CN110795692A - Active power distribution network operation state evaluation method - Google Patents
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Abstract
An active power distribution network operation state evaluation method comprises the following steps: constructing an active power distribution network running state evaluation index system; carrying out consistency processing on the evaluation indexes; determining optimized combination weight; performing distance comprehensive evaluation on the running state of each evaluation object, and giving a relative quality sequence of the comprehensive running state of each evaluation object; comprehensive evaluation of attribute intervals: when the running state of the active power distribution network is analyzed by using an attribute interval algorithm, an attribute measure interval matrix needs to be constructed, and then a final evaluation grade is determined by using a grade characteristic value method. The invention has the advantages that: 1. an evaluation index system representing the running state of the active power distribution network is established; 2. combining subjective weight based on an analytic hierarchy process and objective weight based on a coefficient of variation method by adopting least square optimization; 3. by utilizing an attribute interval algorithm, the characteristics of fuzziness in the running state of the active power distribution network are fully considered, complete membership information is considered, and finally, the obtained comprehensive evaluation result of each index is scientific and effective.
Description
Technical Field
The invention relates to an active power distribution network running state evaluation method based on combination weight, distance comprehensive evaluation and attribute interval algorithm, and belongs to the field of electrical engineering.
Background
The development of the current society can not leave electric power, the self-healing control of the power distribution network is urgently needed to meet the increasing requirements of power users on the power supply of the power distribution network, and the premise that the self-healing control of the power distribution network is achieved is to grasp the running state of the power distribution network.
The self-healing control can endow an Active Distribution Network (ADN) with stronger self-healing capability, so that the ADN can always operate in a good state. The operation state of the active power distribution network is influenced by the environment where the active power distribution network is located, the operation environment is very complex, and the influence factors are numerous. The indexes representing the running state of the power distribution network are very many, and in order to achieve the aim of guiding a reasonable control strategy through running state evaluation, a reasonable evaluation index system, an index model and an evaluation method are firstly established aiming at the running characteristics of the active power distribution network to realize the evaluation of the running state of the active power distribution network, which is the premise and key content for self-healing control.
Currently, research hotspots related to the active power distribution network mainly focus on directions of distributed energy access planning, electric energy quality monitoring devices, active scheduling, control and optimization investment, economic benefits and the like, and research results of an operation state comprehensive evaluation method are few. The invention patent with the application number of 201810885635 provides a comprehensive evaluation method for the power quality of an active power distribution network, and the main idea of the comprehensive evaluation method is only aimed at the comprehensive evaluation of the power quality problem of the active power distribution network; the invention patent with the application number of 201810013969 provides a reliability evaluation method for an active power distribution network containing a distributed power supply based on a TOPSIS method, but the reliability evaluation method does not refer to the operation state indexes of the active power distribution network except the reliability; the invention patent with the application number of 201711023109 provides an active power distribution network operation situation evaluation method based on a utility function, but the weight determination method is still a single analytic hierarchy process with strong subjectivity to determine the weight. The method is used for researching various characteristics influencing the running state, multiple indexes, multiple attributes and fuzziness of the active power distribution network and the problem that subjectivity is too high in traditional evaluation weight determination, an active power distribution network running state evaluation index system is established, subjective and objective weights are optimized and combined from two angles of relative superiority and inferiority of internal objects of the running state of the power distribution network and specific index levels, the running state of the active power distribution network is comprehensively evaluated by adopting a distance comprehensive evaluation method and an attribute interval algorithm, and evaluation results of all indexes of all evaluation objects are given through a radar map.
Disclosure of Invention
The invention provides an active power distribution network operation state evaluation method aiming at overcoming the defects in the prior art.
The method realizes the evaluation of the running state of the active power distribution network with Distributed Generation (DG) access, needs to process basic data of the running of the power distribution network, and calculates a reasonable combined weight value. The method not only gives the grades of various running state indexes of the evaluation objects, but also gives the relative quality sequence of the comprehensive running state of the evaluation objects. An evaluation method aiming at the characteristics of multilevel and strong ambiguity of the running state of the active power distribution network and the problem of over-strong subjectivity of determination of the weight coefficient in the traditional evaluation method of the running state of the power distribution network is designed.
