Power distribution network equipment utilization rate evaluation method and device, equipment and storage medium

Abstract

The invention provides a power distribution network equipment utilization rate evaluation method and device, equipment and a storage medium, and belongs to the field of power distribution network optimization, and the method comprises the steps of evaluation hierarchical structure setting, judgment matrix construction, eigenvalue and weight solving, subjective and objective weight calculation and comprehensive evaluation. Through combination of an analytic hierarchy process and subjective and objective weights, detailed evaluation of the utilization rate of the power distribution network equipment is realized, and a theoretical basis is provided for optimization of the utilization rate.

Description

Technical Field

[0001] This application belongs to the field of power distribution network optimization, and in particular relates to a method, device, equipment and storage medium for evaluating the utilization rate of power distribution network equipment. Background Technology

[0002] Compared to transmission networks, distribution networks have lower voltage levels, more connected equipment and loads, and a more complex network topology. Establishing a distribution network equipment utilization evaluation system can comprehensively assess the equipment utilization level of a region, thereby helping to improve equipment utilization and playing a positive role in the economic operation of the power grid, safe and reliable power supply, and future development planning.

[0003] Due to the unique characteristics of pastoral areas, rural and pastoral power distribution networks do not have high requirements for power supply reliability. Problems mainly arise from aging power facilities and low equipment utilization. However, there is currently no technical method for detailed assessment of equipment utilization. Summary of the Invention

[0004] The purpose of this application is to overcome the problems existing in the prior art and to provide a method, apparatus, equipment and storage medium for evaluating the utilization rate of power distribution network equipment.

[0005] This application provides a method for evaluating the utilization rate of power distribution network equipment, including:

[0006] The evaluation hierarchy of power distribution network equipment utilization rate is set up, including evaluation objectives, intermediate indicators describing the evaluation objectives, and bottom-level indicators describing the intermediate indicators.

[0007] A judgment matrix is ​​constructed based on the underlying indicators, where each element of the judgment matrix represents a proportional scale of the importance of any two underlying indicators to the evaluation target.

[0008] Find the largest eigenvalue of the judgment matrix and determine the eigenvector corresponding to the largest eigenvalue as the index weight;

[0009] Based on the hierarchical structure, the indicator weights are converted into the subjective weights of the underlying indicators relative to the evaluation target;

[0010] Calculate the mean difference and variance of each underlying indicator value, and calculate the objective weight of each underlying indicator based on the mean difference and variance of the indicator values;

[0011] The subjective weight and the objective weight are added together by a preset coefficient to obtain the comprehensive weight;

[0012] The evaluation result of the evaluation target is calculated based on the index value of each underlying index and the comprehensive weight.

[0013] Optionally, the scaling factor includes:

[0014] Equally important is 1, slightly important is 3, relatively important is 5, very important is 7, extremely important is 9;

[0015] The median value between "equally important" and "slightly important" is 2, the median value between "slightly important" and "strongly important" is 2, the median value between "strongly important" and "intensely important" is 6, and the median value between "intensely important" and "extremely important" is 8.

[0016] Optionally, constructing a judgment matrix based on the underlying indicators further includes:

[0017] Perform a consistency check on the judgment matrix, including:

[0018] The consistency index is calculated using the following expression:

[0019]

[0020] The random consistency index is calculated based on the aforementioned consistency index, and the expression is as follows:

[0021]

[0022] The consistency test coefficient is calculated based on the random consistency index and the consistency index, as shown in the following expression:

[0023]

[0024] Where, λ max To determine the largest eigenvalue of a matrix, n is the order of the matrix, and b is the number of times the index is calculated in a single operation; if CR < 0.1, then the matrix is ​​determined to meet the consistency requirement.

