Power transmission and transformation project implementation scheme comprehensive evaluation method based on weight optimization and multi-attribute similarity

Abstract

A power transmission and transformation project implementation scheme comprehensive evaluation method based on weight optimization and multi-attribute similarity comprises the steps that firstly, a power transmission and transformation project implementation scheme comprehensive evaluation index system comprising four first-level indexes and twelve second-level indexes is constructed, then an improved analytic hierarchy process (IAHP) is provided, and the index weight obtained according to the method is used for evaluating the power transmission and transformation project implementation scheme comprehensive evaluation index system. In consideration of the problem of information overlapping among indexes, the weights are optimized by using index correlation; and finally, providing a multi-attribute similarity consistency projection comprehensive evaluation method to realize comprehensive evaluation of a power transmission and transformation project implementation scheme. According to the method provided by the invention, reliable evaluation can be effectively carried out on the power transmission and transformation project implementation scheme, and the method has a reference value for comprehensive evaluation work of the power transmission and transformation project implementation scheme.

Description

Technical Field

[0001] The present invention belongs to the field of evaluation of implementation plans for power transmission and transformation projects, and particularly relates to a comprehensive evaluation method for implementation plans of power transmission and transformation projects based on weight optimization and multi-attribute similarity. Background Art

[0002] Power transmission and transformation projects are the core links in the power system, undertaking the tasks of electric energy transmission and transformation, and are crucial for the stability and security of power supply. With the increase in electricity demand, the scale and complexity of power transmission and transformation projects are constantly rising, and how to ensure their smooth implementation has become an important issue in the power industry. The comprehensive evaluation of implementation plans aims to comprehensively evaluate aspects such as engineering design, construction quality, equipment selection, operation and maintenance, while taking into account factors such as environmental protection and safety guarantee. Through comprehensive evaluation, potential risks can be identified, resource allocation can be optimized, project benefits can be improved, and high-quality project completion can be ensured. In addition, with the popularization of the concept of green development, environmental protection and sustainability have also become important contents of evaluation, providing a strong guarantee for achieving more efficient, safe and environmentally friendly power transmission and transformation projects.

[0003] After retrieving the existing literature, it is found that the literature "Multi-attribute Objective Weight Assignment Method Based on Attribute Importance" (Huang Dingxuan, Wu Zhenye, Zong Yunzhang. Multi-attribute Objective Weight Assignment Method Based on Attribute Importance [J]. Systems Engineering Theory Methodology Applications, 2004, (03): 203-207.) proposed a method for allocating objective weights using attribute importance and discussed how to determine the objective weights of each attribute from objective data according to attribute importance. The literature "Multi-factor Weight Assignment Method Based on Objective Information Entropy" (Huang Dingxuan. Multi-factor Weight Assignment Method Based on Objective Information Entropy [J]. Systems Engineering Theory Methodology Applications, 2003, (04): 321-324.) uses a weight determination method based on objective information entropy, and the weights are determined using the objective weighting method. Although it avoids relying on expert experience, the results may not necessarily conform to objective reality. The literature "Comprehensive Evaluation Model of Power Supply Service Quality Based on Fuzzy Analytic Hierarchy Process" (Wang He, Zeng Ming, Chen Shan, etc. Comprehensive Evaluation Model of Power Supply Service Quality Based on Fuzzy Analytic Hierarchy Process [J]. Power Grid Technology, 2006, (17): 92-96.) uses the group decision-making method and fuzzy mathematics to calculate the index weights. Although it solves the fuzzy uncertainty problem in constructing the comparison judgment matrix, the importance of each index is still determined by expert opinions. The literature "Research on Comprehensive Evaluation Method of Power Quality Based on Fuzzy Theory" (Tan Jiamao, Huang Shaoxian. Research on Comprehensive Evaluation Method of Power Quality Based on Fuzzy Theory [J]. Relay, 2006, (03): 55-59.) determines the weights of each index according to expert opinions, and all use the subjective weighting method, which overly relies on expert experience. The above-mentioned literature determines the index weights only from a single aspect, making it impossible for the weights to take into account both subjectivity and objectivity at the same time.

