CN114399135A - A method for determining the correlation degree of power grid operation abnormality index based on analytic hierarchy process - Google Patents

Abstract

The invention discloses a power grid operation abnormity index cause correlation determination method based on an analytic hierarchy process, wherein a target layer and a criterion layer to be verified or the criterion layer and an index layer are input into a constructed judgment matrix; calculating a weight coefficient of the judgment matrix; performing consistency check according to the weight coefficient, if the consistency check is smaller than a set threshold value, judging that the judgment matrix passes the consistency check, otherwise, failing to pass the consistency check and ending the operation; and carrying out priority arrangement on the target layer, the criterion layer and the index layer, and screening to obtain the layer with the maximum correlation degree of the cause of the abnormal operation index of the power grid. The method helps the scheduling personnel to quickly locate the abnormal factors which have the largest influence on the power grid at present, is convenient for preferentially processing key problems, and improves the response speed and the efficiency of troubleshooting.

Description

A method for determining the correlation degree of power grid operation abnormality index based on analytic hierarchy process

technical field

The invention relates to a method for determining the synergistic correlation degree of power grid operation abnormality indexes based on the analytic hierarchy process, and belongs to the technical field of power system operation and power transmission network.

Background technique

With the rapid expansion of the scale of UHV AC and DC hybrid power grids, the rapid development of new energy grids, and the rapid increase in the proportion of new loads such as distributed power and energy storage, the complexity of the power system has increased. In response to the new requirements of the new generation of power systems for dispatching operation control, comprehensive evaluation applications are required, based on big data technology to aggregate and fuse various types of regulation business data, and analyze and mine the inherent laws between various types of data. The evaluation system based on power grid operation indicators needs to provide guidance or assist decision-making for dispatching operations as much as possible, achieve rapid response and efficient operation, solve problems affecting abnormal power grid operation, and improve the lean and intelligent level of power grid operation.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to overcome the defects of the prior art, and to provide a method for determining the correlation degree of power grid operation abnormality indexes based on AHP, the purpose is to help dispatchers quickly locate the abnormal factors that have the greatest impact on the power grid at present, and give priority to processing them. Critical issues, improving response speed and troubleshooting efficiency.

In order to achieve the above purpose, the present invention provides a method for determining the correlation degree of power grid operation abnormality index based on AHP, including:

Input the target layer to be verified and the criterion layer, or the criterion layer and the index layer into the constructed judgment matrix;

Calculate the weight coefficient of the judgment matrix;

Carry out the consistency check according to the weight coefficient, if the consistency check is less than the set threshold, it is determined that the judgment matrix passes the consistency check, otherwise the consistency check fails and the operation ends;

The target layer, the criterion layer and the index layer are prioritized, and the layer with the highest degree of correlation of the abnormality index of the power grid operation is obtained by screening.

Preferably, according to the existing hierarchical structure model 1, the relationship table between the target layer and the criterion layer is obtained;

Establish a new hierarchical structure model 2, and construct the relationship table between the indicator layer and the criterion layer.

First, construct a judgment matrix, including:

Build the judgment matrix between the target layer and the criterion layer:

In the formula: a _ij is the value that measures the importance of the i-th index of the target layer/criteria layer to the j-th index of the target layer/criteria layer, i∈[1,n], j∈[1,n], n is The sum of the number of indicators in the target layer and the number of indicators in the criterion layer;

a _ij follows an inverse matrix:

In the formula, a _ji is a value that measures the importance of the j-th index of the target layer/criteria layer to the i-th index of the target layer/criteria layer.

First, construct a judgment matrix, including:

Build a judgment matrix between the indicator layer and the criterion layer:

In the formula: a _ef is the value that measures the importance of the e-th index of the index layer/criteria layer to the f-th index of the index layer/criteria layer, e∈[1,h], f∈[1,h], h is The sum of the number of indicators in the indicator layer and the number of indicators in the criterion layer;

a _ef follows an inverse matrix:

In the formula, a _ef is a value that measures the importance of the e-th index of the index layer/criteria layer to the f-th index of the index layer/criteria layer.

Preferentially, calculate the weight coefficients of the judgment matrix, including:

According to the square root method, the jth indicator weight w _j is calculated as:

Preferentially, calculate the weight coefficients of the judgment matrix, including:

According to the square root method, the f-th indicator weight w _f is calculated as:

Preferentially, a consistency check is performed based on weight coefficients, including:

Calculate CI and CR:

In the formula, CI is the general consistency index of the judgment matrix of the target layer and the criterion layer, RI is the random consistency index of the judgment matrix of the target layer and the criterion layer, λ _max is the maximum characteristic root of the judgment matrix of the target layer and the criterion layer, x is n;

Calculate λ _max :

If the CR is less than the set threshold, the judgment matrix passes the consistency check.