[0025] Optionally, the mean deviation and variance of each underlying indicator are calculated, and the objective weight of each underlying indicator is calculated based on the mean deviation and variance of the indicator values, including:

[0026] The mean and variance of the underlying indicators are calculated using the following expressions:

[0027]

[0028] The weights of each underlying metric are calculated using the following expression:

[0029]

[0030] The subjective weight is calculated based on the aforementioned indicator weights, as shown in the following expression:

[0031] wi =v i / ∑v i

[0032] Where m represents the number of underlying metrics, and i represents the i-th underlying metric. S is the average difference of the values ​​of the i-th underlying indicator. i 2 is the variance of the index value of the i-th underlying index, a is an element in the judgment matrix, and j represents another underlying index.

[0033] Optionally, the subjective weight and the objective weight are added together by a preset coefficient to obtain a comprehensive weight, as shown in the following expression:

[0034] W i =αZ i +(1-α)X i

[0035] Among them, Z i and X i Let α = 0.5, where α represents the subjective weight and objective weight of the i-th evaluation indicator.

[0036] Optionally, the intermediate indicators include: the annual average load level of the equipment and the load level of the equipment at the moment of maximum load;

[0037] The underlying metrics include:

[0038] Under the annual average load level of equipment: average load rate of substation, average load rate of line, average load rate of distribution transformer, and load peak-valley difference rate;

[0039] Under the equipment load level at the maximum load time: the load rate of the substation at the maximum load time, the load rate of the line at the maximum load time, and the light load rate of the distribution transformer at the maximum load time.

[0040] Optionally, the power grid includes a distribution network.

[0041] This application also provides a device for evaluating the utilization rate of power distribution network equipment, comprising:

[0042] The hierarchical module is used to set the hierarchical structure for evaluating the utilization rate of distribution network equipment, including evaluation targets, intermediate indicators describing the evaluation targets, and bottom-level indicators describing the intermediate indicators.

[0043] The matrix module is used to construct a judgment matrix based on the underlying indicators, wherein each element in the judgment matrix represents a proportional scale of the importance of any two underlying indicators to the evaluation target.

[0044] The weighting module is used to solve for the maximum eigenvalue of the judgment matrix and determine the eigenvector corresponding to the maximum eigenvalue as the index weight.

[0045] The subjective module is used to convert the indicator weights into subjective weights of the underlying indicators relative to the evaluation target according to the hierarchical structure.

[0046] An objective module is used to calculate the mean difference and variance of each underlying indicator, and to calculate the objective weight of each underlying indicator based on the mean difference and variance of the indicator values.

[0047] The integration module is used to add the subjective weight and the objective weight with a preset coefficient to obtain the integrated weight;

[0048] The evaluation module is used to calculate the evaluation result of the evaluation target based on the index value of each of the underlying indicators and the comprehensive weight.

[0049] This application also provides a device for evaluating the utilization rate of power distribution network equipment, comprising:

[0050] A memory for storing the computer-executable program for the above-mentioned method for evaluating the utilization rate of power distribution network equipment;

[0051] The processor is configured to invoke the computer-executable program and execute the following: setting a hierarchical structure for evaluating the utilization rate of distribution network equipment, including evaluation targets, intermediate indicators describing the evaluation targets, and bottom-level indicators describing the intermediate indicators; constructing a judgment matrix based on the bottom-level indicators, where each element of the judgment matrix represents a proportional scale of the importance of any two bottom-level indicators to the evaluation targets; solving for the maximum eigenvalue of the judgment matrix and determining the eigenvector corresponding to the maximum eigenvalue as the indicator weight; converting the indicator weights into subjective weights of the bottom-level indicators relative to the evaluation targets according to the hierarchical structure; calculating the mean difference and variance of the indicator values ​​for each bottom-level indicator, and calculating the objective weight of each bottom-level indicator based on the mean difference and variance of the indicator values; adding the subjective weights and the objective weights with a preset coefficient to obtain a comprehensive weight; and calculating the evaluation result of the evaluation targets based on the indicator values ​​of each bottom-level indicator and the comprehensive weight.