[0004] To more reasonably determine the index weights, the literature "Evaluation of the Popular Science Ability of Water Conservancy Scenic Areas Based on Entropy Weight Method - AHP Weighting" (Huang Tianyuan, Wang Shunsheng. Evaluation of the Popular Science Ability of Water Conservancy Scenic Areas Based on Entropy Weight Method - AHP Weighting [J]. Water Resources Planning and Design, 2024, (06): 94-98+120.) applies the multiplication combination method to combine the entropy weight method and AHP for weighting; the literature "Research on the Safety Evaluation of High-rise Building Construction Based on Information Entropy - TOPSIS [J]" (Yu Yang. Research on the Safety Evaluation of High-rise Building Construction Based on Information Entropy - TOPSIS [J]. Urban Construction Theory Research (Electronic Edition), 2024, (19): 101-103.) uses the TOPSIS method to calculate the distances to the optimal set and the worst set using the Euclidean distance to determine the degree of fit, and then calculates the combined weight coefficient. However, the above-mentioned literature does not consider the overlap between the data of each index, making the weights unreasonable. Summary of the Invention

[0005] To solve the above technical problems, the present invention discloses a comprehensive evaluation method for the implementation plan of a power transmission and transformation project based on weight optimization and multi-attribute similarity. First, a comprehensive evaluation index system for the implementation plan of a power transmission and transformation project is constructed, which includes 4 first-level indicators and 12 second-level indicators. Then, an improved analytic hierarchy process (IAHP) is proposed to obtain the index weights, and considering the information overlap problem among the indicators, the weights are optimized using the index correlation. Finally, a comprehensive evaluation method based on multi-attribute similarity consistency projection is proposed to achieve the comprehensive evaluation of the implementation plan of a power transmission and transformation project. This method can effectively and reliably evaluate the implementation plan of a power transmission and transformation project and has reference value for the comprehensive evaluation work of the implementation plan of a power transmission and transformation project.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A comprehensive evaluation method for the implementation plan of a power transmission and transformation project based on weight optimization and multi-attribute similarity, comprising the following steps:

[0008] Step 1: Construct a comprehensive evaluation index system for the implementation plan of a power transmission and transformation project, which includes multiple first-level indicators and multiple second-level indicators;

[0009] Step 2: Propose an improved analytic hierarchy process (IAHP) to obtain the index weights;

[0010] Step 3: Considering the information overlap problem among the indicators, optimize the index weights using the index correlation;

[0011] Step 4: Based on the comprehensive evaluation method of multi-attribute similarity consistency projection, achieve the comprehensive evaluation of the implementation plan of a power transmission and transformation project.

[0012] In the above Step 1, in the comprehensive evaluation of the implementation plan of a power transmission and transformation project, in order to comprehensively evaluate the feasibility and benefits of the project, the present invention selects 4 first-level indicators, namely: technical feasibility indicator, economic feasibility indicator, environmental impact indicator, and social benefit and risk management indicator;

[0013] 1) Technical feasibility indicator:

[0014] The technical feasibility indicator is used to evaluate whether the power transmission and transformation project has technical executability and rationality, and mainly includes the following 3 second-level indicators:

[0015] Equipment performance indicator: Used to evaluate the technical maturity, performance stability and adaptability of the selected equipment. The equipment should have stability to ensure low failure rate and high reliability during long-term operation.

[0016] System stability indicator: Determine the stability and response ability of the power transmission and transformation project simulation system under different load conditions through simulation calculation.

[0017] Construction period indicator: Used to evaluate the construction time of each plan, quantified according to the milestone nodes of the project schedule, and compare the implementation periods of different plans;

[0018] 2) Economic feasibility indicators:

[0019] Economic feasibility indicators are mainly judged by evaluating the investment, return and long-term economic benefits of the project funds. It mainly includes the following 4 secondary indicators:

[0020] Total investment indicator: Includes equipment procurement costs, construction costs, project management costs, land use costs, etc. Calculate the total investment of each plan accurately through the project budget to compare the capital requirements of different plans.

[0021] Operating cost indicator: Used to evaluate the fixed costs and variable costs during the operation of the project, including daily maintenance, repair costs, power losses of equipment, etc.

[0022] Return on investment indicator: Used to calculate the internal rate of return of the project and evaluate the economic benefits of long-term investment. Calculate the return on investment by comparing the expected income and cost within the project life cycle.

[0023] Unit power transmission cost indicator: Used to evaluate the cost required for each kilowatt-hour of power transmission, usually used to measure the economic benefits of different plans.

[0024] 3) Environmental impact indicators:

[0025] Environmental impact indicators are mainly used to evaluate the possible impacts on the natural environment during the construction and operation of the project. It mainly includes the following 2 secondary indicators:

[0026] Carbon emission indicator: Used to evaluate the impact of the project on the environment, especially carbon emissions. The carbon dioxide emissions during the use of equipment can be calculated by simulation, such as the carbon dioxide emissions generated per megawatt of power transmission per year.

[0027] Energy efficiency indicator: Used to evaluate the energy conversion efficiency of the power system and calculate the energy loss ratio of the equipment and system during power transmission. Higher energy efficiency means lower energy loss.