Preferentially, a consistency check is performed based on weight coefficients, including:

Calculate CI and CR:

In the formula, CI is the general consistency index of the judgment matrix of the index layer and the criterion layer, RI is the random consistency index of the judgment matrix of the index layer and the criterion layer, λ _max is the maximum characteristic root of the judgment matrix of the index layer and the criterion layer, x is h;

Calculate λ _max :

If the CR is less than the set threshold, the judgment matrix passes the consistency check.

Preferentially, the value of RI is:

Judgment matrix order 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

;

CR takes the value 0.1.

First, the target layer, the criterion layer and the index layer are prioritized, and the ones with the largest synergistic correlation of the abnormal power grid operation indicators are obtained by screening, including:

Descending order of priority: target layer, criterion layer and indicator layer;

Obtain the sum of the weights of all indicators in the target layer, the sum of the weights of all indicators in the criterion layer, and the sum of the weights of all indicators in the indicator layer in turn;

Screen the sum of the weights of all indicators in the target layer, the sum of the weights of all indicators in the criterion layer, and the maximum value of the sum of the weights of all indicators in the indicator layer, and obtain the layer with the largest synergistic correlation of the abnormal power grid operation indicators.

Preferentially, the index of the target layer includes the synergistic correlation degree of the abnormality index of power grid operation;

The indicators of the criterion layer include adjustment ability and steady-state operation ability;

The indicators of the index layer include primary frequency regulation capability, AGC regulation capability, voltage regulation capability, section active power, line overload, main transformer overload, short-circuit current, bus voltage and grid system frequency.

Beneficial effects achieved by the present invention:

In the present invention, the target layer to be checked and the criterion layer, or the criterion layer and the index layer are input into the constructed judgment matrix; the weight coefficient of the judgment matrix is calculated; the consistency test is carried out according to the weight coefficient; Then it is judged that the judgment matrix passes the consistency check, otherwise the consistency check fails and the operation ends; the target layer, the criterion layer and the index layer are prioritized, and the level with the highest degree of correlation of the abnormality index of the power grid operation is obtained by screening to realize the power grid operation. The evaluation of the correlation degree of the operation abnormality index provides a reference basis for the priority of subsequent operations;

The purpose of the invention is to help dispatchers to quickly locate the abnormal factors that have the greatest impact on the power grid at present, facilitate the priority processing of key problems, and improve the response speed and the efficiency of troubleshooting.

Description of drawings

Figure 1 is a flow chart of the present invention.

Detailed ways

The following examples are only used to illustrate the technical solutions of the present invention more clearly, and cannot be used to limit the protection scope of the present invention.

The method for determining the correlation degree of power grid abnormality indicators based on AHP is to capture the index factors that affect the abnormality of the power grid, and based on the AHP method, these influencing factors are reasoned and weighted to provide information for subsequent dispatching operations. refer to. In the process of analysis, the power grid can be regarded as a complex system composed of many interrelated and mutually restricting factors that often lack quantitative data. AHP provides a new method for decision-making and sorting of such multi-factor problems A simple and practical modeling method.

When applying Analytic Hierarchy Process (AHP) to decision-making problems, first of all, the problem should be organized and layered, and a hierarchical model with layers should be constructed. Under this hierarchical model, complex problems are decomposed into components of elements, which in turn form several levels according to their attributes and relationships. Elements of the previous level act as criteria to dominate the related elements of the next level. These levels can be divided into three categories:

(i) The highest level: There is only one element in this level, which is generally the predetermined goal or ideal result of the analysis problem, so it is also called the goal level.

(ii) Intermediate layer: This layer contains the intermediate links involved in achieving the goal. It can be composed of several layers, including the criteria and sub-criteria to be considered, so it is also called the criterion layer.

(iii) The bottom layer: This layer includes specific indicators that can be selected to achieve the goal, so it is also called the indicator layer or the program layer.

