Photovoltaic power generation efficiency influence factor evaluation method based on AHP-ISM

Abstract

The invention relates to the technical field of photovoltaic power generation efficiency factor evaluation, and discloses a photovoltaic power generation efficiency influence factor evaluation method based on AHP-ISM, and the method comprises the steps: obtaining main influence factors which affect the photovoltaic power generation efficiency; respectively constructing a reachable matrix and a judgment matrix based on the influence factors; performing hierarchical decomposition on the influence factors by using the reachable matrix based on an ISM model so as to obtain a hierarchical structure diagram; calculating the weight of each influence factor through the judgment matrix in combination with an APH (Advanced Performance Hierarchy) model; and combining the hierarchical structure diagram with the weights of the corresponding influence factors to jointly construct a weight structure diagram of the influence factors so as to realize comprehensive evaluation of each influence factor. According to the photovoltaic power generation efficiency influence factor weight structure chart constructed by the method, deep and comprehensive understanding about the efficiency influence factors of the photovoltaic power station system is provided for the method, and important data and theoretical support are provided for formulation of subsequent improvement strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation efficiency factor evaluation, and in particular to a photovoltaic power generation efficiency influencing factor evaluation method based on AHP-ISM. Background Art

[0002] The technical shortcomings and deficiencies of the traditional evaluation methods for factors affecting photovoltaic power generation efficiency are mainly reflected in the fact that traditional methods often focus on the evaluation of a single or a few factors, and fail to fully consider all factors that may affect photovoltaic power generation efficiency; accurately obtaining data on all relevant factors and effectively processing them may be a difficult task. For example, data on light intensity and temperature can be obtained in real time through sensors, but data on factors such as humidity, shadows, and pollution are relatively difficult to obtain. In addition, the accuracy and reliability of the data may also be affected by factors such as sensor accuracy and loss during data transmission; the models used in traditional evaluation methods may not accurately reflect the complex relationship between all factors. For example, the impact of light intensity and temperature on photovoltaic power generation efficiency may not be a simple linear relationship, but there may be interactions. If the evaluation model cannot accurately describe this relationship, then the evaluation results may be biased.

[0003] The following are the four mainstream evaluation methods for factors affecting photovoltaic power generation efficiency:

[0004] (1) Qualitative analysis: Qualitative analysis focuses on non-quantitative description and analysis of factors affecting photovoltaic power generation. This method usually relies on expert opinions, historical experience and case studies, and reveals the potential impact of various factors on photovoltaic power generation through induction, deduction and comparison. For example, experts can evaluate the feasibility and potential benefits of photovoltaic power generation projects by comprehensively considering factors such as local climate, geographical environment and light conditions.

[0005] (2) Statistical analysis: Statistical analysis uses statistical principles and methods to collect, organize and analyze relevant data on photovoltaic power generation. This method can reveal the correlation, trend and law between various factors and photovoltaic power generation. Through statistical techniques such as regression analysis, variance analysis and covariance analysis, the influence of various factors on photovoltaic power generation can be quantified, providing a scientific basis for decision-making.

[0006] (3) Experimental analysis method: The experimental analysis method conducts experiments on photovoltaic power generation systems under specific conditions to observe and record the impact of various factors on photovoltaic power generation effects. This method can more directly reveal the mechanism of action of various factors and provide empirical support for the design and optimization of photovoltaic power generation systems.

[0007] (4) Case study method: The case study method conducts field research and analysis on completed photovoltaic power generation projects to summarize their successful experiences and shortcomings. By comparing the differences in the characteristics, operating results and influencing factors of different projects, conclusions and suggestions with universality and regularity can be extracted. This method helps to provide useful reference and reference for future photovoltaic power generation projects.