[0028] 4) Social benefits and risk management indicators:

[0029] Social benefits and risk management indicators involve the contribution of the project to society, potential risks and how to effectively manage these risks. It mainly includes the following 3 secondary indicators:

[0030] New power supply capacity indicator: Used to evaluate the maximum capacity increase of power supply after the implementation of the project.

[0031] Project benefit period indicator: Used to evaluate the economic benefit cycle of a project, usually represented by the effective operation period of the project.

[0032] Risk assessment index indicator: Through a quantitative risk assessment model, evaluate the possible technical risks, financial risks, etc. faced during the project implementation process, usually represented by scores or risk levels.

[0033] In step 2, the present invention uses a trapezoidal fuzzy number complementary judgment matrix to replace the traditional AHP judgment matrix, and designs a new consistency test method. On this basis, a consistency correction method is proposed to avoid the need for experts to re-score due to the inconsistency of the judgment matrix. The improved analytic hierarchy process (IAHP) calculates the subjective weights of each index, including the following steps:

[0034] Step 2.1: Construct a trapezoidal fuzzy number complementary judgment matrix:

[0035] The 13 indicators y 1 , y 2 ,..., y n for the comprehensive evaluation of the transmission and transformation project implementation plan are expressed as the set Y = {y i ∣i ∈ N}, where N = {1, 2,..., n}, N represents the index set, n represents the number of indicators, and y i represents the i-th indicator.

[0036] Based on Table 1, construct a trapezoidal fuzzy number complementary judgment matrix Where:

[0037]

[0038] In the formula: represents the importance of indicator y j relative to indicator y i ; a ij , b ij , c ij , d ij represent trapezoidal membership degrees, and a ij ≤ b ij ≤ c ij ≤ d ij .

[0039] Step 2.2: Consistency test:

[0040] Invite k experts to participate in the evaluation, then the trapezoidal fuzzy number complementary judgment matrix of each expert can be established as represents the trapezoidal fuzzy number complementary judgment matrix.

[0041] To test the consistency, a kernel and a kernel operator are introduced to calculate the kernel matrix of the trapezoidal fuzzy number complementary judgment matrix. The trapezoidal fuzzy number has the following kernel definition:

[0042]

[0043] where: represents the kernel of the trapezoidal fuzzy number , and represent the trapezoidal membership degrees given by the k-th expert.

[0044] The kernel matrix of the trapezoidal fuzzy number complementary judgment matrix can be expressed as:

[0045]

[0046] Based on the kernel matrix calculate the consistency index ρ:

[0047]

[0048] where: represents the kernel of the trapezoidal fuzzy number , the kernel of the trapezoidal fuzzy number , the kernel of the trapezoidal fuzzy number , i represents the i-th index, j represents the j-th index, and f represents the f-th index.

[0049] If ρ < ε, where ε represents the critical value of the consistency index, then satisfies the consistency criterion and can proceed to step 2.4; otherwise, continue to the next step. Among them, ε = 0.2 can be used as the critical value of the consistency index.

[0050] Step 2.3: Consistency correction:

[0051] 1) First, calculate the eigenvalue matrix of the kernel matrix where represents the eigenvalue of

[0052]

[0053] where: represents the sorting value of different rows; χ is a constant and χ ≥ (n - 1) / 2;

[0054] 2) Based on the kernel matrix calculate its deviation matrix O:

[0055]

[0056] Among them: o ij represents the deviation matrix of trapezoidal fuzzy numbers, (o ij ) n×n represents the deviation matrix of the complementary judgment matrix of trapezoidal fuzzy numbers.

[0057] |o st | = max{|o ij |: i < j, i, j ∈ n}, s, t represent the row and column indices; o st represents the maximum deviation amount, i represents the row index, j represents the column index; max{|o ij |: i < j, i, j ∈ n} represents the maximum value of the absolute values of the elements in the deviation matrix of trapezoidal fuzzy numbers.

[0058] 3) When o st ≠ 0, appropriately increase or decrease the fuzzy numbers in the complementary judgment matrix of trapezoidal fuzzy numbers by an adjustment amount α Specifically: If o st < 0, then represents the trapezoidal fuzzy number after correction. If o st > 0, then

[0059] 4) At this time, calculate the consistency index ρ of step 2.2. If the consistency requirement is not met, correct it again; otherwise, generate the output.

[0060] Step 2.4: Weight calculation:

[0061] After obtaining the complementary judgment matrix of trapezoidal fuzzy numbers that meets the consistency requirement combine the evaluation information of each expert:

[0062]

[0063] Among them: Trapezoidal fuzzy numbers after combining each expert, K represents K experts.

