Line loss level evaluation method considering grid structure of power grid and computer medium

Abstract

The invention discloses a line loss level evaluation method considering a grid structure of a power grid. The line loss level evaluation method comprises the steps of S1, performing grid structure analysis according to a first data set to obtain a first index set; the first data set comprises a power grid structure under a preset number of voltage levels; s2, performing index classification according to the first index set to obtain a second index set and a third index set; the second index set comprises indexes having forward influence on the line loss; the third index set comprises indexes having negative influence on the line loss; s3, performing index weight calculation according to the second index set and the third index set to obtain a subjective weight and an objective weight of each index; fusing the subjective weight and the objective weight to obtain a comprehensive weight of each index; s4, calculating according to the comprehensive weight of each index in combination with the improved matter element extension model to obtain an evaluation result; and S2, judging whether the line loss level in the region meets the expectation or not according to an evaluation result, if so, completing evaluation, and if not, dynamically adjusting equipment in the region and then entering S2.

Description

Technical Field

[0001] The present invention relates to the field of power grid operation line loss level assessment, and in particular to a line loss level assessment method and a computer medium that considers a power grid grid structure. Background Art

[0002] With the continuous expansion of the scale of power systems and the advancement of technology, grid line loss has become one of the bottlenecks restricting the improvement of power system efficiency. Grid line loss is the energy loss caused by the heat generated during the transmission of current in the transmission line. Grid line loss is directly related to the economic and efficient use of electric energy and the sustainable development of energy. Reducing grid line loss is an important way to improve the utilization rate of electric energy and reduce operating costs.

[0003] In terms of line loss assessment, Huang Liandong (CN116937558A) proposed a method for calculating and evaluating line losses in distribution networks containing distributed power sources, and used the line loss change amount and line loss change rate to effectively evaluate the line losses of distribution networks under grid interaction. Fan Jianing (CN116882982A) used information such as transmission line structure, user-side analysis, and line loss anomaly assessment to reduce the gap between theoretical line losses and actual line losses, making the analysis more comprehensive. Bai Tai (CN116821739A) used a large amount of data from the low-voltage side distribution network, used the K-means clustering method to cluster the line loss data, and used a convolutional neural network to evaluate the line loss of the low-voltage side distribution network. Yu Jun (CN116307844A) used the low-voltage side data set for clustering training to obtain a line loss assessment model to propose abnormal data. Peng Yu (CN116050921A) reasonably evaluated the line loss data of other substations through the constraints of the benchmark substation. However, the existing line loss level assessment has not yet considered the classification of line loss levels, making it difficult to conduct a detailed assessment of line loss levels and lacking scientific and effective assessment results.

[0004] Therefore, a new technical solution is urgently needed to solve the technical problem of how to divide the line loss levels and thus achieve detailed evaluation of the line loss levels. Summary of the invention

[0005] The present invention provides a line loss level assessment method and a computer medium taking into account the grid structure of a power grid, so as to solve the technical problem of how to classify the line loss level into grades and thus realize detailed assessment of the line loss level.

[0006] To achieve the above object, the present invention provides a method for evaluating line loss level considering the grid structure of a power grid, comprising the following steps:

[0007] S1. Performing grid structure analysis according to a first data set to obtain a first indicator set; the first data set includes grid structures of a power grid under a preset number of voltage levels;

[0008] S2. Classifying the indicators according to the first indicator set to obtain a second indicator set and a third indicator set; the second indicator set includes indicators that have a positive impact on line loss; the third indicator set includes indicators that have a negative impact on line loss;

[0009] S3. Calculate the indicator weights according to the second indicator set and the third indicator set to obtain the subjective weight and objective weight of each indicator; integrate the subjective weight and objective weight of each indicator to obtain the comprehensive weight of each indicator;

[0010] S4. The evaluation result is calculated based on the comprehensive weight of each indicator combined with the improved physical-element extension model; based on the evaluation result, it is determined whether the line loss level in the area meets the expectation. If it does, the evaluation is completed; if not, the equipment in the area is dynamically adjusted and then enter S2.