[0008] The above traditional analysis methods have their own advantages and disadvantages in the analysis of factors affecting photovoltaic power generation, but they also have certain limitations. For example, the qualitative analysis method may focus on qualitative analysis, so it will be affected by subjective factors, and it does not take into account the coupling between the various influencing factors; the statistical analysis method requires sufficient data support, and may be limited by factors such as data quality and sample quantity. For example, in a province where photovoltaic power stations have not been widely put into use, there is no original data to provide statistics and analysis; the experimental analysis method usually requires high cost and time investment, and the experimental results may be limited by factors such as experimental conditions and equipment accuracy; the case study method is not universal. For the photovoltaic power generation project, the degree of influence of influencing factors in different places on the efficiency of photovoltaic power generation varies. The specific conditions of each place are different, and the conclusions drawn have certain particularities. Summary of the invention

[0009] The present invention provides a method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM to solve the above problems.

[0010] The present invention is achieved through the following technical solutions:

[0011] A method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM, including:

[0012] Obtain the main factors affecting photovoltaic power generation efficiency;

[0013] Based on all the influencing factors, a reachable matrix and a judgment matrix are respectively constructed, wherein the elements in the reachable matrix are scaled absolute values ​​for judging whether there is an influence between the influencing factors, and the elements in the judgment matrix are scaled absolute values ​​for comparing the importance of the influencing factors;

[0014] Decomposing the influencing factors hierarchically based on the ISM model using the reachability matrix, thereby obtaining a hierarchical structure diagram after the influencing factors are divided into hierarchical levels;

[0015] The weight of each influencing factor is calculated by combining the judgment matrix with the APH model;

[0016] The hierarchical structure diagram is combined with the weights of the corresponding influencing factors to jointly construct the weight structure diagram of the influencing factors, so as to obtain the weight of each influencing factor in the system on the basis of considering the coupling effect between the influencing factors, and realize the comprehensive evaluation of each influencing factor.

[0017] For optimization, the influencing factors include weather, cleanliness, array operation mode, adaptability, component efficiency, temperature and humidity, solar radiation and operation and maintenance management.

[0018] As an optimization, the elements in the reachable matrix include 0 and 1, where 1 indicates that the influencing factor corresponding to the row where the element is located has an influence on the influencing factor corresponding to the column where the element is located, and 0 indicates that the influencing factor corresponding to the row where the element is located has no influence on the influencing factor corresponding to the column where the element is located.

[0019] As an optimization, the elements in the judgment matrix include non-zero positive numbers, and the elements in the judgment matrix satisfy a ij represents the element in the i-th row and j-th column of the judgment matrix, indicating that the influencing factor corresponding to the i-th row is more important than the influencing factor corresponding to the j-th column. ij times, b ij represents the element in the jth row and ith column of the judgment matrix, indicating that the influencing factor corresponding to the jth row is more important than the influencing factor corresponding to the ith column. ij times.

[0020] As an optimization, the reachable matrix is ​​hierarchically decomposed into the influencing factors based on the ISM model, so that the specific process of obtaining the hierarchical structure diagram after the influencing factors are divided into levels is as follows:

[0021] A1. Establishing a reachable set, a predecessor set, and a common set formed by the intersection of the reachable set and the predecessor set of each influencing factor according to the reachable matrix, wherein the reachable set of the influencing factor is a set of all influencing factors that can be affected by the influencing factor, and the predecessor set of the influencing factor is a set of all influencing factors that can affect the influencing factor;

[0022] A2, let D = 1;

[0023] A3. Find out the influencing factors that are completely identical between the reachable set and the common set, and use the influencing factors found in this iteration as influencing factors at the same level;

[0024] A4, extracting the influencing factors found in step A3 from the reachable set of each influencing factor to form a new reachable set;

[0025] A5. Determine whether there is still a reachable set. If so, set D=2 and then return to A3. Otherwise, end and obtain the hierarchical situation after all the influencing factors are divided.

[0026] As an optimization, the specific process of calculating the weight of each influencing factor by combining the judgment matrix with the APH model is as follows:

[0027] The maximum eigenvalue root of the judgment matrix is ​​calculated and a consistency check is performed on the eigenvalue root. If the maximum eigenvalue root passes the consistency check, the initial eigenvalue vector of the maximum eigenvalue root is obtained, and the initial eigenvalue vector is normalized to obtain an intermediate eigenvalue vector. Each element in the intermediate eigenvalue vector is the weight corresponding to each influencing factor. If the consistency check fails, the pairwise comparison results in the judgment matrix are reviewed and adjusted based on the feedback from experts to improve the consistency.

