combination scheme of the photovoltaic array; The step of iteratively optimizing the photovoltaic array reconstruction model using the SPEA2 algorithm to obtain the Pareto front includes: Step 121: Determine all photovoltaic modules currently shaded in the photovoltaic array, and at the same time determine the basic parameters required by the SPEA2 algorithm, including the maximum number of iterations, population size, and external population size; Step 122: Set the current iteration number to k=0, and randomly initialize multiple initial permutation and combination schemes of the photovoltaic array in the photovoltaic array reconstruction model to obtain the current population. At the same time, create an empty current external population. ; Step 123: In the k-th iteration, the fitness allocation strategy specified by the SPEA2 algorithm is used, combined with the continuous variable discretization formula and the bi-objective optimization function, to determine the current population. and current external population The precise fitness values ​​of all individuals are used to generate a new current external population through environmental selection as defined by the SPEA2 algorithm. The continuous variable discretization formula is applied to an optimization function that aims to minimize the number of switch switching operations; the corresponding expression is: In the formula, The first matrix is ​​composed of the electrical switching states of all photovoltaic modules on the photovoltaic array, and all elements within the first matrix are discrete values. The number of rows of photovoltaic modules set for a photovoltaic array. For the first photovoltaic array The second matrix, consisting of the electrical switching states of all photovoltaic modules defined by the column, and where all elements are continuous values, is obtained through iterative processing using the SPEA2 algorithm. Refers to the sorting function; Step 124: Determine if k has reached the maximum number of iterations; if so, then change the new current external population. The non-dominated individuals in the group constitute the Pareto front; otherwise, proceed to step 125. Step 125: Based on the new current external population A new current population is generated using mating selection and crossover mutation as defined by the SPEA2 algorithm. Assign k+1 to k, then return to step 123.

2. The photovoltaic array reconfiguration method based on dual-objective optimization according to claim 1, characterized in that, In step 110, the expression for the bi-objective optimization function is: in: The switching constraint condition is: In the formula, The output power of the photovoltaic array. This refers to the number of times the photovoltaic array is switched on and off. This represents the total output voltage of the photovoltaic array. This represents the total output current of the photovoltaic array. The number of rows of photovoltaic modules set for the photovoltaic array. For symbolic functions, For the first photovoltaic array row and number The current electrical switching state of the individual photovoltaic module specified in the column. For the first photovoltaic array row and number The initial electrical switching state of a single photovoltaic module as defined in the column. and All are discrete values. For the first photovoltaic array row and number The output current of a single photovoltaic module as defined in the column. For the first photovoltaic array The total output voltage of all photovoltaic modules defined by the row.

3. The photovoltaic array reconfiguration method based on dual-objective optimization according to claim 1, characterized in that, The implementation process of step 130 includes: Step 131: Based on the Pareto front containing multiple undetermined permutations and combinations, create a decision matrix; Step 132: Based on the given weight vector, determine the weighted decision matrix associated with the decision matrix; Step 133: Determine the positive ideal solution and the negative ideal solution based on the weighted decision matrix, and then calculate the weighted decision matrix using the maximization of relative proximity as specified by the TOPSIS decision method as the screening criterion. Finally, determine the optimal permutation and combination scheme from the decision matrix based on the calculation results.

4. The photovoltaic array reconfiguration method based on dual-objective optimization according to claim 3, characterized in that, The optimal arrangement and combination scheme is the optimal electrical connection relationship between all photovoltaic modules defined in each column of the photovoltaic array.

5. A photovoltaic array reconfiguration system based on dual-objective optimization, characterized in that, The system includes: A module is established to establish a photovoltaic array reconfiguration model. The photovoltaic array reconfiguration model includes an optimization function with the objective of maximizing output power, an optimization function with the objective of minimizing the number of switching operations, and switching constraints. The optimization function with the objective of maximizing output power and the optimization function with the objective of minimizing the number of switching operations are denoted as bi-objective optimization functions. The optimization module is used to iteratively optimize the photovoltaic array reconstruction model using the SPEA2 algorithm to obtain the Pareto front. The selection module is used to select a compromise solution from the Pareto front using the TOPSIS decision method, thereby obtaining the optimal arrangement and combination scheme of the photovoltaic array; The step of iteratively optimizing the photovoltaic array reconstruction model using the SPEA2 algorithm to obtain the Pareto front includes: Step 121: Determine all photovoltaic modules currently shaded in the photovoltaic array, and at the same time determine the basic parameters required by the SPEA2 algorithm, including the maximum number of iterations, population size, and external population size; Step 122: Set the current iteration number to k=0, and randomly initialize multiple initial permutation and combination schemes of the photovoltaic array in the photovoltaic array reconstruction model to obtain the current population. At the same time, create an empty current external population. ; Step 123: In the k-th iteration, the fitness allocation strategy specified by the SPEA2 algorithm is used, combined with the continuous variable discretization formula and the bi-objective optimization function, to determine the current population. and current external population The precise fitness values ​​of all individuals are used to generate a new current external population through environmental selection as defined by the SPEA2 algorithm. The continuous variable discretization formula is applied to an optimization function that aims to minimize the number of switch switching operations; the corresponding expression is: In the formula, The first matrix is ​​composed of the electrical switching states of all photovoltaic modules on the photovoltaic array, and all elements within the first matrix are discrete values. The number of rows of photovoltaic modules set for a photovoltaic array. For the first photovoltaic array The second matrix, consisting of the electrical switching states of all photovoltaic modules defined by the column, and where all elements are continuous values, is obtained through iterative processing using the SPEA2 algorithm. Refers to the sorting function; Step 124: Determine if k has reached the maximum number of iterations; if so, then change the new current external population. The non-dominated individuals in the group constitute the Pareto front; otherwise, proceed to step 125. Step 125: Based on the new current external population A new current population is generated using mating selection and crossover mutation as defined by the SPEA2 algorithm. Assign k+1 to k, then return to step 123.

6. A computer device, characterized in that, include: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the photovoltaic array reconfiguration method based on bi-objective optimization as described in any one of claims 1 to 4.

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