First define the category of power grid operation indicators, and then construct the indicators hierarchically, such as selecting the adjustment capability and steady-state operation capability as the model. The hierarchical structure model mainly includes three parts: the target layer, the criterion layer and the indicator layer. After the index system is constructed, it is necessary to calculate the weight value of the power grid index post-evaluation index system. The basic calculation principle is to decompose the various elements of the evaluation system plan into several levels to form a hierarchical and ordered hierarchical structure model, and then compare the elements of each level of the hierarchical structure model with respect to a certain element of the previous level. Compare and judge, so as to obtain the weight of each element; arrange according to the comprehensive weight coefficient, and locate the optimal plan according to the principle of maximum weight.

1.1 Building a Hierarchical Model

Step S1 includes: establishing a required hierarchical structure model, and taking Table 1 as the internal structure of the hierarchical structure model.

Table 1

1.2 Constructing the Judgment Matrix

Step S2 includes: determining the judgment matrix of the target layer and the criterion layer and the judgment matrix of the criterion layer and the index layer, comparing the indicators a and _ij in the system respectively, and comprehensively forming the judgment matrix A:

Build the judgment matrix between the target layer and the criterion layer:

In the formula: a _ij is the value that measures the importance of the i-th index of the target layer/criteria layer to the j-th index of the target layer/criteria layer, i∈[1,n], j∈[1,n], n is The sum of the number of indicators in the target layer and the number of indicators in the criterion layer;

a _ij follows an inverse matrix:

In the formula, a _ji is a value that measures the importance of the j-th index of the target layer/criteria layer to the i-th index of the target layer/criteria layer.

The calculation methods of a _ij and a _ef adopt the AHP calculation criterion formula in the prior art, which will not be described in detail in this embodiment.

Build a judgment matrix between the indicator layer and the criterion layer:

In the formula: a _ef is the value that measures the importance of the e-th index of the index layer/criteria layer to the f-th index of the index layer/criteria layer, e∈[1,h], f∈[1,h], h is The sum of the number of indicators in the indicator layer and the number of indicators in the criterion layer;

a _ef follows an inverse matrix:

In the formula, a _ef is a value that measures the importance of the e-th index of the index layer/criteria layer to the f-th index of the index layer/criteria layer.

1.3 Weight calculation

According to the analytic hierarchy process-based method for evaluating the synergistic correlation degree of the power grid operation abnormality index, step S3 includes calculating the weight coefficient corresponding to the judgment matrix, and specifically calculating the weight w _j of the index j according to the square root method is:

According to the square root method, the weight w _f of the indicator f is calculated as:

1.4 Consistency check

According to the analytic hierarchy process-based method for evaluating the correlation degree of power grid operation abnormality index, the S4 step includes the consistency test of the weight coefficient corresponding to the judgment matrix. The calculation formula of the test result CR is as follows:

In the formula: CI is the general consistency index of the judgment matrix, RI is the random consistency index of the judgment matrix, λ _max is the maximum characteristic root of the judgment matrix, and the calculation formula is as follows:

The value of RI is the standard value of the average random consistency index RI in the AHP method, as shown in Table 2:

Table 2 Average random consistency index RI standard value

matrix order 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

According to the calculation result of CR, if CR<0.1, it is judged that the judgment matrix passes the consistency test, otherwise it does not have satisfactory consistency.

1.5 Weight permutation

Descending order of priority: target layer, criterion layer and indicator layer;

According to 1.4, the S5 step includes the arrangement of the comprehensive weight coefficients, and calculates the weights of the relative importance of all factors at a certain level, which is called the total ordering of the levels. This process is carried out from the highest level to the lowest level. According to the ranking results, locate the priority of the index with the greatest impact on the power grid, that is, the index with the greatest synergistic correlation.

2. Analysis of the results of the state analytic hierarchy process for the evaluation of power grid operation indicators

According to the method for determining the correlation degree of power grid abnormality index based on AHP in 1.5, the order of weights is known. When the consistency test result is smaller, it means that the index has a greater degree of correlation of the power grid abnormality. Therefore, in the process of power grid dispatching operation, in order to speed up the processing efficiency and shorten the corresponding time, we can give priority to solving the abnormal indicators with a large degree of correlation according to the above results.

The above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the technical principle of the present invention, several improvements and modifications can also be made. These improvements and modifications It should also be regarded as the protection scope of the present invention.

Claims (10)

1. A power grid operation abnormity index cause correlation determination method based on an analytic hierarchy process is characterized by comprising the following steps:

inputting a target layer to be verified and a criterion layer, or the criterion layer and an index layer into a constructed judgment matrix;

calculating a weight coefficient of the judgment matrix;

performing consistency check according to the weight coefficient, if the consistency check is smaller than a set threshold value, judging that the judgment matrix passes the consistency check, otherwise, failing to pass the consistency check and ending the operation;

and carrying out priority arrangement on the target layer, the criterion layer and the index layer, and screening to obtain the layer with the maximum correlation degree of the cause of the abnormal operation index of the power grid.