[0028] As an optimization, the specific method for consistency check of characteristic roots is:

[0029] Calculate the consistency ratio CR, where If and only if CR<0.1, it means that the consistency test of the characteristic root has passed, where λ represents the characteristic root, n represents the order of the judgment matrix, and RI represents the consistency index.

[0030] As an optimization, the weight structure diagram is divided into three layers according to the degree of direct influence on photovoltaic power generation efficiency, and is divided into the highest layer about decision-making objectives, the middle layer about influencing factors and the lowest layer about decision alternatives in order of direct influence from high to low. The influencing factors of the highest level obtained after dividing the levels are combined with the corresponding weights as the highest layer, the influencing factors of the second highest level obtained after dividing the levels are combined with the corresponding weights as the middle layer, and the influencing factors of all levels except the highest level and the second highest level obtained after dividing the levels are combined with the corresponding weights as the lowest layer.

[0031] As an optimization, the elements in the reachable matrix and the judgment matrix are obtained by one or more of consulting literature, questionnaire survey, and expert interview.

[0032] The present invention also discloses a photovoltaic power generation efficiency influencing factor evaluation system based on AHP-ISM, comprising:

[0033] A collection module is used to obtain the main factors affecting photovoltaic power generation efficiency;

[0034] A matrix construction module, used to construct a reachable matrix and a judgment matrix based on all the influencing factors, wherein the elements in the reachable matrix are scaled absolute values ​​for judging whether there is an influence between the influencing factors, and the elements in the judgment matrix are scaled absolute values ​​for comparing the importance of the influencing factors;

[0035] A hierarchical module, used for performing hierarchical decomposition of the influencing factors by the reachable matrix based on an ISM model, thereby obtaining a hierarchical structure diagram after the influencing factors are divided into hierarchies;

[0036] A weight calculation module, used to calculate the weight of each of the influencing factors by combining the judgment matrix with the APH model;

[0037] The evaluation module is used to combine the hierarchical structure diagram with the weights of the corresponding influencing factors to jointly construct the weight structure diagram of the influencing factors, so as to obtain the weight of each influencing factor in the system on the basis of taking into account the coupling effect between the influencing factors, and realize the comprehensive evaluation of each of the influencing factors.

[0038] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0039] The present invention firstly uses the Explanatory Structural Model (ISM) to decompose the hierarchical relationship of the influencing factors affecting the efficiency of the photovoltaic power station system, and analyzes the internal relationship of the complex system. It divides the various influencing factors in the system into different levels, and uses directed edges to connect the factors of different levels to each other, thereby forming a model with a hierarchical structure, which can more clearly reveal the interaction and influence relationship between the various influencing factors; then the analytic hierarchy process (AHP) is used to take the efficiency of the photovoltaic power generation system as the target layer, and the weight of each influencing factor on the target layer is obtained through hierarchical analysis, and the rationality of the questionnaire is proved through consistency test; then the weight structure diagram of the influencing factors of photovoltaic power generation efficiency is drawn, and the hierarchical structure is divided into three categories: direct, indirect, and fundamental, which provides the present invention with an in-depth and comprehensive understanding of the influencing factors of the photovoltaic power station system efficiency. These analysis results provide important data and theoretical support for the formulation of subsequent improvement strategies, and lay a solid foundation for improving the system efficiency of photovoltaic power stations. At the same time, it provides important support for the promotion and application of photovoltaic power generation technology, promotes energy transformation, and promotes sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0041] Figure 1 This is a flow chart of a method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM according to the present invention;

[0042] Figure 2 A schematic diagram of a reachability matrix constructed through an ISM expert questionnaire in an embodiment;

[0043] Figure 3 A schematic diagram of a judgment matrix constructed by an AHP expert questionnaire in the embodiment;

[0044] Figure 4 is a weight structure diagram of factors affecting photovoltaic power generation efficiency constructed in the embodiment;