In order to achieve the purpose, the invention respectively provides an active power distribution network running state comprehensive evaluation method based on an Analytic Hierarchy Process (AHP) -variation coefficient method and a distance comprehensive evaluation method and an active power distribution network running state comprehensive evaluation model based on a least square and attribute interval algorithm.
An active power distribution network operation state evaluation method comprises the following steps:
1. constructing an active power distribution network operation state evaluation index system: the method comprises the steps that the operation state of an active power distribution network is divided according to an index system construction principle of systematicness, objectivity, scientificity and practicability, the operation state of the active power distribution network is specifically divided into five operation states of an emergency state, a state needing to be recovered, an abnormal state, a normal state and an optimization state, whether the operation state of a target active power distribution network is healthy or not is evaluated from the aspects of safety, reliability, goodness, economy, adaptability and network performance so as to reflect the state characteristics of the health degree of the target active power distribution network, and an evaluation index system of the operation state of the target active power distribution network is constructed;
2. the evaluation index consistency processing: in each evaluation index, the data type, the dimension and the change interval of the index value are different, the evaluation index contains different characteristic indexes of 'maximum type', 'extremely small' and 'intermediate type', each index item needs to be subjected to dimensionless and unification treatment, each index item is uniformly converted into 'maximum type' index, and the corresponding numerical value in the original data matrix is changed;
for the "very small" index, let
x*＝xmax-x (1)
For the "centered" index, order
In the formula: x is the original value of the evaluation index, x*To equalize the evaluation index values after the processing, xmin、xmaxAnd xoptRespectively representing an allowable minimum value, an allowable maximum value and an ideal value of the evaluation index x;
3. determining optimized combining weight: respectively adopting an Analytic Hierarchy Process (AHP) and a variation coefficient process to calculate to obtain a subjective weight and an objective weight, and then respectively combining the two weight vectors by a combination weight method based on objective correction subjectivity and a least square optimization method to obtain a combination weight;
step 301, obtaining subjective index weight by an AHP method: the AHP method gives out importance comparison between every two indexes according to expert opinions, and a judgment matrix is constructed according to the relative importance between the indexes; after the judgment matrix is constructed, consistency check is carried out on the judgment matrix, and a check result is obtained by a formula (3):
in the formula: CI represents the result value of the consistency check, lambdamaxRepresenting the maximum eigenvalue of the judgment matrix, and n representing the order of the judgment matrix;
step 302, obtaining objective index weight by using a variation coefficient method: the coefficient of variation method directly utilizes the information in the actual data, and the weight of the index is obtained through calculation of a mathematical tool, and the core of the coefficient of variation method is that the larger weight is distributed to the index with larger standard deviation; because the dimensions of each index item in the evaluation index system are different, the variation coefficient is needed to measure the difference degree of the value, and the variation coefficient of each index item is shown as the formula (4):
in the formula: b isiThe coefficient of variation of the ith index term is expressed,
represents the average value of the i-th index term, siThe standard deviation of the index of the ith item is expressed, m represents the total number of index items, i belongs to [1, m ∈]；
Step 303, calculating the subjective combination weight based on the objective correction: the advantages of the objective weighting method are utilized to correct the disadvantages of the subjective weighting method, so that the combination weight can take the advantages of the subjective weight and the objective weight into consideration; determining the importance ratio of adjacent index terms according to the standard deviation of each index term by the formula (5):
in the formula: r isiImportance expert assignment, s, representing the i-th indexi-1Is the standard deviation of the index of the i-1;
r calculated from the formula (5)iValue, which is calculated using equation (6) to obtain its combined weight vi(ii) a And (3) sequentially calculating and obtaining the combination weight of the indexes of i-1, i-2, 3 and 2 by using a formula (7):
vi-1＝ri×vi(7)
in the formula: v. ofi、vi-1The combination weight of objective correction subjectivity of the index of the ith item and the (i-1) th item is expressed, wherein k belongs to the [ s, m ]]，s∈[2,m]；