[0011] Preferably, the grid structure analysis is performed according to the first data set to obtain the first indicator set including:

[0012] According to the equipment operation characteristics under the preset number of voltage levels, considering the multiple influencing factors and multi-party coordination relationship of technical level, design and manufacturing level and investment benefits, the grid structure analysis is carried out to obtain the first indicator set, including:

[0013] First evaluation level: the line loss level in the area is the target layer;

[0014] The second evaluation level: equipment physical characteristic indicators, grid structure characteristic indicators and grid operation characteristic indicators;

[0015] The third evaluation level: the physical characteristic indicators of the equipment include the transformer equipment status indicator and reactive power compensation configuration indicator; the grid structure characteristic indicators include the average power factor indicator, load time distribution indicator and load space distribution indicator; the grid operation characteristic indicators include the average line length indicator and power supply distribution indicator.

[0016] Preferably, S2 comprises:

[0017] Analyze the positive and negative effects of each indicator on the line loss level according to the first indicator set, and obtain a second indicator set including positive indicators and a third indicator set including negative indicators;

[0018] Positive indicators include:

[0019]

[0020] Among them, a ij and c ij They represent the positive normalized index value and the original index value of the i-th regional power grid index j respectively;

[0021] Negative indicators include:

[0022]

[0023] Among them, b ij and c ij They respectively represent the negative standardized index value and the original index value of the i-th regional power grid index j.

[0024] Preferably, calculating the indicator weights according to the second indicator set and the third indicator set includes:

[0025] Calculate the subjective weight of the indicator:

[0026] Using triangular fuzzy number d mn =(l mn ,e mn ,p mn ) indicates the relative importance of different indicators, l mn Indicates the lower limit of importance between different indicators, e mn Indicates the maximum possible value of the importance between different indicators, p mn Indicates the upper limit of importance between different indicators, including:

[0027] C1. Compare the importance of different indicators to obtain the triangular fuzzy number d between each indicator. mn =(l mn ,e mn ,p mn );

[0028] C2, according to the triangular fuzzy number d mn =(l mn ,e mn ,p mn ) Construct the fuzzy matrix E 0 =(e mn );

[0029] C3. Calculate the fuzzy judgment matrix E:

[0030]

[0031] Among them, s mn =(p mn -l mn ) / (2e mn );

[0032] C4. Get the adjustment judgment matrix A, A=E 0 ×E, transform A into a judgment matrix A with a diagonal of 1 0 ;

[0033] C5. Calculate the weight r of index j using the square root method 0j :

[0034]

[0035] Among them, δ mn is the judgment matrix A 0 the various elements of

[0036] r 0j After normalization, the subjective weight w is obtained.

[0037] Preferably, calculating the indicator weights according to the second indicator set and the third indicator set further includes:

[0038] Calculate the objective weight of the indicator:

[0039] B1. Determine the evaluation indicators:

[0040] Experts are hired to make empirical judgments on weights. Assuming there are x evaluation indicators, y experts make empirical judgments on the weights of each indicator at the same time, and the empirical judgment matrix A of the weights of each indicator is obtained:

[0041] A=[A 1 ,A 2 ,A 3 ,…,A x ] T

[0042]

[0043] B2. Determine the reference sequence A 0 :

[0044] A maximum weight value is selected from the experience judgment matrix A as the public reference weight value, and the reference weight values ​​of each expert are assigned to this value:

[0045] A 0 =(a 01 ,a 02 ,…,a 0y )

[0046] B3. Calculate each indicator sequence A 1 , A 2 , …, A x With A 0 The distance D 0i :

[0047]

[0048] Among them, a 0k represents the kth reference value, a ik represents the kth reference value of the ith indicator;

[0049] B4. Obtain the weight of each indicator and normalize each indicator:

[0050]

[0051] Among them, r′ represents the unnormalized weight of the i-th indicator; r represents the objective weight.

[0052] Preferably, the subjective weight and objective weight of each indicator are integrated to obtain the comprehensive weight of each indicator including:

[0053] The subjective weight and objective weight are integrated through the principles of game theory:

[0054] With the goal of minimizing the deviation between the two weights, the subjective weight and the objective weight are optimized, including:

[0055] min(||θ-w|| 2 +||θ-r|| 2 )

[0056] Among them, θ represents the comprehensive weight; w represents the subjective weight; r represents the objective weight;

[0057] The objective is transformed into a system of linear equations using matrix differential properties:

[0058]

[0059] Where T represents the transposition operation; a 1 and a 2 represents the coefficient;

[0060] Normalize the linear equations to get the comprehensive weight:

[0061] θ=b 1 w T +b 2 r T

[0062] in,

[0063] Preferably, the evaluation results calculated based on the comprehensive weight of each indicator combined with the improved matter-element extension model include:

[0064] The improved matter-element extension model includes:

[0065] The classical matter-element analysis method is improved so that both ends of the general classical domain value are divided by the node domain V L The maximum value b on the right Lp , and the improved matter-element extension model is obtained:

[0066]

[0067] Where: N iis the evaluation level of line loss level; C L is the evaluation index; R N V is the matter-element of the object to be evaluated; L represents the comprehensive weight θ of the Lth evaluation index; b Lp Indicates the maximum value of the indicator at the same level;

[0068] Then the distance D of the domain range can be calculated ij ,Right now:

[0069]

[0070] where a ij Indicates the ratio of the minimum value of each indicator to the maximum value of the same level indicator; b ij It indicates the ratio of the maximum value of each indicator to the maximum value of the indicator at the same level; the value range of each indicator is obtained by the expert scoring method;

[0071] Then the comprehensive correlation K can be calculated j (N 0 ) is calculated as

[0072]

[0073] Among them, N 0 is the object to be evaluated; θ represents the comprehensive weight;

[0074]

[0075] in, It indicates the minimum value of the comprehensive correlation of each indicator in the second evaluation level and the third evaluation level; Indicates the maximum value of the comprehensive correlation of each indicator in the second evaluation level and the third evaluation level; K j * (N 0 ) represents the maximum closeness; j * It represents the characteristic value of the evaluation grade variable, which is used to judge the degree to which things tend to be evaluated.

[0076] The present invention also provides a computer medium, comprising a processor, a memory and a computer program for implementing the method of the present invention.

[0077] The present invention has the following beneficial effects:

[0078] The line loss level evaluation method considering the grid structure of the present invention constructs an evaluation system including the dimensions of equipment physical characteristics, grid structure characteristics, and grid operation characteristics, which comprehensively reflects the level of grid line loss. The improved hierarchical analysis method is used to obtain the comprehensive weight of indicators, and the weight is integrated using the idea of ​​game theory to make the weight result more accurate. The evaluation result is obtained by calculating the improved matter-element extension model, which is helpful for accurately evaluating the line loss level. This method has good practical significance.

[0079] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0081] Figure 1 It is a flow chart of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0082] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0083] See also Figure 1 In a preferred embodiment of the present invention, a method for evaluating line loss level considering the grid structure of a power grid is provided, comprising the following steps:

[0084] S1. Performing grid structure analysis according to a first data set to obtain a first indicator set; the first data set includes grid structures of a power grid under a preset number of voltage levels;

[0085] The grid structure analysis is performed based on the first data set, and the first indicator set is obtained, including:

[0086] According to the equipment operation characteristics under the preset number of voltage levels, considering the multiple influencing factors and multi-party coordination relationship of technical level, design and manufacturing level and investment benefits, the grid structure analysis is carried out to obtain the first indicator set, including:

[0087] First evaluation level: the line loss level in the area is the target layer;

[0088] The second evaluation level: equipment physical characteristic indicators, grid structure characteristic indicators and grid operation characteristic indicators;

[0089] The third evaluation level: the physical characteristic indicators of the equipment include the transformer equipment status indicator and reactive power compensation configuration indicator; the grid structure characteristic indicators include the average power factor indicator, load time distribution indicator and load space distribution indicator; the grid operation characteristic indicators include the average line length indicator and power supply distribution indicator.

[0090] S2. Classifying the indicators according to the first indicator set to obtain a second indicator set and a third indicator set; the second indicator set includes indicators that have a positive impact on line loss; the third indicator set includes indicators that have a negative impact on line loss;

[0091] S2 includes:

[0092] Analyze the positive and negative effects of each indicator on the line loss level according to the first indicator set, and obtain a second indicator set including positive indicators and a third indicator set including negative indicators;

[0093] Positive indicators include:

[0094]

[0095] Among them, a ij and c ij They represent the positive normalized index value and the original index value of the i-th regional power grid index j respectively;

[0096] Negative indicators include:

[0097]

[0098] Among them, b ij and c ij They respectively represent the negative standardized index value and the original index value of the i-th regional power grid index j.

[0099] In the preferred embodiment of the present invention, the power distribution area should be divided according to the geographical scope or administrative area in principle, with clear boundaries, to avoid duplication of construction, cross power supply, universal service and guaranteed power supply service can not be implemented, etc. The concept of area can be diluted, such as selecting an area around the school, which can include 10kv distribution network, 35kv distribution network and 0.4kv distribution network.