[0045] Figure 5 It is the overall technical roadmap of the method of the present invention. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0047] There are three technical problems in the evaluation of factors affecting the efficiency of traditional photovoltaic power generation. First, traditional methods often focus on the evaluation of a single or a few factors, but fail to fully consider all factors that may affect the efficiency of photovoltaic power generation; second, the model used by the traditional evaluation method cannot accurately reflect the complex relationship between all factors, fails to consider the interaction between various influencing factors, and ignores the coupling between factors; third, it is difficult to accurately obtain data of all relevant factors and effectively process them. The present invention is based on qualitative analysis and does not require accurate acquisition of relevant data. However, compared with the strong subjectivity of traditional qualitative analysis, the AHP-ISM hybrid model is used for calculation based on the subjective scoring of decision makers to make the final result relatively objective.

[0048] Next, the method of the present invention is introduced through specific embodiments.

[0049] This embodiment 1 provides a method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM. Figure 1 As shown, including:

[0050] S1. Obtain the main factors affecting photovoltaic power generation efficiency;

[0051] By reviewing literature, conducting questionnaire surveys, and interviewing experts, we comprehensively sorted out the reasons that may affect the efficiency of photovoltaic power generation, obtained a series of indicator factors, and determined the influencing factors of photovoltaic power generation efficiency. We provided questionnaire scoring tables to relevant experts (the larger the number of scoring experts, the more accurate the results). Here is an example of analyzing 8 factors that affect the efficiency of photovoltaic power generation. The influencing factors are meteorology, cleanliness, array operation mode, adaptability, component efficiency, temperature and humidity, solar radiation, and operation and maintenance management.

[0052] S2. Constructing a reachable matrix and a judgment matrix based on all the influencing factors, respectively, wherein the elements in the reachable matrix are scaled absolute values ​​for judging whether there is an influence between the influencing factors, and the elements in the judgment matrix are scaled absolute values ​​for comparing the importance of the influencing factors;

[0053] like Figure 2 As shown, the elements in the reachable matrix include 0 and 1, wherein 1 indicates that the influencing factor corresponding to the row where the element is located has an influence on the influencing factor corresponding to the column where the element is located, and 0 indicates that the influencing factor corresponding to the row where the element is located has no influence on the influencing factor corresponding to the column where the element is located.

[0054] Establish an ISM model, use the explanatory structural model to establish a directed edge relationship graph between factors, and conduct an in-depth analysis of the interaction and influence relationship between factors. Determine the causal relationship between the two elements, such as whether element 1 has an impact on element 2, and whether element 2 has an impact on element 1. If there is an impact, assign a value of 1. There is no need to judge the causal relationship of the element itself, and the default value is 1. The reachable matrix K can be obtained from the ISM expert questionnaire, and then interval decomposition and inter-level decomposition are required.

[0055] S3. Decomposing the influencing factors in the reachable matrix hierarchically based on the ISM model, thereby obtaining a hierarchical structure diagram after all the influencing factors are divided into hierarchies.

[0056] The specific process is:

[0057] A1. Establishing a reachable set, a predecessor set, and a common set formed by the intersection of the reachable set and the predecessor set of each influencing factor according to the reachable matrix, wherein the reachable set of the influencing factor is a set of all influencing factors that can be affected by the influencing factor, and the predecessor set of the influencing factor is a set of all influencing factors that can affect the influencing factor;

[0058] A2, let D = 1;

[0059] A3. Find out the influencing factors that are completely identical between the reachable set and the common set, and use the influencing factors found in this iteration as influencing factors at the same level;

[0060] A4, extracting the influencing factors found in step A3 from the reachable set of each influencing factor to form a new reachable set;

[0061] A5. Determine whether there is still a reachable set. If so, set D=2 and then return to A3. Otherwise, end and obtain the hierarchical situation after all the influencing factors are divided.

[0062] The first step is to determine the reachable set and predecessor set of each factor ai in the reachable matrix K. The reachable set contains all the factors that can be reached from factor ai (influencing factor ai) through a series of directed paths, while the predecessor set contains all the factors that can reach factor ai.

[0063] That is, the elements in the reachable set of factor ai are the factors that can be affected by factor ai, and the elements in the preceding set of factor ai are the factors that can affect factor ai.

