CN112686536A - Power grid disaster response capability quantitative evaluation method based on fuzzy comprehensive evaluation - Google Patents
Power grid disaster response capability quantitative evaluation method based on fuzzy comprehensive evaluation Download PDFInfo
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Abstract
The invention discloses a power grid disaster response capability quantitative evaluation method based on fuzzy comprehensive evaluation, belonging to the technical field of power grid disaster response capability evaluation, and the method comprises the following steps: firstly, establishing an assessment index system of the pre-disaster bearing capacity and the post-disaster recovery capacity of the power grid according to collected data of the conventional operation aspect of the power grid, relevant indexes such as power grid assessment indexes and the like; then, an analytic hierarchy process and an entropy weight process are used for respectively obtaining objective weight and subjective weight, the objective weight and the subjective weight are combined into comprehensive weight to determine index weight, and the advantages of the two types of weight are fused, so that weight deviation of the index caused by single objective weight and subjective weight is reduced; and finally, carrying out quantitative evaluation on the disaster response capability of the power grid by fuzzy comprehensive evaluation on the membership function and the comprehensive weight.
Description
Technical Field
The invention relates to a power grid disaster response capability quantitative evaluation method based on fuzzy comprehensive evaluation and weight, and belongs to the technical field of power grid disaster response capability evaluation.
Background
The power system is an important component part of the continuous development of the current social economy, the power grid is used as an important part of infrastructure in the power system, the power grid and various factors form a complex and polymorphic power grid system, and any micro disturbance can possibly cause the key infrastructure accidents of the power grid. In recent years, extreme natural disasters are frequently caused due to abnormal global climate change, and the influence degree and range of various extreme natural disasters with small probability and high risk on a power grid are obviously increased. The power grid has the advantages of multiple electrical devices, wide installation positions, flexible and changeable operation modes and complex network structure, the evaluation of the disaster response capability of the power grid is a multi-attribute comprehensive evaluation problem, the power grid becomes more complex along with the continuous development of a power system, the dependence of the society on the power system is also enhanced, and once the power grid is damaged by natural disasters, the power grid not only has great influence on the economy, but also can directly influence the normal production order of the society. Therefore, the evaluation of the capability of the power grid to cope with extreme natural disasters and the capability of the power grid to recover after disasters has important practical significance.
At present, the research on the evaluation technology of the influence of extreme natural disasters on the power grid is mainly focused on the aspects of an evaluation model and an evaluation system, an evaluation index system is mainly established for the toughness evaluation technology of the power distribution grid side, but a quantitative analysis and evaluation result technology is lacked; the power grid emergency system evaluation uses an analytic hierarchy process and fuzzy comprehensive evaluation by establishing an evaluation system, but certain subjective assumption exists, and in addition, the existing power grid emergency capacity evaluation technology does not consider the dynamic property and complex relevance among various indexes of a power grid. In the research of a power distribution network differentiation rule considering the composite fault risk and a power distribution network disaster response capability evaluation and disaster-resistant planning method, a power grid disaster response capability evaluation system is not established from the time dimension of influencing a power grid by extreme disasters, and meanwhile, the evaluation result is not quantized in the method.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a power grid disaster response capability quantitative evaluation method based on fuzzy comprehensive evaluation.