[0052] This application also provides a storage medium, including a computer-executable program stored thereon, which is used by a processor to execute the steps of the above-described power distribution equipment utilization evaluation method.

[0053] The beneficial effects of this application are:

[0054] This application provides a method for evaluating the utilization rate of distribution network equipment, comprising: setting a hierarchical evaluation structure for distribution network equipment utilization, including an evaluation target, intermediate indicators describing the evaluation target, and bottom-level indicators describing the intermediate indicators; constructing a judgment matrix based on the bottom-level indicators, wherein each element in the judgment matrix represents a proportional scale of the importance of any two bottom-level indicators to the evaluation target; solving for the maximum eigenvalue of the judgment matrix and determining the eigenvector corresponding to the maximum eigenvalue as the indicator weight; converting the indicator weights into subjective weights of the bottom-level indicators relative to the evaluation target according to the hierarchical structure; calculating the mean difference and variance of the indicator values ​​for each bottom-level indicator, and calculating the objective weight of each bottom-level indicator based on the mean difference and variance of the indicator values; adding the subjective weights and the objective weights with a preset coefficient to obtain a comprehensive weight; and calculating the evaluation result of the evaluation target based on the indicator value of each bottom-level indicator and the comprehensive weight. This application achieves a detailed evaluation of the utilization rate of distribution network equipment based on hierarchical analysis and subjective and objective weights, providing a theoretical basis for optimizing the utilization rate of distribution network equipment. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the power distribution network equipment utilization evaluation process in this application;

[0056] Figure 2 This is a diagram illustrating the weight comparison in this application. Detailed Implementation

[0057] The present application will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it.

[0058] Please refer to Figure 1 As shown, a method for evaluating the utilization rate of power distribution network equipment includes the following steps:

[0059] S101 sets up a hierarchical structure for evaluating the utilization rate of distribution network equipment, including evaluation targets, intermediate indicators describing the evaluation targets, and bottom-level indicators describing the intermediate indicators.

[0060] Hierarchical structure modeling (AHP) is a structured approach that decomposes complex problems into a series of levels. Each level contains an evaluation of a different aspect or dimension of the problem. In this application, it is used to evaluate the utilization rate of distribution network equipment.

[0061] The evaluation objective is at the highest level (target level) in the hierarchical model, representing the ultimate goal or result that the decision-maker hopes to achieve. In this application, the target level is "distribution network equipment utilization rate," which can be represented by a capital letter (such as T) or a specific symbol (such as...). The target layer is represented by ), which is denoted as "A1" in this application.

[0062] The intermediate indicators refer to the indicators at the middle layer, which are macro-level evaluation indicators (first-level evaluation indicators). This middle layer includes a series of macro-level or higher-level evaluation indicators directly related to the target layer. These indicators are key factors that decision-makers believe have a significant impact on achieving the target.

[0063] In this case, the intermediate layer includes five primary evaluation indicators, such as "power supply reliability" and "network structure." These indicators may be selected based on experience, expert opinions, or historical data, and are used to comprehensively evaluate various aspects of the utilization rate of distribution network equipment. Each primary evaluation indicator is usually represented by a capital letter (such as A, B, C...) or a specific symbol. In this application, they are referred to as "B1," "B2," etc., respectively.

[0064] The bottom-level indicators refer to the lowest layer, which are the specific evaluation indicators. The lowest layer contains a series of more specific and detailed evaluation indicators used to quantify or describe the macro-level indicators in the intermediate layer. These indicators are typically obtained through measurement, calculation, or observation. In this application, they are designated as C1, C2, ..., C7, respectively.

[0065] Specifically, Table 1 contains detailed metrics for the target layer, intermediate layer, and bottom layer as described in this application.

[0066]

[0067] Table 1

[0068] In a hierarchical model, each level has a clear relationship with the levels above and below it. Specifically:

[0069] The target layer is the core and ultimate goal of the model; all other layers are set up to achieve this goal.