2. The analytic hierarchy process-based power grid operation abnormity index cause correlation determination method according to claim 1, characterized in that a relation table between a target layer and a criterion layer is obtained according to an existing hierarchical structure model I;

and establishing a new hierarchical structure model II, and establishing a relation table between the index layer and the criterion layer.

3. The analytic hierarchy process-based power grid operation anomaly index cause correlation determination method according to claim 1, wherein constructing a judgment matrix comprises:

constructing a judgment matrix between a target layer and a criterion layer:

in the formula: a is_ijTo measure the importance of the ith index of the target layer/criterion layer to the jth index of the target layer/criterion layer, i is the [1, n ]]，j∈[1,n]N is the sum of the number of indexes in the target layer and the number of indexes in the criterion layer;

a_ijfollowing a positive reciprocal matrix:

in the formula, a_jiFor measuring j index of target layer/criterion layer to target layer/criterion layerThe value of the importance of the i-th index.

4. The analytic hierarchy process-based power grid operation anomaly index cause correlation determination method according to claim 1, wherein constructing a judgment matrix comprises:

constructing a judgment matrix between the index layer and the criterion layer:

in the formula: a is_efTo measure the importance of the e index of index layer/criterion layer to the f index of index layer/criterion layer, e belongs to [1, h]，f∈[1,h]H is the sum of the number of indexes in the index layer and the number of indexes in the criterion layer;

a_effollowing a positive reciprocal matrix:

in the formula, a_efThe importance of the e index of the index layer/criterion layer to the f index of the index layer/criterion layer is measured.

5. The analytic hierarchy process-based power grid operation abnormality index cause correlation determination method according to claim 3, wherein calculating the weight coefficient of the determination matrix comprises:

calculating the jth index weight w according to a square root method_jComprises the following steps:

6. the analytic hierarchy process-based power grid operation anomaly index cause correlation determination method according to claim 4, wherein calculating the weight coefficient of the judgment matrix comprises:

calculating the f index weight w according to the square root method_fComprises the following steps:

7. the analytic hierarchy process-based power grid operation anomaly index cause correlation determination method according to claim 5, wherein the consistency check is performed according to a weight coefficient, and comprises:

calculating CI and CR:

wherein CI is the general consistency index of the judgment matrix of the target layer and the criterion layer, RI is the random consistency index of the judgment matrix of the target layer and the criterion layer, and lambda_maxThe maximum characteristic root of a judgment matrix of a target layer and a criterion layer is obtained, and x is n;

calculating lambda_max：

If CR is less than the set threshold, the judgment matrix passes the consistency check.

8. The analytic hierarchy process-based power grid operation abnormality index cause correlation determination method according to claim 1, wherein the cause correlation determination method comprises the following steps,

and performing consistency check according to the weight coefficient, wherein the consistency check comprises the following steps:

calculating CI and CR:

wherein, CI is the general consistency index of the judgment matrix of the index layer and the criterion layer, RI is the random consistency index of the judgment matrix of the index layer and the criterion layer, and lambda_maxThe judgment matrix is the maximum characteristic root of the judgment matrix of the index layer and the criterion layer, and x is h;

calculating lambda_max：

If CR is less than the set threshold, the judgment matrix passes the consistency check.

9. The analytic hierarchy process-based power grid operation anomaly index cause correlation determination method according to claim 6 or 7, wherein the RI takes values of:

determining the order of the matrix 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

；

The value of CR is 0.1.

10. The analytic hierarchy process-based power grid operation abnormality index cause correlation determination method according to claim 1, wherein the step of prioritizing the target layer, the criterion layer and the index layer and screening to obtain the maximum cause correlation of the power grid operation abnormality index includes:

sorting the priority in descending order: a target layer, a criterion layer and an index layer;

sequentially obtaining the sum of the weights of all indexes in the target layer, the sum of the weights of all indexes in the criterion layer and the sum of the weights of all indexes in the index layer;

and screening the maximum value among the sum of the weights of all the indexes in the target layer, the sum of the weights of all the indexes in the criterion layer and the sum of the weights of all the indexes in the index layer to obtain the level with the maximum correlation degree of the cause of the abnormal operation index of the power grid.

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