In order to solve the technical problem, the method for quantitatively evaluating the disaster response capability of the power grid based on the fuzzy comprehensive evaluation comprises the following steps of:
receiving comprehensive operation information data of a power grid, wherein the comprehensive operation information data of the power grid comprises power grid operation conditions, emergency personnel allocation conditions, emergency resource allocation condition information data and historical disaster response data of the power grid;
classifying and sorting comprehensive operation information data of the power grid according to the influence degree of extreme natural disasters on the power grid and the disaster coping stage of the power grid system to form various power grid disaster coping capacity indexes, wherein the power grid disaster coping capacity indexes can be divided into two categories, namely a power grid disaster pre-tolerance index system and a power grid disaster post-restoration index system;
subjective weight and objective weight of power grid disaster response capability index evaluation are respectively obtained through an analytic hierarchy process and an entropy weight process, and each corresponding index is subjected to index weight proportion calculation to obtain comprehensive weight so as to reduce weight deviation caused by two types of weight processes;
dividing the power grid disaster response capability evaluation result into 5 different grades, and forming a fuzzy evaluation set, wherein the evaluation set V is { V ═ V }1(high disaster response capability) V2(stronger disaster response capability) V3(general disaster response capability), V4(weak disaster response capacity), V5(weak disaster response capability) }, simultaneously establishing 5 Gauss type membership functions corresponding to the evaluation set to obtain a fuzzy evaluation matrix, and using a fuzzy operator to calculate comprehensive weight and the fuzzy evaluation matrix to obtain a power grid disaster response capability evaluation result;
dividing evaluation in the evaluation set into numerical grades to quantify the evaluation result of the disaster response capability of the power grid under the extreme natural disaster.
Preferably, the power grid pre-disaster tolerance index system includes: the disaster response capability of the power transmission network comprises the average load rate of a line, the reliability of system power supply, the frequency qualification rate, the voltage qualification rate, the average capacity-load ratio of a transformer, the short-circuit current, the insulation rate, the unbalance degree of three-phase voltage and the reactive compensation proportion; the disaster responding capacity of the power distribution network comprises line load rate, voltage qualification rate, N-1 passing rate of the power distribution network lines, power supply diversity, distributed power supply capacity proportion, black start capacity, intelligent electric meter installation rate, inter-area contact rate, communication network coverage rate proportion, user average power failure time and user average power failure times; the disaster response capability of the transformer substation comprises equipment health degree, multi-principal rate of the transformer substation, full-stop check passing rate of the transformer substation and heavy-load transformer substation proportion; the emergency prevention capability comprises training and exercise before crisis disaster, emergency principle and strategy updating, crisis early warning capability, emergency information system, power failure information issuing correctness, decision and command coordination capability and emergency resource guarantee; the grid disaster resistance indexes comprise grid disaster resistance rate, important load distribution uniformity, automatic isolation technology line coverage rate, emergency power supply application rate and distribution equipment operation year level;
preferably, the post-disaster recovery index system specifically comprises network frame recovery capacity, main line recovery rate, distribution network distribution automation coverage rate and communication fiber rate, wherein the network frame recovery capacity comprises backbone line recovery proportion, main line recovery proportion; the power grid load recovery capacity comprises a power supply path recovery rate, a distribution network automatic recovery rate, a key load recovery speed, island emergency guarantee time, emergency communication recovery time and power load transfer capacity; the emergency resource regulation and control capability comprises emergency team organization capability, material guarantee capability and emergency rescue response speed.
Preferably, the analytic hierarchy process comprises:
(1) judging the structure of the matrix: assigning values to the weights of indexes of all levels according to experts, and comparing every two indexes according to a nine-scale method to construct the indexes;
(2) and (3) checking consistency: the maximum eigenvalue lambda of the judgment matrix can be calculatedmaxAnd the corresponding characteristic vector theta, and the consistency index CI of the judgment matrix is shown as the formula (1):
in the formula: n is the order of the matrix, λmaxIs the maximum eigenvalue of the matrix.
At the same time, the consistency ratio CR can be calculated as shown in equation (2):
in the formula: CI is a judgment matrix consistency index, RI is an average consistency index corresponding to the judgment matrix, when CR is less than 0.10, consistency check is passed, the feature vector is the weight of the index, otherwise, if CR is more than 0.10, the judgment matrix needs to be reconstructed.