[0064] Thus, the comprehensive judgment matrix

[0065] The fuzzy evaluation value of each index y i :

[0066]

[0067] In the formula: represents the expectation of the fuzzy evaluation value of index y i ; The comprehensive value of trapezoidal membership degrees, a i , bi , c i and d i represent the trapezoidal membership degree after synthesizing various experts, and represent the fuzzy numbers after synthesizing various experts.

[0068] Finally, the weight ω of each index is determined through normalization i :

[0069]

[0070] In step 3, the indicators of the implementation plan for the power transmission and transformation project often lead to information overlap due to certain correlations, and the indicators cannot independently reflect the characteristics of a certain aspect of the implementation plan for the power transmission and transformation project, thus resulting in distorted evaluation results. Optimize the index weights using index correlations, including the following steps:

[0071] Step 3.1: Establish the direct influence matrix Z = (z ij ):

[0072] Determine the influence degree between each pair of same-level indicators of the implementation plan for the power transmission and transformation project by adopting the 5-level scaling method. Among them, z ij is the influence degree of the former on the latter when the i-th indicator is compared with the j-th indicator, and the relationship between the specific linguistic variables and the quantization numbers is shown in Table 2.

[0073] Step 3.2: Calculate the comprehensive influence matrix T

[0074] First, normalize the direct influence matrix Z = (z ij ):

[0075]

[0076] M = Z / λ

[0077] In the formula: λ represents the maximum value of the columns of the direct influence matrix; M is the normalized direct influence matrix.

[0078] Then, calculate the comprehensive influence matrix T = (t ij ) using the following formula, where t ij represents the influence between the i-th and j-th indicators.

[0079] T = M(I - M) -1

[0080] In the formula: I is the identity matrix;

[0081] Step 3.3: Weight optimization:

[0082] First, based on the comprehensive influence matrix T = (t ij ), calculate the influence degree Ci :

[0083]

[0084] Next, define the index independence degree \(g\) to characterize the degree to which this index is not affected by other indices:

[0085]

[0086] In the formula: \(min(C i )\) represents the minimum value of the influence degree, and \(max(C i )\) represents the maximum value of the influence degree.

[0087] The optimized weight result \(W=(w i )\) after considering the index independence degree is:

[0088]

[0089] In the formula: \(w i \) represents the weight of the \(i\)-th index; \(\omega i \) is the index weight obtained by the improved analytic hierarchy process (IAHP) in step 2; \(\beta\) is the independence degree influence factor, which characterizes the influence degree of the independence degree on the index weight, and \(\beta\in(0, +\infty)\); Index weight adjustment amount.

[0090] In step 4, the indices in the comprehensive evaluation index system of the power transmission and transformation project implementation plan are divided into benefit type, cost type, and fixed type index sets, represented by \(\eta = \{\eta 1 ,\eta 2 ,\eta 3 \}\). \(\eta 1 \) represents the benefit type index set, \(\eta 2 \) represents the benefit type index set, \(\eta 3 \) represents the fixed type index set. After collecting the index data \(x i \), different normalization methods are used to normalize it to obtain the normalized data \(y i \):

[0091]

[0092] In the formula: \(m i \) represents the optimal stability value of the index data set \(\eta 3 \); represents the minimum value of the \(i\)-th index quantity, represents the minimum value of the \(i\)-th index quantity, represents the minimum value between the \(i\)-th index and the optimal stability value, represents the minimum value between the \(i\)-th index and the optimal stability value.

[0093] The normalized index data vector obtained is Y = (y i ), obviously, the ideal values of each index are The ideal index data vector is

[0094] Based on the optimized weight vector W above, the weighted decision vector A(w 1 y 1 , w 2 y 2 , …, w m y m ) t , where: A represents the weighted decision vector, w 1 , w 2 , …, w m represent the weights of each index respectively, y 1 , y 2 , …, y m represent the index quantities of each index respectively, and t represents the transpose of the matrix. The weighted ideal vector of the ideal scheme represents the optimal quantity of each index respectively.

[0095] Let the cosine of the included angle r between the weighted decision vector A and the weighted ideal vector A ＊ be:

[0096]

[0097] In the formula: θ represents the included angle between A and A ＊ , ||A|| represents the norm of A, ||A * || represents the norm of A ★ .

[0098] Denote the distance vector d from the weighted decision vector A to the weighted ideal vector A ＊ as:

[0099] d = A * - A;

[0100] Finally, in the traditional projection method, calculate the projection D (D = r·||A||) of the weighted decision vector A on the weighted ideal vector A ＊ as the evaluation result, D represents the evaluation result, r represents the cosine of the included angle, A represents the weighted decision vector, and D() represents the projection of the weighted decision vector A on the weighted ideal vector A ＊ .