[0070] The intermediate layers provide a macro framework or key dimensions for achieving the goals, breaking down the goal layer into more specific and actionable components.

[0071] The lowest layer provides specific data or information support for the middle layer. By measuring and quantifying these specific indicators, the status and level of the macro indicators of the middle layer can be evaluated.

[0072] S102 constructs a judgment matrix based on the underlying indicators, where each element in the judgment matrix represents a proportional scale of the importance of any two underlying indicators to the evaluation target.

[0073] In the Analytic Hierarchy Process (AHP), constructing the decision matrix is ​​a crucial step in assessing the relative importance of different indicators or options. The decision matrix represents the decision-maker's judgment on the relative importance of various indicators.

[0074] Construct a judgment matrix A, which is an n×n matrix where n is the number of evaluation indicators. The elements a of matrix A are... ij This indicates the importance of the i-th indicator relative to the j-th indicator to the target layer. These importance values ​​are obtained using a 1-9 scale (also known as the Saaty scale), as shown in Table 2.

[0075] Scale value Relative importance description 1 Both are equally important 3 The former is slightly more important than the latter. 5 The former is clearly more important than the latter. 7 The former is significantly more important than the latter. 9 The former is extremely important than the latter. 2,4,6,8 The median of the above adjacent scales reciprocal <![CDATA[If a ij = m, then a ij = 1 / m]]>

[0076] Table 2

[0077] In the scaling method, if the importance of the i-th indicator relative to the j-th indicator is m, then the importance of the j-th indicator relative to the i-th indicator is 1 / m. This ensures the reciprocity of the judgment matrix.

[0078] For example, as shown in Table 3:

[0079] Table 3 shows the judgment relationship of intermediate layer indicators in this application.

[0080] index <![CDATA[B1]]> <![CDATA[B2]]> <![CDATA[B1]]> 1 2 <![CDATA[B2]]> 1 / 2 1

[0081] Table 3

[0082] For example, as shown in Table 4:

[0083] Table 4 shows the relative importance of micro-indicators over time periods in the underlying indicators of this application.

[0084] index <![CDATA[C1]]> <![CDATA[C2]]> <![CDATA[C3]]> <![CDATA[C4]]> <![CDATA[C1]]> 1 1 / 2 1 / 3 3 <![CDATA[C2]]> 2 1 1 / 3 3 <![CDATA[C3]]> 3 3 1 3 <![CDATA[C4]]> 1 / 3 1 / 3 1 / 3 1

[0085] Table 4

[0086] For example, as shown in Table 5:

[0087] Table 5 shows the relative importance of time-crossing micro-indicators of the underlying indicators in this application.

[0088] index <![CDATA[C5]]> <![CDATA[C6]]> <![CDATA[C7]]> <![CDATA[C5]]> 1 1 / 3 1 / 5 <![CDATA[C6]]> 3 1 1 / 3 <![CDATA[C7]]> 2 3 1

[0089] Table 5

[0090] S103 solves for the maximum eigenvalue of the judgment matrix and determines the eigenvector corresponding to the maximum eigenvalue as the index weight.

[0091] Eigenvalues ​​are a concept in linear algebra used to describe the scaling factors of certain vectors during matrix transformations. In AHP, this application is interested in determining the largest eigenvalue λ of matrix A. max This is because it relates to the consistency of the matrix.

[0092] Calculate the largest eigenvalue λ max Numerical methods, such as the power method and the Jacobi iteration method, are typically used. In practical applications, many software tools (such as Excel add-ins and MATLAB) can also directly calculate the eigenvalues ​​of matrices.

[0093] An eigenvector is a vector corresponding to an eigenvalue, representing a vector whose direction remains unchanged under matrix transformations at that eigenvalue. In AHP, the largest eigenvalue is λ. max The corresponding feature vector w is the weight vector of each indicator, which is referred to as indicator weight in this application.

[0094] Similarly, eigenvectors are typically obtained using numerical methods or software tools. The resulting eigenvector w needs to be normalized so that the sum of all its elements is 1, thus yielding the final weight vector.