Preferably, the entropy weight method is to calculate the objective weight of the power grid disaster response capability according to the information amount carried by each index and the importance degree of the evaluation index in decision making, and includes:
(1) the multiple experts evaluate the disaster response capability indexes of the power grid in extreme natural disasters by means of expert experience assignment to form a judgment matrix X ═ (X ═ X)ij)m×nI is 1,2 … m, j is 1,2 … n (m is the number of experts, n is the number of indexes). The X judgment matrix is normalized, and the formula is shown as formula (3):
in the formula: r isijIs the normalized index value.
(2) Entropy calculation is carried out on the normalized index data, and the entropy of the ith expert on the jth index is as shown in a formula (4):
in the formula: hjFor the entropy value found, j is 1,2 … n.
When u isijWhen the value is equal to 0, then u is addedijlnuijWhen the entropy value of the j index is 0, the entropy weight of the j index is calculated as the following formula (5):
further, the comprehensive weight refers to a more reasonable comprehensive weight obtained by fusing two weights under the condition that subjective factors exist in an analytic hierarchy process and an entropy weight process lacks expert experience and cannot reflect the importance degree of indexes in the actual power grid disaster response problem, as shown in the following formula (6):
in the formula: thetajAnd omegaj(j ═ 1,2 … n) represents the subjective weight value and the objective weight value of the index, respectively.
Further, the Gauss-type membership function f (x, σ, c) is represented by the following formula (7):
in the formula: x is a decision index, sigma and c are 2 parameters of the Gauss membership function, sigma is generally a positive number, wherein sigma is 0.3, and the value of c represents the center position of the membership function.
Further preferably, in order to ensure that each index has 5 scoring membership degrees, the c value of 5 indexes is adopted: c. C1＝1，c2＝0.75，c3＝0.5，c4＝0.25，c5When the value is 0, the value is substituted into formula (7) to obtain membership functions corresponding to 5 evaluation sets, and the index R in the judgment matrix R is obtained by calculation beforeijThe evaluation matrix F is obtained by substituting the evaluation matrix F into membership functions of 5 evaluation levels, as shown in the following formula (8):
in the formula: f. ofVk(rij) (k ═ 1,2, … 5; j ═ 1,2, … n) is the index rijTo the judgment grade VkDegree of membership.
Preferably, the first and second liquid crystal materials are,
the operators are methods of first integrating and then summing, the synthesis processThe index information in the judgment matrix can be reasonably applied due to high intensity, so that the method adopts
Operator (to)
And replacing), and simultaneously combining the previous comprehensive weight according to an average weighting principle to obtain the overall evaluation result of the power grid disaster response evaluation system as shown in the following formula (9):
in the formula: p is a radical ofi(Vk) Is to represent the relative V of each indexkDegree of membership, i.e. VkTo the extent described.
Further preferably, the evaluation of the formula (9) is quantified, and the calculation method for quantifying the corresponding quantitative evaluation of the fuzzy evaluation set is shown in the formula (10):
and judging the interval according to the table 1 according to the calculated comprehensive evaluation result of the power grid, so as to obtain the disaster response capability result of the power grid in the extreme natural disaster.
Compared with the prior art, the method has the advantages that the time dimension of the influence of extreme natural disasters on the power grid is taken as a starting point, the disaster response capability of the power grid is divided into the pre-disaster stage and the post-disaster stage, the disaster response capability of the power grid is effectively evaluated, meanwhile, a more comprehensive evaluation index system is established, the comprehensiveness and the accuracy of evaluation are improved, and more scientific and feasible guidance suggestions are provided for power grid operators.
Drawings
Fig. 1 is a flowchart of a power grid disaster response capability quantitative evaluation method based on fuzzy comprehensive evaluation according to an embodiment;
FIG. 2 is the power grid pre-disaster tolerance index system;
fig. 3 is an index system of the post-disaster recovery force of the power grid.
Detailed Description
Embodiments and examples of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for quantitatively evaluating disaster response capability of a power grid based on fuzzy comprehensive evaluation according to the present invention, and the method for quantitatively evaluating disaster response capability of a power grid based on fuzzy comprehensive evaluation shown in fig. 1 is specifically described with reference to steps of the method for quantitatively evaluating disaster response capability of a power grid based on fuzzy comprehensive evaluation shown in fig. 1.