Step 304, performing least square optimization combination on the subjective and objective weights by using a genetic function algorithm, and writing a fitness function, namely a least square optimization model, as shown in formula (8):
in the formula: h (W) is a constraint function, wiFor the ith index, the comprehensive weight value U after least square optimizationiAnd U'iThe subjective weight value and the objective weight value, x, of the ith index* ijData of the jth evaluation moment of the ith index after the consistency processing is carried out;
4. and (3) performing distance comprehensive evaluation on the running state of each evaluation object: giving a relative quality sequence of the comprehensive operation state of each evaluation object;
step 401, constructing a planning evaluation matrix: the data matrix after the consistency transformation is obtained from the consistency index value obtained by the formula (2) is shown as a formula (9):
X*＝(x* ij)m×n(9)
in the formula: x*Representing the data matrix after the uniform transformation;
data matrix X is obtained by equations (10) and (11)*Processing to obtain a planning evaluation matrix:
Y*＝(y* ij)m×n(11)
in the formula: y is* ijFor the data of the jth evaluation time of the ith index after the planning process, Y*Representing the planned data matrix;
step 402, constructing a weighted planning evaluation matrix: combining the combination weight based on the objective correction subjectivity obtained in the step 303 with a planning evaluation matrix to obtain a weighted planning matrix, as shown in formula (12):
zij＝vi×y* ij(12)
in the formula: z is a radical ofijThe weighted and planned data of the jth evaluation moment of the ith index is obtained;
step 403, determining an evaluation reference sample: selecting the optimal sample and the worst sample as reference samples, corresponding to the index consistency processing, as shown in formula (13), taking the maximum value of the index as the value of the optimal sample point, and taking the minimum value of the index as the value of the worst sample point:
in the formula: z+For the best sample of all index terms, Z-The worst sample of all index items;
and
respectively and sequentially representing the best sample and the worst sample of the index of the ith item, i belongs to [1, m ∈ [ ]]；
Step 404, determining the relative distance between each evaluation object and the sample and calculating the optimal proximity: as shown in equations (15) and (16), the relative distance between the actual value of each index and the best reference object and the relative distance between the actual value of each index and the worst reference object are calculated respectively:
in the formula:
and
respectively an ideal sample distance and a negative ideal sample distance of the ith index item;
from equation (17), the relative proximity of each evaluation object to the best reference object is calculated:
in the formula: ciThe optimal closeness of the ith index item;
finally, sequencing all the evaluation objects according to the optimal proximity to obtain a good and bad sequence;
5. comprehensive evaluation of attribute intervals: when the running state of the active power distribution network is analyzed by using an attribute interval algorithm, an attribute measure interval matrix needs to be constructed, the meaning of the attribute measure interval matrix is the degree that the running state of the active power distribution network at a certain moment belongs to a certain grade, and then a grade characteristic value method is used for determining the final evaluation grade;
step 501, constructing an attribute measurement interval matrix of the evaluation sample to the running state, wherein the form is shown as formula (18):
in the formula: [ mu ] of(low)tk,μ(up)tk]Evaluating the sample x for the t-th time instantiRelative to the attribute measurement interval of the kth operation state, T represents the total number of evaluation moments, and K represents the total number of operation statesCounting; mu.s(low)tkAnd mu(up)tkRespectively a lower bound attribute measure and an upper bound attribute measure of a kth state which is affiliated to the t moment;
step 502, obtaining the comprehensive attribute measure of each index of each object to be evaluated: calculating the average value of the upper bound attribute measure and the lower bound attribute measure to obtain an evaluation sample xiMeasuring the comprehensive attribute belonging to the kth running state;
step 503, determining an index evaluation grade: by using a level characteristic value method, the distortion hidden danger generated when fuzzy evaluation is carried out based on the maximum membership principle is avoided by using complete membership information;
step 504, using the radar map to show the evaluation grade of each target layer index of each evaluated object: in the radar map, the outer solid line represents the best performance of all the evaluation objects in the target layer, the inner solid line represents the worst performance of all the evaluation objects in the target layer, and the middle shaded area represents the performance in each target layer at the evaluation time.