[0095] Each element w in the weight vector w i This indicates the importance of the i-th indicator in the entire evaluation system. The larger the weight, the greater the influence of that indicator on the final evaluation result.

[0096] For example, referring to Tables 3, 4, and 5, the judgment matrix and indicator weights are expressed as follows:

[0097]

[0098] W0 = [0.6667 0.3333] -1

[0099]

[0100] W1=[0.17610.24720.48270.0939] -1

[0101]

[0102] W2 = [0.1047 0.2583 0.6370] -1

[0103] Furthermore, hierarchical single ranking refers to ranking indicators based on their calculated weight values. Indicators with higher weight values ​​are more important in the evaluation system and thus rank higher. Hierarchical single ranking is an important step in AHP (Analysis of Hierarchical Powers), helping decision-makers intuitively understand the relative importance of each indicator.

[0104] Furthermore, in AHP, a consistency check is required to verify the rationality of the weight values ​​obtained through the judgment matrix. The purpose of the consistency check is to ensure that the judgment matrix is ​​logically consistent, i.e., there are no obvious contradictions or errors.

[0105] The consistency index (CI) is used to measure the degree of inconsistency in the judgment matrix. The formula for calculating CI is:

[0106]

[0107] Where, λ max CI is the largest eigenvalue of the judgment matrix, and n is the order of the matrix (i.e., the number of indices). When CI equals 0, the judgment matrix is ​​a consistent matrix. As the value of CI increases, the inconsistency of the judgment matrix also increases.

[0108] Since random factors can also cause inconsistencies in the judgment matrix, a random consistency index (RI) is needed for correction. RI is the average consistency index obtained by simulating a large number of random judgment matrices.

[0109]

[0110] In fact, the correspondence between RI and the matrix order n is fixed, as shown in Table 6:

[0111]

[0112]

[0113] Table 6

[0114] To comprehensively consider the effects of CI and RI, a test coefficient CR is introduced. The formula for calculating CR is:

[0115]

[0116] When CR is less than 0.1, the judgment matrix meets the consistency requirement and the weight values ​​are set reasonably; otherwise, the judgment matrix needs to be modified or reconstructed.

[0117] S104 converts the indicator weights into subjective weights of the underlying indicators relative to the evaluation target based on the hierarchical structure.

[0118] Starting from the lowest level, the weight of each indicator is multiplied by the weight of the indicator at the next higher level to obtain the weight of that indicator relative to the overall goal. This process is repeated layer by layer upwards until the weights of all lowest-level indicators relative to the overall goal are calculated.

[0119] This step yields the subjective weights of each indicator relative to the overall goal. These weights reflect the experts' or decision-makers' assessment of the importance of different indicators in achieving the overall goal.

[0120] S105 calculates the mean difference and variance of each underlying indicator value, and calculates the objective weight of each underlying indicator based on the mean difference and variance of the indicator values.

[0121] The mean is the average of all values. For the i-th indicator, its mean is... It is the average of the actual values ​​of all samples on this indicator.

[0122] The mathematical formula is:

[0123]

[0124] Variance is the average of the squared differences between each value and the mean, used to measure the degree of dispersion of values. For the i-th indicator, its variance S i 2 It is the average of the squared differences between the actual values ​​and the mean of all samples for this indicator.

[0125] The mathematical formula is:

[0126]

[0127] Where m is the number of samples, a ij is the true value of the indicator, and j represents the number of multiple values ​​of the indicator.

[0128] Calculate the weight of each indicator.

[0129]

[0130] The final subjective weighting is:

[0131] w i =v i / ∑v i

[0132] S106 The subjective weight and the objective weight are added together with a preset coefficient to obtain a comprehensive weight.