[0064] Compare the common set of factors ai, that is, the intersection of the reachable set and the antecedent set. When the common set is equal to the reachable set, it means that factor ai is only affected by other factors in its level, but not by factors in higher levels. Therefore, the common set can be divided into the highest level. According to this method, the present invention determines and divides the levels of the remaining factors in sequence:

[0065] Table 1 Results after the 0th extraction

[0066]

[0067]

[0068] Table 2 Results after the first extraction

[0069]

[0070] Table 3 Results after the second extraction

[0071]

[0072] Table 4 Results after the third extraction

[0073]

[0074] As can be seen from Table 1, the reachable set of factor a5 is a5, and the common set is also a5. Therefore, during the first traversal, only factor a5 meets the condition that "the reachable set and the common set are exactly the same". Therefore, factor a5 is first selected as the first-level influencing factor. Then, all the factor a5 contained in the reachable sets are extracted (eliminated) to form Table 2.

[0075] Similarly, in Table 2, it can be seen that the reachable sets of factors a2, a3, a4, and a6 are a2, a3, a4, and a6 respectively, and the common sets of factors a2, a3, a4, and a6 are also a2, a3, a4, and a6. Therefore, during the second traversal, factors a2, a3, a4, and a6 meet the condition that "the reachable set and the common set are exactly the same". Therefore, factors a2, a3, a4, and a6 are selected as the second-level influencing factors. Then, factors a2, a3, a4, and a6 contained in all reachable sets are extracted (eliminated) to form Table 3.

[0076] As can be seen from Table 3, the reachable sets of factors a7 and a8 are a7 and a8 respectively, and the common sets of factors a7 and a8 are also a7 and a8. Therefore, during the third traversal, factors a7 and a8 meet the condition that "the reachable sets and common sets are exactly the same". Therefore, factors a7 and a8 are selected as the influencing factors of the third layer. Then, factors a7 and a8 contained in all reachable sets are extracted (eliminated) to form Table 4.

[0077] In Table 4, only factor a1 remains, whose reachable set and common set are both a1, so a1 is taken as the influencing factor of the fourth layer.

[0078] The above table shows the hierarchical decomposition process of the model. In each extraction, if it is found that the reachable set is consistent with the intersection of the reachable set and the preceding set, the feature set can be extracted for hierarchical division. After the hierarchical extraction is completed, the hierarchical structure can be divided, as shown in Table 5.

[0079] Table 5 Level of factors affecting photovoltaic power generation efficiency

[0080]

[0081] It should be noted that, during the 0th extraction, there must be at least one influencing factor that satisfies the situation where the reachable set is equal to the common set.

[0082] S4. Calculate the weight of each influencing factor by combining the judgment matrix with the APH model;

[0083] The elements in the judgment matrix include non-zero positive numbers, and the elements in the judgment matrix satisfy a ij represents the element in the i-th row and j-th column of the judgment matrix, indicating that the influencing factor corresponding to the i-th row is more important than the influencing factor corresponding to the j-th column. ij times, b ij represents the element in the jth row and ith column of the judgment matrix, indicating that the influencing factor corresponding to the jth row is more important than the influencing factor corresponding to the ith column. ij times.

[0084] The analytic hierarchy process (AHP model) is used to analyze and model the factors affecting photovoltaic power generation efficiency. First, according to the AHP expert scoring table ( Figure 2 ) to obtain the judgment matrix A, where the elements in the judgment matrix The goal of decision-making, the factors to be considered (decision-making criteria) and the decision-making object are divided into the highest level, the middle level and the lowest level according to the relationship between them, and a hierarchical structure diagram is drawn. The highest level refers to the purpose of decision-making and the problem to be solved. The lowest level refers to the alternative plans when making decisions. The middle level refers to the factors to be considered and the criteria for decision-making. Here, the target level (highest level) of the present invention is photovoltaic power generation efficiency, and the criterion level (middle level) is the factors affecting photovoltaic power generation efficiency.