The invention has the following beneficial effects: 1. an evaluation index system representing the running state of the active power distribution network is established; 2. combining subjective weight based on an analytic hierarchy process and objective weight based on a coefficient of variation method by adopting least square optimization; 3. by utilizing an attribute interval algorithm, the characteristics of fuzziness in the running state of the active power distribution network are fully considered, complete membership information is considered, and finally, the obtained comprehensive evaluation result of each index is scientific and effective.
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FIG. 1 is a flow chart of an embodiment of the method of the present invention.
Fig. 2 is an evaluation index system diagram of the operating state of the active power distribution network.
Fig. 3a to 3h are radar maps for estimating the operating state of the active power distribution network according to the present invention, where fig. 3a is a radar map display of an estimation sample 1, fig. 3b is a radar map display of an estimation sample 2, fig. 3c is a radar map display of an estimation sample 3, fig. 3d is a radar map display of an estimation sample 4, fig. 3e is a radar map display of an estimation sample 5, fig. 3f is a radar map display of an estimation sample 6, and fig. 3g is a radar map display of an estimation sample 7, fig. 3h is a radar map display of an estimation sample 8.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited to these examples. The flow chart of the implementation of the invention in the embodiment is shown in the attached figure 1.
An active power distribution network operation state evaluation method comprises the following steps:
1. constructing an active power distribution network operation state evaluation index system: the method comprises the steps that the operation state of an active power distribution network is divided according to an index system construction principle of systematicness, objectivity, scientificity and practicability, the operation state of the active power distribution network is specifically divided into five operation states of an emergency state, a state needing to be recovered, an abnormal state, a normal state and an optimization state, whether the operation state of a target active power distribution network is healthy or not is evaluated from the aspects of safety, reliability, goodness, economy, adaptability and network performance so as to reflect the state characteristics of the health degree of the target active power distribution network, and an evaluation index system of the operation state of the target active power distribution network is constructed;
specifically, in the embodiment, the constructed evaluation index system of the running state of the active power distribution network is shown in fig. 2; in an evaluation system base layer, safety indexes comprise overvoltage, low voltage and overload, reliability indexes comprise fault probability and load loss risk, goodness comprises voltage deviation and feeder line power factors, economic indexes comprise network loss rate, adaptability comprises load isolated power supply capacity, reactive power shortage and line connection capacity, and network comprises attacked frequency, attacked severity and information transmission loopholes;
2. the evaluation index consistency processing: in each evaluation index, the data type, the dimension and the change interval of the index value are different, the evaluation index contains different characteristic indexes of 'maximum type', 'extremely small' and 'intermediate type', each index item needs to be subjected to dimensionless and unification treatment, each index item is uniformly converted into 'maximum type' index, and the corresponding numerical value in the original data matrix is changed; for the 'very small' index, the transformation method is shown as a formula (1), and for the 'middle' index, the transformation method is shown as a formula (2);
in the embodiment, the electric energy quality index data to be evaluated obtained after the uniformization treatment is shown in table 1;
3. determining optimized combining weight: respectively adopting an Analytic Hierarchy Process (AHP) and a variation coefficient process to calculate to obtain a subjective weight and an objective weight, and then respectively combining the two weight vectors by a combination weight method based on objective correction subjectivity and a least square optimization method to obtain a combination weight;
step 301, obtaining subjective index weight by an AHP method: the AHP method gives out importance comparison between every two indexes according to expert opinions, and a judgment matrix is constructed according to the relative importance between the indexes; after the judgment matrix is constructed, consistency check is needed to be carried out on the judgment matrix, and a check result is obtained by a formula (3);
TABLE 1 evaluation index values at respective times after alignment
Step 302, obtaining objective index weight by using a variation coefficient method: the coefficient of variation method directly utilizes the information in the actual data, and the weight of the index is obtained through calculation of a mathematical tool, and the core of the coefficient of variation method is that the larger weight is distributed to the index with larger standard deviation; because the dimensions of each index item in the evaluation index system are different, the variation coefficient is used for measuring the difference degree of the value, and the variation coefficient of each index item is shown as a formula (4);
step 303, calculating the subjective combination weight based on the objective correction: the advantages of the objective weighting method are utilized to correct the disadvantages of the subjective weighting method, so that the combination weight can take the advantages of the subjective weight and the objective weight into consideration; determining the importance ratio of adjacent index items through a formula (5) according to the standard deviation of each index item;