In step S1, receiving grid integrated operation information data, where the grid integrated operation information data includes grid operation status, emergency personnel allocation, and emergency resource allocation information data;
in step S2, classifying and sorting the comprehensive operation information data of the power grid according to the degree of influence of extreme natural disasters on the power grid and the disaster coping stage of the power grid system to form multiple power grid disaster response capability indexes, wherein the power grid disaster response capability indexes can be divided into two categories, namely a pre-disaster tolerance index and a post-disaster recovery index of the power grid;
as shown in fig. 2, the power grid pre-disaster tolerance index system includes: the disaster response capability of the power transmission network comprises the average load rate of a line, the reliability of system power supply, the frequency qualification rate, the voltage qualification rate, the average capacity-load ratio of a transformer, the short-circuit current, the insulation rate, the unbalance degree of three-phase voltage and the reactive compensation proportion; the disaster responding capacity of the power distribution network comprises line load rate, voltage qualification rate, N-1 passing rate of the power distribution network lines, power supply diversity, distributed power supply capacity proportion, black start capacity, intelligent electric meter installation rate, inter-area contact rate, communication network coverage rate proportion, user average power failure time and user average power failure times; the disaster response capability of the transformer substation comprises equipment health degree, multi-principal rate of the transformer substation, full-stop check passing rate of the transformer substation and heavy-load transformer substation proportion; the emergency prevention capability comprises training and exercise before crisis disaster, emergency principle and strategy updating, crisis early warning capability, emergency information system, power failure information issuing correctness, decision and command coordination capability and emergency resource guarantee; the grid disaster resistance indexes comprise grid disaster resistance rate, important load distribution uniformity, automatic isolation technology line coverage rate, emergency power supply application rate and distribution equipment operation year level;
as shown in fig. 3, the post-disaster recovery index system specifically comprises a backbone line recovery ratio, a trunk line recovery ratio, a distribution network distribution automation coverage rate and a communication fiber rate, wherein the network frame recovery capability comprises the backbone line recovery ratio, the trunk line recovery ratio, the distribution network distribution automation coverage rate and the communication fiber rate; the power grid load recovery capacity comprises a power supply path recovery rate, a distribution network automatic recovery rate, a key load recovery speed, island emergency guarantee time, emergency communication recovery time and power load transfer capacity; the emergency resource regulation and control capability comprises emergency team organization capability, material guarantee capability and emergency rescue response speed.
In step S3, subjective weight and objective weight of power grid disaster response capability index evaluation are obtained by an analytic hierarchy process and an entropy weight process, and each corresponding index is subjected to calculation of index weight ratio to obtain a comprehensive weight, so as to reduce weight deviation caused by the two types of weight processes;
for example, based on the actual condition of the power grid, the subjective thinking of a decision maker for evaluating the disaster response capability of the power grid is separated and modeled, the abstraction is converted into a concrete method, the index weight is the basis of evaluating the disaster response capability of the power grid, the complex and fuzzy evaluation problem is quantified, an analytic hierarchy process is adopted as a calculation method of the subjective weight,
(1) judging the structure of the matrix: and (4) assigning the weights of indexes of all levels according to experts, and comparing every two indexes according to a nine-scale method to construct the indexes.
(2) And (3) checking consistency: the maximum eigenvalue lambda of the judgment matrix can be calculatedmaxAnd the corresponding characteristic vector theta, and the consistency index CI of the judgment matrix is shown as the formula (1):
in the formula: n is the order of the matrix, λmaxIs the maximum eigenvalue of the matrix.
At the same time, the consistency ratio CR can be calculated as shown in equation (2):
in the formula: CI is a judgment matrix consistency index, RI is an average consistency index corresponding to the judgment matrix, when CR is less than 0.10, consistency check is passed, the feature vector is the weight of the index, otherwise, if CR is more than 0.10, the judgment matrix is required to be reconstructed.