[0100] S3. Calculate the indicator weights according to the second indicator set and the third indicator set to obtain the subjective weight and objective weight of each indicator; integrate the subjective weight and objective weight of each indicator to obtain the comprehensive weight of each indicator;

[0101] The indicator weight calculation based on the second indicator set and the third indicator set includes:

[0102] Calculate the subjective weight of the indicator:

[0103] Using triangular fuzzy number d mn =(l mn ,e mn ,p mn ) indicates the relative importance of different indicators, l mn Indicates the lower limit of importance between different indicators, e mn Indicates the maximum possible value of the importance between different indicators, p mn Indicates the upper limit of importance between different indicators, including:

[0104] C1. Compare the importance of different indicators. The importance is obtained by expert scoring method. The triangular fuzzy number d between each indicator is obtained. mn =(l mn ,e mn ,p mn );

[0105] C2, according to the triangular fuzzy number d mn =(l mn ,e mn ,p mn ) Construct the fuzzy matrix E 0 =(e mn );

[0106] C3. Calculate the fuzzy judgment matrix E:

[0107]

[0108] Among them, s mn =(p mn -l mn ) / (2e mn );

[0109] C4. Get the adjustment judgment matrix A, A=E 0 ×E, transform A into a judgment matrix A with a diagonal of 1 0 ;

[0110] C5. Calculate the weight r of index j using the square root method 0j :

[0111]

[0112] Among them, δ mn is the judgment matrix A 0 The elements of ; m and n represent the rows and columns of the matrix respectively.

[0113] r 0j After normalization, the subjective weight w is obtained.

[0114] The indicator weight calculation according to the second indicator set and the third indicator set also includes:

[0115] Calculate the objective weight of the indicator:

[0116] B1. Determine the evaluation indicators:

[0117] Experts are hired to make empirical judgments on weights. Assuming there are x evaluation indicators, y experts make empirical judgments on the weights of each indicator at the same time, and the empirical judgment matrix A of the weights of each indicator is obtained:

[0118] A=[A 1 ,A 2 ,A 3 ,…,A x ] T

[0119]

[0120] B2. Determine the reference sequence A 0 :

[0121] A maximum weight value is selected from the experience judgment matrix A as the public reference weight value, and the reference weight values ​​of each expert are assigned to this value:

[0122] A 0 =(a 01 ,a 02 ,…,a 0y )

[0123] B3. Calculate each indicator sequence A 1 , A 2 , …, A x With A 0 The distance D 0i :

[0124]

[0125] Among them, a 0k represents the kth reference value, a ik represents the kth reference value of the ith indicator;

[0126] B4. Obtain the weight of each indicator and normalize each indicator:

[0127]

[0128] Among them, r′ represents the unnormalized weight of the i-th indicator; r represents the objective weight.

[0129] The subjective weight and objective weight of each indicator are integrated to obtain the comprehensive weight of each indicator, including:

[0130] The subjective weight and objective weight are integrated through the principles of game theory:

[0131] With the goal of minimizing the deviation between the two weights, the subjective weight and the objective weight are optimized, including:

[0132] min(||θ-w|| 2 +||θ-r|| 2 )

[0133] Among them, θ represents the comprehensive weight; w represents the subjective weight; r represents the objective weight;

[0134] The objective is transformed into a system of linear equations using matrix differential properties:

[0135]

[0136] Where T represents the transposition operation; a 1 and a 2 represents the coefficient;

[0137] Normalize the linear equations to get the comprehensive weight:

[0138] θ=b 1 w T +b 2 r T

[0139] in,

[0140] S4. The evaluation results are calculated based on the comprehensive weight of each indicator combined with the improved matter-element extension model; based on the evaluation results, it is determined whether the line loss level in the area meets the expectations. If it does, the evaluation is completed; if it does not, the equipment operation in the area is dynamically adjusted and then enters S2. The dynamic adjustment includes: according to the evaluation results, selecting the lower evaluation level.