r calculated from the formula (5)iValue, which is calculated using equation (6) to obtain its combined weight vi(ii) a Sequentially calculating by using a formula (7) to obtain the (i-1) th and (i-2) th,3, 2 indexes of combination weight;
step 304, performing least square optimization combination on the subjective and objective weights by using a genetic function algorithm, and compiling a fitness function, namely a least square optimization model, as shown in a formula (8);
in the embodiment, the combined weight result of each index obtained after the processing in step 3 is shown in table 2;
table 2 evaluation of secondary index comprehensive weight of operation state of active power distribution network
4. And (3) performing distance comprehensive evaluation on the running state of each evaluation object: giving a relative quality sequence of the comprehensive operation state of each evaluation object;
step 401, constructing a planning evaluation matrix: obtaining a data matrix after the consistency transformation according to the consistency index value obtained by the formula (2) and shown by a formula (9);
data matrix X is obtained by equations (10) and (11)*Processing to obtain a planning evaluation matrix;
step 402, constructing a weighted planning evaluation matrix: combining the objective correction subjective-based combination weight obtained in the step 303 with a planning evaluation matrix to obtain a weighted planning matrix, as shown in a formula (12);
step 403, determining an evaluation reference sample: selecting an optimal sample and a worst sample as reference samples, corresponding to index unification treatment, and taking an index maximum value as a value of an optimal sample point and an index minimum value as a value of a worst sample point as shown in a formula (13);
step 404, determining the relative distance between each evaluation object and the sample and calculating the optimal proximity: calculating the relative distance between each index actual value and the best reference object and the relative distance between each index actual value and the worst reference object respectively as shown in formulas (15) and (16); calculating the relative proximity of each evaluation object to the optimal reference object by formula (17); finally, sequencing all the evaluation objects according to the optimal proximity to obtain a good and bad sequence;
in the embodiment, the comprehensive evaluation result of the distance between the running states of the active power distribution network at each evaluation time obtained after the processing of the step 4 is shown in table 3;
table 3 comprehensive evaluation and evaluation result of distance between operation states of active power distribution network
5. Comprehensive evaluation of attribute intervals: when the running state of the active power distribution network is analyzed by using an attribute interval algorithm, an attribute measure interval matrix needs to be constructed, the meaning of the attribute measure interval matrix is the degree that the running state of the active power distribution network at a certain moment belongs to a certain grade, and then a grade characteristic value method is used for determining the final evaluation grade;
step 501, constructing an attribute measurement interval matrix of the evaluation sample to the running state, wherein the form is shown as formula (18);
step 502, obtaining the comprehensive attribute measure of each index of each object to be evaluated: calculating the average value of the upper bound attribute measure and the lower bound attribute measure to obtain an evaluation sample xiMeasuring the comprehensive attribute belonging to the kth running state;
in the embodiment, the evaluation comprehensive attribute measures of five comprehensive states of emergency, recovery, abnormal, normal and optimized at each evaluation moment of the active power distribution network are shown in table 4;
step 503, determining an index evaluation grade: by using a level characteristic value method, the distortion hidden danger generated when fuzzy evaluation is carried out based on the maximum membership principle is avoided by using complete membership information;
in the embodiment, the criterion layer evaluation index calculation result and the operation state identification result of the active power distribution network at each evaluation moment are respectively shown in tables 5 and 6;
step 504, using the radar map to show the evaluation grade of each target layer index of each evaluated object: in a radar map, the outer solid line represents the best performance of all the evaluation objects in the target layer, the inner solid line represents the worst performance of all the evaluation objects in the target layer, and the middle shaded area represents the performance in each target layer at the evaluation moment;
table 4 comprehensive state evaluation comprehensive attribute measurement of active power distribution network
TABLE 5 evaluation index calculation results of criterion layer
Table 6 identification result of operation state of active power distribution network
Step 504, using the radar chart to represent the evaluation grade of each target layer index of each evaluated object: in a radar map, the outer solid line represents the best performance of all the evaluation objects in the target layer, the inner solid line represents the worst performance of all the evaluation objects in the target layer, and the middle shaded area represents the performance of the evaluation time in each target layer;
in the embodiment, radar maps of comprehensive evaluation results of the active power distribution network on six target layers of safety, reliability, goodness, economy, adaptability and network performance are sequentially shown in fig. 3a to fig. 3h from time 1 to time 8 of a sample to be evaluated.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.