[0101] However, there are cases where it is impossible to effectively evaluate the situation where the modulus similarity is small but the cosine similarity is large, or the modulus similarity is large but the cosine similarity is small. Based on this, the present invention introduces the ideal offset vector U:

[0102] U = A + τd = (1 - τ)A + τA *

[0103] where: τ is the preference coefficient, τ ∈ [0, +∞), and τ is related to the deviation degree of the weighted ideal vector A * ; d represents the aforementioned distance vector.

[0104] When β is close to 0, the weighted ideal vector A * approaches the weighted decision vector A; when β = 1, U = A * indicates that A * has no deviation. When β is far from 1 and tends to +∞, it indicates that A * moves away from A. The ideal deviation vector U can adjust the modulus and direction of A * through the parameter β, so as to adjust the weights among the modulus similarity, distance similarity, cosine similarity and direction similarity with the decision vector A.

[0105] Based on the ideal deviation vector U, a new comprehensive evaluation function H is proposed:

[0106]

[0107] Finally, the implementation plan of the power transmission and transformation project is evaluated according to the magnitude of the H value. The larger the H value, the better the construction plan.

[0108] The comprehensive evaluation method for the implementation plan of the power transmission and transformation project based on weight optimization and multi-attribute similarity of the present invention has the following technical effects:

[0109] 1) The evaluation index system constructed in step 1 of the present invention includes multiple primary and secondary indexes, comprehensively covering multiple dimensions such as the technology, economy, environment and social benefits of the power transmission and transformation project. This comprehensive evaluation system ensures a multi-angle and multi-level analysis of the engineering implementation plan, not only paying attention to traditional technical and economic factors, but also considering environmental impacts and social benefits, meeting the requirements of modern green development concepts and sustainable development. Through this all-round index system, the comprehensive feasibility of each implementation plan can be evaluated more objectively, avoiding the deviation caused by one-sided evaluation. In addition, the detailed secondary indexes help to refine the analysis, providing accurate data support for decision-makers, so as to avoid possible risks and problems in actual projects and improve the safety and benefits of the overall project.

[0110] 2) Although the traditional Analytic Hierarchy Process (AHP) is widely used, it often encounters problems such as inconsistent judgment matrices and strong subjectivity of experts. The improved Analytic Hierarchy Process (IAHP) in step 2 of the present invention overcomes these drawbacks by introducing trapezoidal fuzzy numbers and a consistency test method. First, IAHP uses trapezoidal fuzzy numbers to represent the relative importance between indicators, which can more accurately reflect the uncertainty and ambiguity of experts in the evaluation process and avoid the errors caused by rigid scoring. Second, a consistency test and correction method are proposed to ensure the consistency of the judgment matrix, reduce the influence of human factors on the evaluation results, and improve the scientificity and reliability of the evaluation. Through this improvement, the obtained weights are more reasonable, which can effectively support the subsequent decision-making process and ensure that the evaluation results are more in line with the actual situation.

[0111] 3) During the evaluation process, there is a certain correlation between many indicators, which may lead to information overlap and distort the evaluation results. Step 3 of the present invention proposes a weight optimization method based on indicator correlation. By establishing a direct influence matrix and a comprehensive influence matrix, the mutual influence degree between each indicator is accurately calculated, thereby eliminating the redundant information between indicators. In actual operation, by normalizing the direct influence matrix, the deviation caused by subjective judgment can be avoided, and the weights of each indicator are more reasonable. After the weight optimization, it can more truly reflect the importance of each indicator in the actual evaluation, thereby improving the accuracy and credibility of the evaluation results. Through this optimization method, it can be ensured that each evaluation indicator independently and coordinately reflects the comprehensive performance of the project, avoiding the problem of over-relying on a certain indicator or underestimating other key factors.

[0112] 4) Traditional comprehensive evaluation methods often can only evaluate the scheme from one aspect, ignoring the mutual influence between different attributes and multi-angle comparison. Step 4 of the present invention introduces a multi-attribute similarity consistency projection method, which comprehensively considers the closeness of the weighted values of each indicator to the ideal scheme. By introducing an ideal offset vector, this method overcomes the defect of inconsistent module similarity and cosine similarity in the traditional projection method during evaluation, making the evaluation process more comprehensive and detailed. It not only considers the similarity between indicators, but also incorporates factors such as the distance and direction of each indicator into the evaluation, thereby obtaining a more accurate comprehensive evaluation result. Through this method, a more reasonable scheme ranking can be achieved, helping decision-makers better identify the optimal scheme and improving the scientificity and practicality of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] The present invention will be further described below in conjunction with the drawings and examples;

[0114] Figure 1 It is a flowchart of the comprehensive evaluation method for the implementation plan of the power transmission and transformation project of the present invention.