[0085] The next step is weight calculation. The specific process of calculating the weights of all the influencing factors by combining the judgment matrix with the hierarchical analysis method is as follows:

[0086] Calculate the largest characteristic root of the judgment matrix,

[0087] A consistency check is performed on the eigenvalue root. If the maximum eigenvalue root passes the consistency check, the initial eigenvalue vector of the maximum eigenvalue root is obtained, and the initial eigenvalue vector is normalized to obtain an intermediate eigenvalue vector. Each element in the intermediate eigenvalue vector is the weight corresponding to each influencing factor. If the consistency check fails, the pairwise comparison results in the judgment matrix are reviewed and adjusted based on the feedback from experts to improve consistency.

[0088] Corresponding to the maximum characteristic root λ of the judgment matrix max The characteristic vector of is normalized (the sum of the elements in the vector is equal to 1) and recorded as W. The elements of W are the ranking weights of the relative importance of the factors at the same level to a factor at the previous level. This process is called hierarchical single sorting. Whether the hierarchical single sorting can be confirmed requires a consistency test. The so-called consistency test refers to the allowable range of inconsistency determined for A. Among them, the only non-zero characteristic root of the n-order consistent matrix is ​​n; the maximum characteristic root λ≥n of the n-order positive reciprocal matrix A, A is a consistent matrix if and only if λ=n. In this embodiment, since there are 8 influencing factors, the judgment matrix is ​​an 8-order matrix, and n is 8.

[0089] Since λ continuously depends on a ij , the larger λ is than n, the more serious the inconsistency of A is. The consistency index is calculated by CI. The smaller the CI is, the greater the consistency is. max The corresponding eigenvector is used as the weight vector of the influence of the compared factor on a certain factor in the upper layer. The greater the inconsistency, the greater the judgment error caused. Therefore, the value of λ-n can be used to measure the inconsistency of A. The consistency index is defined as:

[0090]

[0091] Among them: CI = 0, there is complete consistency;

[0092] The CI is close to 0, which means there is satisfactory consistency;

[0093] The larger the CI, the more serious the inconsistency;

[0094] In order to measure the size of CI, the random consistency index RI is introduced. The consistency index RI is related to the order of the judgment matrix. Generally speaking, the larger the matrix order, the greater the possibility of random deviation from consistency. The corresponding relationship is shown in Table 6:

[0095] Table 6 Correspondence between matrix order and RI

[0096]

[0097] Because the consistency ratio If and only if CR<0.1, it means that the consistency check passes.

[0098] Next, the present invention can obtain the result, as shown in Table 7:

[0099] Table 7 AHP hierarchical analysis results

[0100]

[0101] The eigenvector in Table 7 is the eigenvector corresponding to the eigenroot 8.461, and the weight value is the result of normalizing the eigenvector.

[0102] From the results obtained by the AHP tomography analysis method, it can be seen that among the 8 factors affecting photovoltaic power generation, a7 (solar radiation) and a5 (component efficiency) have a large impact on photovoltaic power generation efficiency and need to be considered first in the decision-making process; the proportions of a1 (meteorology) and a3 (array operation mode) are not much different, and they still need to be considered in the decision-making process according to the actual situation; a2 (cleanliness), a4 (adaptability), a6 (temperature and humidity), and a8 (operation and maintenance management) can be considered last in the decision-making ranking.

[0103] Then the consistency test results are performed to see if they are reasonable and correct, as shown in Table 8:

[0104] Table 8 Consistency test results

[0105]

[0106] From the consistency test results, we can see that CR=0.047<0.1 (consistency test passed).

[0107] S5. The hierarchical structure diagram is combined with the weights of the corresponding influencing factors to jointly construct a weight structure diagram of the influencing factors, so as to obtain the weight of each influencing factor in the system on the basis of taking into account the coupling effect between the influencing factors, and realize a comprehensive evaluation of each of the influencing factors.

[0108] In this embodiment, the weight structure diagram is divided into three layers according to the degree of direct influence on photovoltaic power generation efficiency, and is divided into a highest layer regarding decision-making objectives, a middle layer regarding influencing factors, and a lowest layer regarding decision alternatives in order of direct influence from high to low. The influencing factors of the highest level obtained after dividing the levels are combined with the corresponding weights as the highest layer, the influencing factors of the second highest level obtained after dividing the levels are combined with the corresponding weights as the middle layer, and the influencing factors of all levels except the highest level and the second highest level obtained after dividing the levels are combined with the corresponding weights as the lowest layer.