Claims (1)
1. An active power distribution network operation state evaluation method comprises the following steps:
step 1, constructing an evaluation index system of the running state of the active power distribution network: dividing the operation state of the active power distribution network according to an index system construction principle of systematicness, objectivity, scientificity and practicability, and specifically dividing the operation state into five operation states of an emergency state, a state needing to be recovered, an abnormal state, a normal state and an optimization state; evaluating whether the running state of the target active power distribution network is healthy or not from six aspects of safety, reliability, goodness, economy, adaptability and network performance to reflect the state characteristics of the health degree of the target active power distribution network, and constructing an evaluation index system of the running state of the target active power distribution network;
step 2, evaluation index consistency treatment: in each evaluation index, the data type, the dimension and the change interval of the index value are different, the evaluation index contains different characteristic indexes of 'maximum type', 'extremely small' and 'intermediate type', each index item needs to be subjected to dimensionless and unification treatment, each index item is uniformly converted into 'maximum type' index, and the corresponding numerical value in the original data matrix is changed;
for the "very small" index, let
x*＝xmax-x (1)
For the "centered" index, order
In the formula: x is the original value of the evaluation index, x*To equalize the evaluation index values after the processing, xmin、xmaxAnd xoptRespectively representing an allowable minimum value, an allowable maximum value and an ideal value of the evaluation index x;
step 3, determining optimized combination weight: respectively adopting an Analytic Hierarchy Process (AHP) and a variation coefficient process to calculate to obtain a subjective weight and an objective weight, and then respectively combining the two weight vectors by a combination weight method based on objective correction subjectivity and a least square optimization method to obtain a combination weight;
step 301, obtaining subjective index weight by an AHP method: the AHP method gives out importance comparison between every two indexes according to expert opinions, and a judgment matrix is constructed according to the relative importance between the indexes; after the judgment matrix is constructed, consistency check is carried out on the judgment matrix, and a check result is obtained by a formula (3):
in the formula: CI represents the result value of the consistency check, lambdamaxRepresenting the maximum eigenvalue of the judgment matrix, and n representing the order of the judgment matrix;
step 302, obtaining objective index weight by using a variation coefficient method: the coefficient of variation method directly utilizes the information in the actual data, and the weight of the index is obtained through calculation of a mathematical tool, and the core of the coefficient of variation method is that the larger weight is distributed to the index with larger standard deviation; because the dimensions of each index item in the evaluation index system are different, the variation coefficient is needed to measure the difference degree of the value, and the variation coefficient of each index item is shown as the formula (4):
in the formula: b isiThe coefficient of variation of the ith index term is expressed,represents the average value of the i-th index term, siThe standard deviation of the index of the ith item is expressed, m represents the total number of index items, i belongs to [1, m ∈]；
Step 303, calculating the subjective combination weight based on the objective correction: the advantages of the objective weighting method are utilized to correct the disadvantages of the subjective weighting method, so that the combination weight can take the advantages of the subjective weight and the objective weight into consideration; determining the importance ratio of adjacent index terms according to the standard deviation of each index term by the formula (5):
in the formula: r isiImportance expert assignment, s, representing the i-th indexi-1Is the standard deviation of the index of the i-1;
r calculated from the formula (5)iValue, which is calculated using equation (6) to obtain its combined weight vi(ii) a And (3) sequentially calculating and obtaining the combination weight of the indexes of i-1, i-2, 3 and 2 by using a formula (7):
vi-1＝ri×vi(7)
in the formula: v. ofi、vi-1The combination weight of objective correction subjectivity of the index of the ith item and the (i-1) th item is expressed, wherein k belongs to the [ s, m ]]，s∈[2,m]；
Step 304, performing least square optimization combination on the subjective and objective weights by using a genetic function algorithm, and writing a fitness function, namely a least square optimization model, as shown in formula (8):