[0115] Figure 2 This is the comprehensive evaluation index system diagram of the implementation plan for the power transmission and transformation project of the present invention. Specific implementation manners

[0116] Figure 1 This is the comprehensive evaluation method for the implementation plan of the power transmission and transformation project proposed by the present invention based on weight optimization and multi-attribute similarity. First, a comprehensive evaluation index system for the implementation plan of the power transmission and transformation project including 4 first-level indicators and 12 second-level indicators is constructed. Then, an improved analytic hierarchy process (IAHP) is proposed to obtain the index weights. Considering the information overlap problem between indicators, the weights are optimized using indicator correlation. Finally, a multi-attribute similarity consistency projection comprehensive evaluation method is proposed to realize the comprehensive evaluation of the implementation plan of the power transmission and transformation project. The method proposed by the present invention can effectively and reliably evaluate the implementation plan of the power transmission and transformation project, and has reference value for the comprehensive evaluation work of the implementation plan of the power transmission and transformation project.

[0117] Figure 2 This is the comprehensive evaluation index system diagram of the implementation plan of the power transmission and transformation project proposed by the present invention. Four first-level indicators including technical feasibility, economic feasibility, environmental impact, and social benefits and risk management are constructed. Among them, technical feasibility can be further divided into 3 second-level indicators: equipment performance index, system stability, and construction period; economic feasibility can be further divided into 4 second-level indicators: total investment, operating cost, return on investment, and unit power transmission cost; environmental impact can be further divided into 2 second-level indicators: carbon emissions and energy efficiency; social benefits and risk management can be further divided into 3 second-level indicators: new power supply capacity, project benefit period, and risk assessment index.

[0118] Table 1 Trapezoidal fuzzy number table

[0119]

[0120] Table 2 5-level scale method table

[0121]

[0122] Table 3 Comparative analysis table of evaluation results of different evaluation models

[0123]

[0124] Table 1 is the trapezoidal fuzzy number table proposed by the present invention. According to the trapezoidal fuzzy number table, a trapezoidal fuzzy number complementary judgment matrix is constructed accordingly.

[0125] Table 2 is the 5-level scale method table proposed by the present invention. According to the 5-level scale method table, a direct influence matrix is constructed accordingly.

[0126] Table 3 is a comparative analysis table of the evaluation results of different evaluation models proposed in the present invention. It can be seen from Table 3 that the comprehensive evaluation method for the implementation plan of the power transmission and transformation project based on weight optimization and multi-attribute similarity proposed in the present invention has the highest accuracy rate, reaching 86.2%. Compared with the entropy weight method, the fuzzy comprehensive evaluation method, and the grey correlation degree method, the evaluation accuracy rates are increased by 13.7%, 8%, and 15.9% respectively. This shows that the method proposed in the present invention effectively improves the accuracy rate of evaluating the implementation plan of the power transmission and transformation project.

Claims

1. A comprehensive evaluation method for power transmission and transformation project implementation plan based on weight optimization and multi-attribute similarity, characterized by The following steps are involved: Step 1: A comprehensive evaluation index system for the implementation plan of power transmission and transformation projects, which includes multiple primary indicators and multiple secondary indicators, was constructed; Step 2: An improved analytic hierarchy process is proposed to obtain the indicator weights; Step 3: Considering the information overlap between indicators, the indicator weights are optimized using indicator correlation; Step 4: Based on the multi-attribute similarity consistency projection comprehensive evaluation method, a comprehensive evaluation of the implementation plan of the power transmission and transformation project is achieved.

2. The comprehensive evaluation method for power transmission and transformation project implementation plan based on weight optimization and multi-attribute similarity according to claim 1 is characterized by: In the step 1, four first-level indicators are selected, namely: technical feasibility indicator, economic feasibility indicator, environmental impact indicator, and social benefit and risk management indicator; 1) Technical feasibility indicators: Technical feasibility indicators are used to evaluate whether the power transmission and transformation project is technically feasible and reasonable, including the following three secondary indicators: Equipment performance indicators: used to evaluate the technical maturity, performance stability and adaptability of the selected equipment; System stability index: Determine the stability and response capability of the power transmission and transformation engineering simulation system under different load conditions through simulation calculation; Construction cycle indicator: used to evaluate the construction time of each solution, quantified according to the milestone nodes of the project schedule; 2) Economic feasibility indicators: The economic feasibility index is determined by evaluating the investment, return and long-term economic benefits of the project funds; It includes the following four secondary indicators: Total investment indicators: including equipment procurement costs, construction costs, project management costs, and land use costs; accurately calculate the total investment of each plan through project budget; Operating cost indicators: used to evaluate the fixed and variable costs during the project operation, including daily equipment maintenance, repair costs, and power loss; Return on investment indicator: used to calculate the internal rate of return of a project and evaluate the economic benefits of long-term investment; the return on investment is calculated by comparing the expected income and costs during the project life cycle; Unit electricity transmission cost indicator: used to evaluate the cost of electricity transmission per kilowatt-hour, used to measure the economic benefits of different solutions; 3) Environmental impact indicators: Environmental impact indicators are used to assess the impact that a project may have on the natural environment during construction and operation, and include the following two secondary indicators: Carbon emissions indicator: used to assess the environmental impact of a project by simulating the amount of carbon dioxide emitted during the use of computing equipment; Energy efficiency index: used to evaluate the energy conversion efficiency of the power system and calculate the proportion of energy loss in the process of power transmission of equipment and systems; 4) Social benefits and risk management indicators: The social benefit and risk management indicators include the following three secondary indicators: New power supply capacity indicator: used to evaluate the maximum capacity increase of power supply after project implementation; Project benefit life indicator: used to evaluate the economic benefit cycle of the project; Risk assessment index indicator: Through a quantitative risk assessment model, the technical risks and financial risks during project implementation are assessed.