[0109] The hierarchical structure of factors affecting photovoltaic power generation efficiency obtained by the ISM model is divided into layers. The elements of the third and fourth layers are merged to simplify the final structure diagram, resulting in a total of three layers, which are divided from top to bottom into direct influencing factor layer, indirect influencing factor layer, and fundamental influencing factor layer.

[0110] The weights of the influencing factors on the target layer obtained by the AHP hierarchical analysis method are combined with the hierarchical structure diagram of the influencing factors of photovoltaic power generation efficiency obtained by the ISM model to obtain the weight structure diagram of the influencing factors of photovoltaic power generation efficiency. Figure 4 As shown, the hierarchical structure of factors affecting photovoltaic system efficiency and the weight coefficient K of each factor can be intuitively seen.

[0111] Embodiment 2 further discloses a photovoltaic power generation efficiency influencing factor evaluation system based on AHP-ISM, comprising:

[0112] A collection module is used to obtain the main factors affecting photovoltaic power generation efficiency;

[0113] A matrix construction module, used to construct a reachable matrix and a judgment matrix based on all the influencing factors, wherein the elements in the reachable matrix are scaled absolute values ​​for judging whether there is an influence between the influencing factors, and the elements in the judgment matrix are scaled absolute values ​​for comparing the importance of the influencing factors;

[0114] A hierarchical module, used for performing hierarchical decomposition of the influencing factors by the reachable matrix based on an ISM model, thereby obtaining a hierarchical structure diagram after the influencing factors are divided into hierarchies;

[0115] A weight calculation module, used to calculate the weight of each of the influencing factors by combining the judgment matrix with the APH model;

[0116] The evaluation module is used to combine the hierarchical structure diagram with the weights of the corresponding influencing factors to jointly construct the weight structure diagram of the influencing factors, so as to obtain the weight of each influencing factor in the system on the basis of taking into account the coupling effect between the influencing factors, and realize the comprehensive evaluation of each of the influencing factors.

[0117] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM, characterized in that: include: Obtain the main factors affecting photovoltaic power generation efficiency; Based on the influencing factors, a reachable matrix and a judgment matrix are respectively constructed, wherein the elements in the reachable matrix are scaled absolute values ​​for judging whether there is an influence between the influencing factors, and the elements in the judgment matrix are scaled absolute values ​​for comparing the importance of the influencing factors; Decomposing the influencing factors hierarchically based on the ISM model using the reachability matrix, thereby obtaining a hierarchical structure diagram after the influencing factors are divided into hierarchies; The weight of each influencing factor is calculated by combining the judgment matrix with the APH model; The hierarchical structure diagram is combined with the weights of the corresponding influencing factors to jointly construct the weight structure diagram of the influencing factors, so as to obtain the weight of each influencing factor in the system on the basis of considering the coupling effect between the influencing factors, and realize the comprehensive evaluation of each influencing factor.

2. The method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM according to claim 1, characterized in that: The influencing factors include weather, cleanliness, array operation mode, adaptability, component efficiency, temperature and humidity, solar radiation and operation and maintenance management.

3. The method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM according to claim 1, characterized in that: The elements in the reachable matrix include 0 and 1, wherein 1 indicates that the influencing factor corresponding to the row where the element is located has an influence on the influencing factor corresponding to the column where the element is located, and 0 indicates that the influencing factor corresponding to the row where the element is located has no influence on the influencing factor corresponding to the column where the element is located.

4. The method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM according to claim 1, characterized in that: The elements in the judgment matrix include non-zero positive numbers, and the elements in the judgment matrix satisfy a ij represents the element in the i-th row and j-th column of the judgment matrix, indicating that the influencing factor corresponding to the i-th row is more important than the influencing factor corresponding to the j-th column. ij times, b ij represents the element in the jth row and ith column of the judgment matrix, indicating that the influencing factor corresponding to the jth row is more important than the influencing factor corresponding to the ith column. ij times.