in the formula: h (W) is a constraint function, wiFor the ith index, the comprehensive weight value U after least square optimizationiAnd U'iThe subjective weight value and the objective weight value, x, of the ith index* ijData of the jth evaluation moment of the ith index after the consistency processing is carried out;
step 4, carrying out distance comprehensive evaluation on the running state of each evaluation object: giving a relative quality sequence of the comprehensive operation state of each evaluation object;
step 401, constructing a planning evaluation matrix: the data matrix after the consistency transformation is obtained from the consistency index value obtained by the formula (2) is shown as a formula (9):
X*＝(x* ij)m×n(9)
in the formula: x*Representing the data matrix after the uniform transformation;
data matrix X is obtained by equations (10) and (11)*Processing to obtain a planning evaluation matrix:
Y*＝(y* ij)m×n(11)
in the formula: y is* ijFor the data of the jth evaluation time of the ith index after the planning process, Y*Representing the planned data matrix;
step 402, constructing a weighted planning evaluation matrix: combining the combination weight based on the objective correction subjectivity obtained in the step 303 with a planning evaluation matrix to obtain a weighted planning matrix, as shown in formula (12):
zij＝vi×y* ij(12)
in the formula: z is a radical ofijThe weighted and planned data of the jth evaluation moment of the ith index is obtained;
step 403, determining an evaluation reference sample: selecting the optimal sample and the worst sample as reference samples, corresponding to the index consistency processing, as shown in formula (13), taking the maximum value of the index as the value of the optimal sample point, and taking the minimum value of the index as the value of the worst sample point:
in the formula: z+For the best sample of all index terms, Z-The worst sample of all index items;
and
respectively and sequentially representing the best sample and the worst sample of the index of the ith item, i belongs to [1, m ∈ [ ]]；
Step 404, determining the relative distance between each evaluation object and the sample and calculating the optimal proximity: as shown in equations (15) and (16), the relative distance between the actual value of each index and the best reference object and the relative distance between the actual value of each index and the worst reference object are calculated respectively:
in the formula:andrespectively an ideal sample distance and a negative ideal sample distance of the ith index item;
from equation (17), the relative proximity of each evaluation object to the best reference object is calculated:
in the formula: ciThe optimal closeness of the ith index item;
finally, sequencing all the evaluation objects according to the optimal proximity to obtain a good and bad sequence;
step 5, comprehensive evaluation of attribute intervals: when the running state of the active power distribution network is analyzed by using an attribute interval algorithm, an attribute measure interval matrix needs to be constructed, the meaning of the attribute measure interval matrix is the degree that the running state of the active power distribution network at a certain moment belongs to a certain grade, and then a grade characteristic value method is used for determining the final evaluation grade;
step 501, constructing an attribute measurement interval matrix of the evaluation sample to the running state, wherein the form is shown as formula (18):
in the formula: [ mu ] of(low)tk,μ(up)tk]Evaluating the sample x for the t-th time instantiRelative to the attribute measurement interval of the kth running state, T represents the total number of evaluation moments, and K represents the total number of running states; mu.s(low)tkAnd mu(up)tkRespectively a lower bound attribute measure and an upper bound attribute measure of a kth state which is affiliated to the t moment;
step 502, obtaining the comprehensive attribute measure of each index of each object to be evaluated: calculating the average value of the upper bound attribute measure and the lower bound attribute measure to obtain an evaluation sample xiMeasuring the comprehensive attribute belonging to the kth running state;
step 503, determining an index evaluation grade: by using a level characteristic value method, the distortion hidden danger generated when fuzzy evaluation is carried out based on the maximum membership principle is avoided by using complete membership information;
step 504, using the radar map to show the evaluation grade of each target layer index of each evaluated object: in the radar map, the outer solid line represents the best performance of all the evaluation objects in the target layer, the inner solid line represents the worst performance of all the evaluation objects in the target layer, and the middle shaded area represents the performance in each target layer at the evaluation time.
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