3. The comprehensive evaluation method for power transmission and transformation project implementation plan based on weight optimization and multi-attribute similarity according to claim 1 is characterized by: In step 2, the improved analytic hierarchy process is used to calculate the subjective weight of each indicator, including the following steps: Step 2.1: The 13 indicators y1, y2, ..., y n Represented as a set Y = {y i |i∈N}, where N={1,2,...,n}, N represents the indicator set, n represents the number of indicators, y i represents the i-th indicator; Based on constructing trapezoidal fuzzy number complementary judgment matrix in: Where: Relative to the index y j , index y i The importance of ij , b ij , c ij , d ij represents the trapezoidal membership, and a ij ≤ b ij ≤c ij ≤d ij ; Step 2.2: Invite k experts to participate in the evaluation, and the trapezoidal fuzzy number complementary judgment matrix of each expert is established as Represents the trapezoidal fuzzy number complementary judgment matrix; In order to check the consistency, the kernel and kernel operator are introduced to calculate the kernel matrix of the trapezoidal fuzzy number complementary judgment matrix; The kernel of is defined as follows: Where: Trapezoidal fuzzy number The core, and represents the trapezoidal membership given by the kth expert; Trapezoidal fuzzy number complementary judgment matrix The kernel matrix It is expressed as: Based on the kernel matrix Calculate the consistency index ρ: Where: Trapezoidal fuzzy number The core, Trapezoidal fuzzy number The core, Trapezoidal fuzzy number The kernel of , i represents the i-th index, j represents the j-th index, and f represents the f-th index; If ρ<ε, ε represents the critical value of the consistency index, then If the consistency criteria are met, proceed to step 2.4; otherwise, proceed to the next step; Step 2.3: Consistency correction: 1) First, calculate the kernel matrix The eigenvalue matrix of express The characteristic value of Where: Represents the ranking value of different rows; χ is a constant and χ≥(n-1) / 2; 2) Based on the kernel matrix Calculate its deviation matrix O: Among them: ij The deviation matrix representing the trapezoidal fuzzy number, (o ij ) n×n The deviation matrix representing the trapezoidal fuzzy number complementary judgment matrix; |o st | = max{|o ij |: i < j, i, j ∈ n}, s, t represent the indices of rows and columns; o st represents the maximum deviation amount, i represents the index of the row, j represents the index of the column; max{o ij |: i < j, i, j ∈ n} represents the maximum value of the absolute values of the elements in the deviation matrix of trapezoidal fuzzy numbers; 3) When o st ≠0, the fuzzy number in the trapezoidal fuzzy number complementary judgment matrix is ​​increased or decreased by an adjustment amount α Specifically: If o st <0, then represents the modified trapezoidal fuzzy number, if o st >0, then 4) Now calculate the consistency index ρ of step 2.

2. If the consistency requirement is not met, make corrections again; otherwise, generate output; Step 2.4: Weight calculation: Obtain a trapezoidal fuzzy number complementary judgment matrix that meets the consistency requirements Finally, the evaluation information of each expert is synthesized: in: The trapezoidal fuzzy number after integrating all experts, K represents K experts; Each indicator y i Fuzzy evaluation value of: Where: Indicates the index y i The expectation of the fuzzy evaluation value of The comprehensive value of the trapezoidal membership, a i , b i , c i and d i It represents the trapezoidal membership after integrating all experts. It represents the fuzzy number of all experts; Finally, the weight of each indicator ω is determined by normalization i :