[0141] The evaluation results calculated based on the comprehensive weight of each indicator and the improved matter-element extension model include:

[0142] The improved matter-element extension model includes:

[0143] The classical matter-element analysis method is improved so that both ends of the general classical domain value are divided by the node domain V L The maximum value b on the right Lp , and the improved matter-element extension model is obtained:

[0144]

[0145] Where: N i is the evaluation level of line loss level; C L is the evaluation index; R N V is the matter-element of the object to be evaluated; Lrepresents the comprehensive weight θ of the Lth evaluation index; b Lp Indicates the maximum value of the indicator at the same level;

[0146] Then the distance D of the domain range can be calculated ij ,Right now:

[0147]

[0148] where a ij Indicates the ratio of the minimum value of each indicator to the maximum value of the same level indicator; b ij It indicates the ratio of the maximum value of each indicator to the maximum value of the indicator at the same level; the value range of each indicator is obtained by the expert scoring method;

[0149] Then the comprehensive correlation K can be calculated j (N 0 ) is calculated as

[0150]

[0151] Among them, N 0 is the object to be evaluated; θ represents the comprehensive weight.

[0152]

[0153] in, It indicates the minimum value of the comprehensive correlation of each indicator in the second evaluation level and the third evaluation level; Indicates the maximum value of the comprehensive correlation of each indicator in the second evaluation level and the third evaluation level; K j * (N 0 ) represents the maximum closeness; j * It represents the characteristic value of the evaluation grade variable, which is used to judge the degree to which things tend to be evaluated.

[0154] j * The value range is between 0 and 5, with 1 being the worst and 5 being the best, and the values ​​in between increasing from small to large.

[0155] A preferred embodiment of the present invention further provides a computer medium, including a processor, a memory, and a computer program for implementing the method of the present invention.

[0156] In summary, the line loss level assessment method considering the grid structure of the present invention constructs an assessment system including the dimensions of equipment physical characteristics, grid structure characteristics, and grid operation characteristics, which comprehensively reflects the level of grid line loss. The improved hierarchical analysis method is used to obtain the comprehensive weight of indicators, and the game theory is used to integrate the weights to make the weight results more accurate. The evaluation results are obtained by calculating the improved physical element extension model, which is helpful for accurately evaluating the line loss level. This method has good practical significance.

[0157] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating line loss level considering the grid structure of a power grid, characterized in that: The following steps are involved: S1. Performing grid structure analysis according to a first data set to obtain a first indicator set; the first data set includes grid structures of a power grid under a preset number of voltage levels; S2. Classifying indicators according to the first indicator set to obtain a second indicator set and a third indicator set; the second indicator set includes indicators that have a positive impact on line loss; the third indicator set includes indicators that have a negative impact on line loss; S3, calculating the indicator weights according to the second indicator set and the third indicator set to obtain the subjective weight and objective weight of each indicator; fusing the subjective weight and objective weight of each indicator to obtain the comprehensive weight of each indicator; S4. Calculate the evaluation result based on the comprehensive weight of each indicator combined with the improved matter-element extension model; determine whether the line loss level in the area meets the expectation based on the evaluation result. If it does, complete the evaluation; if not, dynamically adjust the equipment in the area and enter S2.

2. The line loss level assessment method considering the grid structure of the power grid according to claim 1 is characterized in that: The grid structure analysis is performed according to the first data set to obtain the first indicator set, which includes: According to the equipment operation characteristics under the preset number of voltage levels, considering the multiple influencing factors and multi-party coordination relationship of technical level, design and manufacturing level and investment benefits, the grid structure analysis is carried out to obtain the first indicator set, including: First evaluation level: the line loss level in the area is the target layer; The second evaluation level: equipment physical characteristic indicators, grid structure characteristic indicators and grid operation characteristic indicators; The third evaluation level: the equipment physical characteristic indicators include transformer equipment status indicators and reactive power compensation configuration indicators; the grid structure characteristic indicators include average power factor indicators, load time distribution indicators and load space distribution indicators; the grid operation characteristic indicators include average line length indicators and power supply distribution indicators.

3. The line loss level assessment method considering the grid structure of the power grid according to claim 2 is characterized in that: The S2 includes: Analyzing the positive and negative effects of each indicator on the line loss level according to the first indicator set, to obtain a second indicator set including positive indicators and a third indicator set including negative indicators; The positive indicators include: Among them, a ij and c ij They represent the positive normalized index value and the original index value of the i-th regional power grid index j respectively; The negative indicators include: Among them, b ij and c ij They respectively represent the negative standardized index value and the original index value of the i-th regional power grid index j.