Analyzing the information quantity carried by each index of the power grid and the importance degree of the evaluation index in decision making, and calculating the objective weight of the disaster response capability of the power grid by using an entropy weight method, wherein the calculation steps are as follows:
(4) the multiple experts evaluate the disaster response capability indexes of the power grid in extreme natural disasters by means of expert experience assignment to form a judgment matrix X ═ (X ═ X)ij)m×nI is 1,2 … m, j is 1,2 … n (m is the number of experts, n is the number of indexes). The X judgment matrix is normalized, and the formula is shown as formula (3):
in the formula: r isijIs the normalized index value.
(5) Entropy calculation is carried out on the normalized index data, and the entropy of the ith expert on the jth index is as shown in a formula (4):
in the formula: hjFor the entropy value found, j is 1,2 … n.
When u isijWhen the value is equal to 0, then u is addedijlnuijWhen the entropy value of the j index is 0, the entropy weight of the j index is calculated as the following formula (5):
for subjective factors existing in an analytic hierarchy process, an entropy weight method lacks expert experience and cannot reflect the importance degree of indexes in the actual power grid disaster response problem, two weights are fused to obtain a more reasonable comprehensive weight, as shown in the following formula (6):
in the formula: thetajAnd omegaj(j ═ 1,2 … n) represents the subjective weight value and the objective weight value of the index, respectively.
In step S4, a fuzzy comprehensive evaluation matrix combining the comprehensive weight and the Gauss-type membership function is used to evaluate the power grid disaster response capability by using a fuzzy operator; preferably, the fuzzy comprehensive evaluation matrix is specifically a class of evaluation set, where the evaluation set { (strong disaster response capability), (general disaster response capability), (weak disaster response capability) };
the power grid disaster response capability assessment has certain ambiguity, and meanwhile, the power grid is a complex network which is influenced by multiple factors and has dynamics, so that the fuzzy theory has strong advantages to balance the problems. Meanwhile, fuzzy subsets of different indexes for different judgments can be described by a membership function, and a Gauss type membership function f (x, σ, c) can be used, as shown in the following formula (7):
in the formula: x is a decision index, sigma and c are 2 parameters of the Gauss membership function, sigma is generally a positive number, and sigma is 0.3 in the text. The value of c represents the center position of the membership function.
To ensure 5 scoring membership per index, we assume a c-value of 5 indices: c. C1＝1，c2＝0.75，c3＝0.5，c4＝0.25，c5When the evaluation set is 0, the evaluation set is substituted into formula (7) to obtain membership functions corresponding to 5 evaluation sets. Meanwhile, the index R in the judgment matrix R is obtained by previous calculationijThe evaluation matrix F is obtained by substituting the evaluation matrix F into membership functions of 5 evaluation levels, as shown in the following formula (8):
in the formula: f. ofVk(rij) (k ═ 1,2, … 5; j ═ 1,2, … n) is the index rijTo the judgment grade VkDegree of membership.
in the formula: p is a radical ofi(Vk) Is to represent the relative V of each indexkDegree of membership, i.e. VkTo the extent described.
In step S5, the evaluation language in the evaluation set is divided into numerical grades to quantify the evaluation result of the power grid disaster response capability under the extreme natural disaster.
The evaluation of the formula (9) is quantized, the fuzzy evaluation set is quantized correspondingly, and the quantization and disaster response capability grading are shown in the following table 1:
TABLE 1 index evaluation results and quantitative rating Table
The calculation method for evaluating quantification is shown in formula (10):
and judging the interval according to the table 1 according to the calculated comprehensive evaluation result of the power grid, so as to obtain the disaster response capability result of the power grid in the extreme natural disaster.