[0133] In the linear weighted model, each evaluation index has a subjective weight and an objective weight, as shown in Table 7:

[0134]

[0135] Table 7

[0136] Subjective weights are typically determined based on expert judgment, experience, or other subjective factors, while objective weights are calculated based on actual data, statistical results, or some objective standard. To obtain a comprehensive weight, these two weight values ​​are combined.

[0137] Suppose there are n indicators. For the i-th evaluation indicator (where i ranges from 1 to n), its subjective weight is Z. i The objective weight is X i Overall weight w i Calculated using the following formula:

[0138] W i =αZ i +(1-α)X i

[0139] In this formula, α is a weighting coefficient used to adjust the relative importance of subjective and objective weights in the overall weight. α is set to 0.5, meaning that subjective and objective weights have equal weight in the overall weight calculation.

[0140] S107 calculates the evaluation result of the evaluation target based on the index value of each of the underlying indicators and the comprehensive weight.

[0141] Weighted summation is the most common method for calculating evaluation results. It multiplies the standardized score of each underlying indicator by its overall weight, and then sums all the products to obtain the total score.

[0142] The calculation method for the index values ​​is shown in Table 8:

[0143]

[0144]

[0145] Table 8 According to Table 8, the correspondence between indicator values ​​and scores is shown in Table 9:

[0146]

[0147] Table 9

[0148] This application is based on the above method, and adds an entropy weight method to the calculation method of objective weight for comparison in an experiment.

[0149] Experimental data:

[0150] This model has been applied to the 10kV Gangyi Road distribution network in rural and pastoral areas of Qinghai Province. The evaluation model based on the combined weighting of the analytic hierarchy process and the coefficient of variation method can more scientifically and reasonably evaluate the utilization rate of equipment in rural and pastoral distribution networks.

[0151] Table 10. Values ​​and scores of each indicator for the evaluation subjects.

[0152]

[0153] Table 11 Evaluation Indicators for the Utilization Rate of Distribution Network Equipment in Rural and Pastoral Areas: Subjective and Objective Weights

[0154]

[0155]

[0156] Table 12 Overall Score

[0157] area Overall score Gangyi Road 47.1170

[0158] Please refer to Figure 2 As shown, for the substation average load rate indicator, the objective weight, subjective weight, and comprehensive weight are not significantly different. For the three indicators of line average load rate, substation maximum load time load rate, and line maximum load time load rate, the objective weight calculated by the coefficient of variation method is higher than the subjective weight. This is because the differences among the six evaluation objects in these indicators are relatively large, and the coefficient of variation method is more sensitive to the differences in indicator data, thus determining a larger weight.

[0159] The subjective weights determined by the analytic hierarchy process (AHP) for the three indicators of average load rate of distribution transformers, peak-valley load difference rate, and light load rate at the moment of maximum load of distribution transformers are too large. This is because the importance of each indicator in the AHP is obtained based on human experience, which is too subjective.

[0160] In general, the objective weights determined by the coefficient of variation method are more discriminative than those determined by the entropy weight method. The analytic hierarchy process has the problem of being too subjective, while the comprehensive weighting avoids these problems and sets the weights of the indicators more reasonably.

[0161] This application also provides a device for evaluating the utilization rate of power distribution network equipment, comprising:

[0162] The hierarchical module is used to set the hierarchical structure for evaluating the utilization rate of distribution network equipment, including evaluation targets, intermediate indicators describing the evaluation targets, and bottom-level indicators describing the intermediate indicators.

[0163] The matrix module is used to construct a judgment matrix based on the underlying indicators, wherein each element in the judgment matrix represents a proportional scale of the importance of any two underlying indicators to the evaluation target.

[0164] The weighting module is used to solve for the maximum eigenvalue of the judgment matrix and determine the eigenvector corresponding to the maximum eigenvalue as the index weight.

[0165] The subjective module is used to convert the indicator weights into subjective weights of the underlying indicators relative to the evaluation target according to the hierarchical structure.

[0166] An objective module is used to calculate the mean difference and variance of each underlying indicator, and to calculate the objective weight of each underlying indicator based on the mean difference and variance of the indicator values.