4. The comprehensive evaluation method for power transmission and transformation project implementation plan based on weight optimization and multi-attribute similarity according to claim 3 is characterized by: In step 3, the indicator weights are optimized using the indicator correlation, including the following steps: Step 3.1: Establish the direct impact matrix Z = (z ij ): The five-level scaling method is used to determine the degree of influence between each indicator of the same level in the implementation plan of the power transmission and transformation project; among them, z ij It is the influence of the i-th indicator on the j-th indicator when the former is compared with the latter; Step 3.2: Calculate the comprehensive impact matrix T: First, the direct influence matrix Z = (z ij ) to normalize: M=Z / λ Where: λ represents the maximum value of the column of the direct influence matrix; M is the normalized direct influence matrix; Next, the comprehensive influence matrix T is calculated using the following formula: ij ), t ij Represents the influence between the i-th and j-th indicators; T=M(I-M) -1 Where: I is the unit matrix; Step 3.3: Weight optimization: First, based on the comprehensive influence matrix T = (t ij ), calculate the influence C i : Next, define the indicator independence g to represent the degree to which the indicator is not affected by other indicators: Where: min(C i ) represents the minimum influence, max(C i ) indicates the maximum influence; The weight optimization result after considering the index independence is W = (w i )for: Where: w i represents the weight of the i-th indicator; ω i is the index weight obtained by the improved hierarchical analysis method; β is the independence influencing factor, which represents the influence of independence on the index weight, β∈(0,+∞); The amount by which the indicator weight is adjusted.

5. The comprehensive evaluation method for power transmission and transformation project implementation plan based on weight optimization and multi-attribute similarity according to claim 4 is characterized in that: In step 4, the indicators in the comprehensive evaluation index system of the power transmission and transformation project implementation plan are divided into benefit-type, cost-type and fixed-type indicator sets, represented by η = {η1, η2, η3}, η1 represents the benefit-type indicator set, η2 represents the benefit-type indicator set, η3 represents the fixed-type indicator set, and the index data x of each indicator are calculated. i After collection, different normalization methods are used to normalize it to obtain normalized data y i : Where: m i represents the best stable value of the indicator data set η3; represents the minimum value of the i-th indicator, represents the minimum value of the i-th indicator, represents the minimum value between index i and the optimal stable value, It represents the minimum value between index i and the optimal stable value; Thus, the normalized index data vector is obtained as Y=(y i ), the ideal value of each indicator is The ideal indicator data vector is 6. The comprehensive evaluation method for power transmission and transformation project implementation plan based on weight optimization and multi-attribute similarity according to claim 5 is characterized by: Based on the optimized weight vector W, we get the weighted decision vector A(w1y1,w2y2,…,w m y m ) t , where: A represents the weighted decision vector, w1,w2,…,w m Represent the weight of each indicator, y1, y2, ..., y m Respectively represent the index quantity of each index, t represents the transpose of the matrix; the weighted ideal vector Represent the optimal value of each indicator respectively.

7. The comprehensive evaluation method for power transmission and transformation project implementation plan based on weight optimization and multi-attribute similarity according to claim 6 is characterized in that: Weighted decision vector A and weighted ideal vector A ★ The cosine of the angle r is: Where: θ represents A and A ★ The angle between A and A is the modulus of A. * Indicates A ★ The module length; Note the weighted decision vector A to the weighted ideal vector A ★ The distance vector d is: d=A * -A; Finally, the weighted decision vector A is calculated in the projection method on the weighted ideal vector A ★ The projection D (D = r·A) on is taken as the evaluation result, where D represents the evaluation result, r represents the cosine of the angle, A represents the weighted decision vector, and D() represents the weighted decision vector A on the weighted ideal vector A ＊ Projection on.

8. The comprehensive evaluation method for power transmission and transformation project implementation plan based on weight optimization and multi-attribute similarity according to claim 7 is characterized by: Introduce the ideal offset vector U: U=A+τd=(1-τ)A+τA * Where: τ is the preference coefficient, τ∈[0,+∞), and τ is related to the weighted ideal vector A * d represents the distance vector mentioned above; When β is close to 0, the weighted ideal vector A * Approaching the weighted decision vector A; when β = 1, U = A * Indicates A * No deviation, when β is far away from 1 and tends to +∞, it means A * Away from A; the ideal offset vector U can be used to adjust A through the parameter β * The modulus and direction of the decision vector A are adjusted to adjust the weights between the modulus similarity, distance similarity, cosine similarity and direction similarity.

9. The comprehensive evaluation method for power transmission and transformation project implementation plan based on weight optimization and multi-attribute similarity according to claim 8 is characterized by: Based on the ideal offset vector U, a comprehensive evaluation function H is proposed: Finally, the implementation plan of the power transmission and transformation project is evaluated according to the size of the H value. The larger the H, the better the construction plan.

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