Claims (9)
1. A power grid disaster response capability quantitative evaluation method based on fuzzy comprehensive evaluation is characterized by comprising the following steps:
receiving comprehensive operation information data of a power grid, wherein the comprehensive operation information data of the power grid comprises power grid operation state data, emergency personnel allocation condition data, emergency resource allocation condition information data and power grid historical disaster response data;
classifying and sorting comprehensive operation information data of the power grid according to the influence degree of extreme natural disasters on the power grid and the disaster coping stage of the power grid system to form various power grid disaster coping capacity indexes, wherein the power grid disaster coping capacity indexes can be divided into two categories, namely a power grid disaster pre-tolerance index system and a power grid disaster post-restoration index system;
subjective weight and objective weight of power grid disaster response capability index evaluation are respectively obtained through an analytic hierarchy process and an entropy weight process, and each corresponding index is subjected to index weight proportion calculation to obtain fuzzy operator operation comprehensive weight so as to reduce weight deviation caused by two types of weight methods;
dividing the power grid disaster response capability evaluation result into 5 different grades, and forming a fuzzy evaluation set, wherein V is { V ═ V }1(high disaster response capability) V2(stronger disaster response capability) V3(general disaster response capability), V4(weak disaster response capacity), V5(weak disaster response capability) }, 5 Gauss type membership functions corresponding to the fuzzy evaluation set are simultaneously established to obtain a fuzzy evaluation matrix, and the comprehensive weight is operated by using a fuzzy operatorObtaining a power grid disaster response capability evaluation result by the aid of the weight and fuzzy evaluation matrix;
dividing evaluation in the evaluation set into numerical grades to quantify the evaluation result of the disaster response capability of the power grid under the extreme natural disaster.
2. The method for quantitatively evaluating the disaster response capability of the power grid based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein the system of the tolerance index before the power grid disaster comprises: the disaster response capability of the power transmission network comprises the average load rate of a line, the reliability of system power supply, the frequency qualification rate, the voltage qualification rate, the average capacity-load ratio of a transformer, the short-circuit current, the insulation rate, the unbalance degree of three-phase voltage and the reactive compensation proportion; the disaster responding capacity of the power distribution network comprises line load rate, voltage qualification rate, N-1 passing rate of the power distribution network lines, power supply diversity, distributed power supply capacity proportion, black start capacity, intelligent electric meter installation rate, inter-area contact rate, communication network coverage rate proportion, user average power failure time and user average power failure times; the disaster response capability of the transformer substation comprises equipment health degree, multi-principal rate of the transformer substation, full-stop check passing rate of the transformer substation and heavy-load transformer substation proportion; the emergency prevention capability comprises training and exercise before crisis disaster, emergency principle and strategy updating, crisis early warning capability, emergency information system, power failure information issuing correctness, decision and command coordination capability and emergency resource guarantee; the grid disaster resistance indexes comprise grid disaster resistance rate, important load distribution uniformity, automatic isolation technology line coverage rate, emergency power supply application rate and distribution equipment operation year level.
3. The method for quantitatively evaluating the disaster response capability of the power grid based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein the post-disaster recovery capability index system specifically comprises a backbone line recovery ratio, a trunk line recovery ratio, a distribution network distribution automation coverage rate and a communication fiber rate; the power grid load recovery capacity comprises a power supply path recovery rate, a distribution network automatic recovery rate, a key load recovery speed, island emergency guarantee time, emergency communication recovery time and power load transfer capacity; the emergency resource regulation and control capability comprises emergency team organization capability, material guarantee capability and emergency rescue response speed.