5. The method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM according to claim 1, characterized in that: The specific process of performing hierarchical decomposition of the influencing factors based on the ISM model on the reachability matrix to obtain a hierarchical structure diagram after the influencing factors are divided into hierarchies is as follows: A1. Establishing a reachable set, a predecessor set, and a common set formed by the intersection of the reachable set and the predecessor set of each influencing factor according to the reachable matrix, wherein the reachable set of the influencing factor is a set of all influencing factors that can be affected by the influencing factor, and the predecessor set of the influencing factor is a set of all influencing factors that can affect the influencing factor; A2, let D = 1; A3. Find out the influencing factors that are completely identical between the reachable set and the common set, and use the influencing factors found in this iteration as influencing factors at the same level; A4, extracting the influencing factors found in step A3 from the reachable set of each influencing factor to form a new reachable set; A5. Determine whether there is still a reachable set. If so, set D=2 and then return to A3. Otherwise, end and obtain the hierarchical situation after all the influencing factors are divided.

6. The method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM according to claim 1, characterized in that: The specific process of calculating the weight of each influencing factor by combining the judgment matrix with the APH model is as follows: Calculate the maximum eigenvalue of the judgment matrix, perform a consistency check on the eigenvalue, and if the maximum eigenvalue passes the consistency check, obtain an initial eigenvector of the maximum eigenvalue, normalize the initial eigenvector to obtain an intermediate eigenvector, and each element in the intermediate eigenvector is the weight corresponding to each influencing factor; If the consistency check fails, the pairwise comparison results in the judgment matrix are reviewed and adjusted based on the expert's feedback to improve consistency.

7. The method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM according to claim 6, characterized in that: The specific method for consistency test of characteristic roots is: Calculate the consistency ratio CR, where If and only if CR<0.1, it means that the consistency test of the characteristic root has passed, where λ represents the characteristic root, n represents the order of the judgment matrix, and RI represents the consistency index.

8. The method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM according to claim 1, characterized in that: The weight structure diagram is divided into three layers according to the degree of direct influence on photovoltaic power generation efficiency, and is divided into a highest layer regarding decision-making objectives, a middle layer regarding influencing factors, and a lowest layer regarding decision alternatives in order of the degree of direct influence from high to low. The influencing factors of the highest level obtained after dividing the levels are combined with the corresponding weights as the highest layer, the influencing factors of the second highest level obtained after dividing the levels are combined with the corresponding weights as the middle layer, and the influencing factors of all levels except the highest level and the second highest level obtained after dividing the levels are combined with the corresponding weights as the lowest layer.

9. The method for evaluating factors affecting photovoltaic power generation efficiency based on AHP-ISM according to claim 1, characterized in that: The elements in the reachable matrix and the judgment matrix are obtained by one or more of consulting literature, conducting questionnaire surveys, and interviewing experts.

10. A photovoltaic power generation efficiency influencing factor evaluation system based on AHP-ISM, used to execute the photovoltaic power generation efficiency influencing factor evaluation method based on AHP-ISM according to any one of claims 1 to 9, characterized in that: include: A collection module is used to obtain the main factors affecting photovoltaic power generation efficiency; A matrix construction module, used to construct a reachable matrix and a judgment matrix based on all the influencing factors, wherein the elements in the reachable matrix are scaled absolute values ​​for judging whether there is an influence between the influencing factors, and the elements in the judgment matrix are scaled absolute values ​​for comparing the importance of the influencing factors; A hierarchical module, used for performing hierarchical decomposition of the influencing factors by the reachable matrix based on an ISM model, thereby obtaining a hierarchical structure diagram after the influencing factors are divided into hierarchies; A weight calculation module, used to calculate the weight of each of the influencing factors by combining the judgment matrix with the APH model; The evaluation module is used to combine the hierarchical structure diagram with the weights of the corresponding influencing factors to jointly construct the weight structure diagram of the influencing factors, so as to obtain the weight of each influencing factor in the system on the basis of taking into account the coupling effect between the influencing factors, and realize the comprehensive evaluation of each of the influencing factors.

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