4. The line loss level assessment method considering the grid structure of the power grid according to claim 3 is characterized in that: Calculating the indicator weights according to the second indicator set and the third indicator set includes: Calculate the subjective weight of the indicator: Using triangular fuzzy number d mn =(l mn ,e mn ,p mn ) indicates the relative importance of different indicators, l mn Indicates the lower limit of importance between different indicators, e mn Indicates the maximum possible value of the importance between different indicators, p mn Indicates the upper limit of importance between different indicators, including: C1. Compare the importance of different indicators to obtain the triangular fuzzy number d between each indicator. mn =(l mn ,e mn ,p mn ); C2, according to the triangular fuzzy number d mn =(l mn ,e mn ,p mn ) constructs the fuzzy matrix E0=(e mn ); C3. Calculate the fuzzy judgment matrix E: Among them, s mn =(p mn -l mn ) / (2e mn ); C4, obtain the adjusted judgment matrix A, A = E0 × E, and transform A into a judgment matrix A0 with a diagonal of 1; C5. Calculate the weight r of index j using the square root method 0j : Among them, δ mn are the elements of the judgment matrix A0; r 0j After normalization, the subjective weight w is obtained.

5. The line loss level assessment method considering the grid structure of the power grid according to claim 4 is characterized in that: Calculating the indicator weights according to the second indicator set and the third indicator set also includes: Calculate the objective weight of the indicator: B1. Determine the evaluation indicators: Experts are hired to make empirical judgments on weights. Assuming there are x evaluation indicators, y experts make empirical judgments on the weights of each indicator at the same time, and the empirical judgment matrix A of the weights of each indicator is obtained: <h2 style=";text-align:left;direction:ltr">A=[A1,A2,A3,…,A<h2 style=";text-align:left;direction:ltr"> x <h2 style=";text-align:left;direction:ltr"> ]<h2 style=";text-align:left;direction:ltr"> T B2. Determine the reference sequence A0: A maximum weight value is selected from the experience judgment matrix A as a common reference weight value, and the reference weight values ​​of each expert are assigned to this value: A0=(a 01 ,to 02 ,…,to 0y ) B3. Calculate each index sequence A1, A2, ..., A x Distance D from A0 0i : Among them, a 0k represents the kth reference value, a ik represents the kth reference value of the ith indicator; B4. Obtain the weight of each indicator and normalize each indicator: Among them, r′ represents the unnormalized weight of the i-th indicator; r represents the objective weight.

6. The line loss level assessment method considering the grid structure of the power grid according to claim 5 is characterized in that: The subjective weight and objective weight of each indicator are integrated to obtain the comprehensive weight of each indicator, including: The subjective weight and the objective weight are integrated through the game theory principle: With the goal of minimizing the deviation between the two weights, the subjective weight and the objective weight are optimized, including: min(||θ-w||2+||θ-r||2) Among them, θ represents the comprehensive weight; w represents the subjective weight; r represents the objective weight; The objective is transformed into a system of linear equations through matrix differential properties: Wherein, T represents the transposition operation; a1 and a2 represent coefficients; The linear equations are normalized to obtain the comprehensive weight: θ=b1w T +b2r T in, 7. The line loss level assessment method considering the grid structure of the power grid according to claim 6 is characterized in that: The evaluation results calculated based on the comprehensive weight of each indicator combined with the improved matter-element extension model include: The improved matter-element extension model includes: The classical matter-element analysis method is improved so that both ends of the general classical domain value are divided by the node domain V L The maximum value b on the right Lp , and the improved matter-element extension model is obtained: Where: N i is the evaluation level of line loss level; C L is the evaluation index; R N V is the matter-element of the object to be evaluated; L represents the comprehensive weight θ of the Lth evaluation index; b Lp Indicates the maximum value of the indicator at the same level; Then the distance D of the domain range can be calculated ij ,Right now: where a ij It represents the ratio of the minimum value of each indicator to the maximum value of the indicator at the same level; b ij It indicates the ratio of the maximum value of each indicator to the maximum value of the indicator at the same level; the value range of each indicator is obtained by the expert scoring method; Then the comprehensive correlation K can be calculated j (N0) is calculated as Among them, N0 is the object to be evaluated; θ represents the comprehensive weight; in, Indicates the minimum value of the comprehensive correlation of each indicator in the second evaluation level and the third evaluation level; K represents the maximum value of the comprehensive correlation of each indicator in the second evaluation level and the third evaluation level; j * (N0) represents the maximum closeness; j * It represents the characteristic value of the evaluation grade variable, which is used to judge the degree to which things tend to be evaluated.

8. A computer medium, characterized in that The invention comprises a processor, a memory and a computer program for implementing the method according to any one of claims 1 to 7.

Images