4. The method for quantitatively evaluating the disaster response capability of the power grid based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein the analytic hierarchy process comprises:
assigning values to the weights of indexes of all levels according to experts, and comparing every two indexes according to a nine-scale method to construct the indexes;
the maximum eigenvalue lambda of the judgment matrix can be calculatedmaxAnd judging a consistency index CI of the matrix according to the corresponding characteristic vector theta, wherein the consistency index CI is calculated in a mode shown by the following formula:
where n is the order of the matrix, λmaxIs the maximum eigenvalue of the matrix;
at the same time, the consistency ratio CR can be calculated as shown below:
wherein, CI is the consistency index of the judgment matrix, RI is the average consistency index corresponding to the judgment matrix, when CR <0.10, the consistency test is passed, the characteristic vector is the weight of the index, otherwise, if CR >0.10, the judgment matrix needs to be reconstructed.
5. The method for quantitatively evaluating the disaster response capability of the power grid based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein the entropy weight method is to calculate the objective weight of the disaster response capability of the power grid according to the information amount carried by each index and the importance degree of the evaluation index in decision making.
6. The method for quantitatively evaluating the disaster response capability of the power grid based on the fuzzy comprehensive evaluation as claimed in claim 5, wherein the calculating the objective weight of the disaster response capability of the power grid by the entropy weight method comprises:
and (X) evaluating the power grid disaster response capability index by means of expert experience in extreme natural disasters to form a judgment matrix X ═ X (Xij)m×nI is 1,2 … m, j is 1,2 … n, wherein m is the number of experts and n is the number of indexes; the X judgment matrix is normalized, and its formula is as follows:
in the formula: r isijIs the normalized index value;
performing entropy calculation on the normalized index data, wherein the entropy of the ith expert on the jth index is shown as the following formula:
in the formula: hjFor the entropy value found, j ═ 1,2 … n;
when u isijWhen the value is equal to 0, then u is addedijlnuijWhen the entropy value of the j index is 0, the entropy value weight of the j index is calculated as the following formula:
7. the method for quantitatively evaluating the disaster response capability of the power grid based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein the comprehensive weight is a more reasonable comprehensive weight obtained by fusing two weights under the condition that the entropy weight method lacks expert experience and cannot reflect the importance degree of the index in the actual disaster response problem of the power grid for the presence of subjective factors in the analytic hierarchy process, as shown in the following formula:
in the formula: thetajAnd omegaj(j ═ 1,2 … n) represents the subjective weight value and the objective weight value of the index, respectively.
8. The method for quantitatively evaluating the disaster response capability of the power grid based on the fuzzy comprehensive evaluation as claimed in claim 1, wherein the Gauss-type membership function f (x, σ, c) is represented by the following formula:
in the formula: x is a decision index, sigma and c are 2 parameters of the Gauss membership function, sigma is generally a positive number, and the value of c represents the central position of the membership function.
9. The method for quantitatively evaluating the disaster response capability of the power grid based on the fuzzy comprehensive evaluation as claimed in claim 8, wherein σ is 0.3.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113919763A (en) * | 2021-12-13 | 2022-01-11 | 国网江西省电力有限公司电力科学研究院 | Power grid disaster analysis method and device based on fuzzy evaluation matrix |
| CN115081951A (en) * | 2022-07-28 | 2022-09-20 | 东南大学溧阳研究院 | Fuzzy comprehensive evaluation-based power quality evaluation method for wind power grid-connected system |
| CN115619284A (en) * | 2022-11-10 | 2023-01-17 | 国网江苏省电力有限公司扬州供电分公司 | Photovoltaic power distribution network bearing capacity assessment method and device |
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2020
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113919763A (en) * | 2021-12-13 | 2022-01-11 | 国网江西省电力有限公司电力科学研究院 | Power grid disaster analysis method and device based on fuzzy evaluation matrix |
| CN115081951A (en) * | 2022-07-28 | 2022-09-20 | 东南大学溧阳研究院 | Fuzzy comprehensive evaluation-based power quality evaluation method for wind power grid-connected system |
| CN115619284A (en) * | 2022-11-10 | 2023-01-17 | 国网江苏省电力有限公司扬州供电分公司 | Photovoltaic power distribution network bearing capacity assessment method and device |
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Application publication date: 20210420 |