[0167] The integration module is used to add the subjective weight and the objective weight with a preset coefficient to obtain the integrated weight;

[0168] The evaluation module is used to calculate the evaluation result of the evaluation target based on the index value of each of the underlying indicators and the comprehensive weight.

[0169] This application also provides a device for evaluating the utilization rate of power distribution network equipment, comprising:

[0170] A memory for storing the computer-executable program for the above-mentioned method for evaluating the utilization rate of power distribution network equipment;

[0171] The processor is configured to invoke the computer-executable program and execute the following: setting a hierarchical structure for evaluating the utilization rate of distribution network equipment, including evaluation targets, intermediate indicators describing the evaluation targets, and bottom-level indicators describing the intermediate indicators; constructing a judgment matrix based on the bottom-level indicators, where each element of the judgment matrix represents a proportional scale of the importance of any two bottom-level indicators to the evaluation targets; solving for the maximum eigenvalue of the judgment matrix and determining the eigenvector corresponding to the maximum eigenvalue as the indicator weight; converting the indicator weights into subjective weights of the bottom-level indicators relative to the evaluation targets according to the hierarchical structure; calculating the mean difference and variance of the indicator values ​​for each bottom-level indicator, and calculating the objective weight of each bottom-level indicator based on the mean difference and variance of the indicator values; adding the subjective weights and the objective weights with a preset coefficient to obtain a comprehensive weight; and calculating the evaluation result of the evaluation targets based on the indicator values ​​of each bottom-level indicator and the comprehensive weight.

[0172] This application also provides a storage medium, including a computer-executable program stored thereon, which is used by a processor to execute the steps of the above-described power distribution equipment utilization evaluation method.

Claims

1. A method for evaluating the utilization rate of power distribution network equipment, characterized in that, include: The evaluation hierarchy of power distribution network equipment utilization rate is set up, including evaluation objectives, intermediate indicators describing the evaluation objectives, and bottom-level indicators describing the intermediate indicators. A judgment matrix is ​​constructed based on the underlying indicators, where each element of the judgment matrix represents a proportional scale of the importance of any two underlying indicators to the evaluation target. Find the largest eigenvalue of the judgment matrix, and determine the eigenvector corresponding to the largest eigenvalue as the index weight; Based on the hierarchical structure, the indicator weights are converted into the subjective weights of the underlying indicators relative to the evaluation target; Calculate the mean difference and variance of each underlying indicator value, and calculate the objective weight of each underlying indicator based on the mean difference and variance of the indicator values; The subjective weight and the objective weight are added together by a preset coefficient to obtain the comprehensive weight; The evaluation result of the evaluation target is calculated based on the index value of each underlying index and the comprehensive weight.

2. The method for evaluating the utilization rate of power distribution network equipment according to claim 1, characterized in that, The scaling factor includes: Equally important is 1, slightly important is 3, relatively important is 5, very important is 7, extremely important is 9; The median value between equal importance and slightly important is 2, the median value between slightly important and more important is 2, the median value between more important and strongly important is 6, and the median value between strongly important and extremely important is 8.

3. The method for evaluating the utilization rate of power distribution network equipment according to claim 1, characterized in that, Constructing a judgment matrix based on the underlying indicators also includes: Perform a consistency check on the judgment matrix, including: The consistency index is calculated using the following expression: The random consistency index is calculated based on the aforementioned consistency index, and the expression is as follows: The consistency test coefficient is calculated based on the random consistency index and the consistency index, as shown in the following expression: Where, λ max To determine the largest eigenvalue of a matrix, n is the order of the matrix; if CR < 0.1, then the matrix is ​​determined to meet the consistency requirement.

4. The method for evaluating the utilization rate of power distribution network equipment according to claim 1, characterized in that, Calculate the mean deviation and variance of each underlying indicator value, and calculate the objective weight of each underlying indicator based on the mean deviation and variance of the indicator values, including: The mean and variance of the underlying indicators are calculated using the following expressions: The weights of each underlying metric are calculated using the following expression: The subjective weight is calculated based on the aforementioned indicator weights, as shown in the following expression: w i =v i / ∑v i Where m represents the number of underlying metrics, and i represents the i-th underlying metric. S is the average difference of the values ​​of the i-th underlying indicator. i 2 is the variance of the index value of the i-th underlying index, a is an element in the judgment matrix, and j represents another underlying index.

5. The method for evaluating the utilization rate of power distribution network equipment according to claim 1, characterized in that, The subjective weight and the objective weight are added together by a preset coefficient to obtain the comprehensive weight, as shown in the following expression: W i =αZ i +(1-a)X i Among them, Z i and X i Let α = 0.5, where α represents the subjective weight and objective weight of the i-th evaluation indicator.

6. The method for evaluating the utilization rate of power distribution network equipment according to claim 1, characterized in that, The intermediate indicators include: the annual average load level of the equipment and the load level of the equipment at the moment of maximum load; The underlying metrics include: Under the annual average load level of equipment: average load rate of substation, average load rate of line, average load rate of distribution transformer, and load peak-valley difference rate; Under the equipment load level at the maximum load time: the load rate of the substation at the maximum load time, the load rate of the line at the maximum load time, and the light load rate of the distribution transformer at the maximum load time.

7. A device for evaluating the utilization rate of power distribution network equipment, characterized in that, include: The hierarchical module is used to set the hierarchical structure for evaluating the utilization rate of distribution network equipment, including evaluation targets, intermediate indicators describing the evaluation targets, and bottom-level indicators describing the intermediate indicators. The matrix module is used to construct a judgment matrix based on the underlying indicators, wherein each element in the judgment matrix represents a proportional scale of the importance of any two underlying indicators to the evaluation target. The weighting module is used to solve for the maximum eigenvalue of the judgment matrix and determine the eigenvector corresponding to the maximum eigenvalue as the index weight. The subjective module is used to convert the indicator weights into subjective weights of the underlying indicators relative to the evaluation target according to the hierarchical structure. An objective module is used to calculate the mean difference and variance of each underlying indicator, and to calculate the objective weight of each underlying indicator based on the mean difference and variance of the indicator values. The integration module is used to add the subjective weight and the objective weight with a preset coefficient to obtain the integrated weight; The evaluation module is used to calculate the evaluation result of the evaluation target based on the index value of each of the underlying indicators and the comprehensive weight.

8. A device for evaluating the utilization rate of power distribution network equipment, characterized in that, include: A memory for storing a computer-executable program of the power distribution equipment utilization evaluation method according to any one of claims 1 to 7; The processor is configured to invoke the computer-executable program and execute the following: setting a hierarchical structure for evaluating the utilization rate of distribution network equipment, including evaluation targets, intermediate indicators describing the evaluation targets, and bottom-level indicators describing the intermediate indicators; constructing a judgment matrix based on the bottom-level indicators, where each element in the judgment matrix represents a proportional scale of the importance of any two bottom-level indicators to the evaluation targets; solving for the maximum eigenvalue of the judgment matrix and determining the eigenvector corresponding to the maximum eigenvalue as the indicator weight; converting the indicator weights into subjective weights of the bottom-level indicators relative to the evaluation targets according to the hierarchical structure; calculating the mean difference and variance of the indicator values ​​for each bottom-level indicator, and calculating the objective weight of each bottom-level indicator based on the mean difference and variance of the indicator values. The subjective weight and the objective weight are added together by a preset coefficient to obtain the comprehensive weight; The evaluation result of the evaluation target is calculated based on the index value of each underlying index and the comprehensive weight.

9. A storage medium, characterized in that, It includes a computer-executable program stored thereon, which is used by a processor to execute the steps of the power distribution equipment utilization evaluation method according to any one of claims 1 to 7.

Images