CN112966972A - Line loss data processing method, device and medium based on combined weighted TOPSIS - Google Patents

Line loss data processing method, device and medium based on combined weighted TOPSIS Download PDF

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CN112966972A
CN112966972A CN202110340396.4A CN202110340396A CN112966972A CN 112966972 A CN112966972 A CN 112966972A CN 202110340396 A CN202110340396 A CN 202110340396A CN 112966972 A CN112966972 A CN 112966972A
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index
weight
line loss
matrix
calculating
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李佳新
皇甫成
陈建华
朱正甲
梁吉
汪鸿
邱婷
孟菁
赵丹阳
陈广宇
徐凌燕
李胜雨
张宇
陈亮
黄微
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BEIJING BRON S&T Ltd
State Grid Corp of China SGCC
State Grid Jibei Electric Power Co Ltd
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BEIJING BRON S&T Ltd
State Grid Corp of China SGCC
State Grid Jibei Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063114—Status monitoring or status determination for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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Abstract

The invention provides a line loss data processing method, a device and a medium based on combined weighted TOPSIS, wherein the method comprises the following steps: establishing a line loss comprehensive calculation index system; acquiring data of the line loss of the regional power distribution network from the power distribution network according to the determined comprehensive calculation index system; calculating a first weight of each index by using an improved analytic hierarchy process according to the comprehensive calculation index system; calculating a second weight of each index based on the data of the line loss of the regional power distribution network by using an entropy weight method; combining the first weight and the second weight of each index to obtain a combined weight of each index; and obtaining a comprehensive calculation result by using a TOPSIS method according to the combined weight of each index and the collected data of the line loss of the regional power distribution network, and controlling a power grid operation system to reduce the line loss of the regional power grid. And the comprehensive calculation result is obtained by calculating the combination weight of each index and the collected regional power distribution network line loss data, so that the line loss estimation is more accurate and objective.

Description

Line loss data processing method, device and medium based on combined weighted TOPSIS
Technical Field
The invention relates to the technical field of data processing, in particular to a line loss data processing method, a device and a medium based on combined weighted TOPSIS.
Background
In the prior art, the assessment method of the line loss management level in the provincial and urban areas usually uses the comprehensive line loss rate as the assessment standard, or uses the combination of the comprehensive line loss rate, the partial voltage line loss rate and the loss line loss rate as the assessment standard. However, the line loss management of the power grid company is targeted at fine management, and the traditional evaluation method cannot meet the requirements, that is, objective factors affecting the line loss cannot be found.
In the prior art, one of the main methods at present is an analytic hierarchy process: the traditional evaluation method of the multi-dimensional indexes generally uses an analytic hierarchy process to carry out comprehensive evaluation, the analytic hierarchy process carries out hierarchical decomposition comparison judgment on a complex system with multiple targets and multiple criteria, the thought is simple and clear, a large amount of specific data is not needed, the importance degree of each index is influenced by determining the weight of each layer finally, but the analytic hierarchy process still has many problems. In the prior art, the nine-scale method or the three-scale method commonly used in the analytic hierarchy process has too strong subjective ambiguity, and generally has influence on the final result due to the subjective assumption of individuals, namely, the evaluation is not objective enough.
The other method is an entropy weight method: the entropy weight of each index is calculated by using the information entropy according to the variation degree of each index, and the weight of each index is corrected by the entropy weight, so that objective weight is obtained. The degree of dispersion of an index can be judged by using an entropy value, the smaller the information entropy value is, the larger the degree of dispersion of the index is, the larger the influence (namely weight) of the index on comprehensive evaluation is, and if the values of the index are all equal, the index does not play a role in the comprehensive evaluation, so that the evaluation calculation is not accurate enough.
Therefore, in the prior art, the calculation of monitoring the line loss of the power grid is not objective and accurate enough, so that the control accuracy of the power grid system is influenced.
Disclosure of Invention
The present invention provides the following technical solutions to overcome the above-mentioned drawbacks in the prior art.
A line loss data processing method based on combined weighted TOPSIS comprises the following steps:
step S1, analyzing factors influencing the line loss rate of the power distribution network in a certain area, and establishing a line loss comprehensive calculation index system under four dimensions of a planning level, a management level, an operation level and a technical level;
step S2, collecting data of the line loss of the regional power distribution network from the power distribution network according to the determined comprehensive calculation index system;
step S3, calculating a first weight of each index by using an improved analytic hierarchy process according to the comprehensive calculation index system;
step S4, calculating a second weight of each index based on the collected data of the line loss of the regional power distribution network by using an entropy weight method;
step S5, combining the first weight and the second weight of each index to obtain a combined weight of each index;
step S6, obtaining a comprehensive calculation result by using a TOPSIS method according to the combined weight of each index and the collected data of the line loss of the regional power distribution network;
and step S7, controlling the power grid operation system according to the comprehensive calculation result so as to reduce the power grid line loss of the region.
Further, the comprehensive calculation index system comprises: the indexes selected from the planning level are the power supply radius qualification rate and the 10kV distribution transformer reactive compensation rate, the indexes selected from the management level are the old low-voltage electric energy meter ratio, the metering fault error rate, the line loss abnormity handling rate and the line loss abnormity rate, the indexes selected from the operation level are the line heavy and light load proportion, the transformer heavy and light load proportion, the comprehensive voltage qualification rate and the power factor qualification rate, and the indexes selected from the technical level are the high loss distribution transformer proportion, the transformer substation reactive compensation device availability rate and the energy-saving main transformation rate.
Further, the operation of calculating the first weight of each index by using the improved analytic hierarchy process is as follows: firstly, a comparison matrix A, A is constructed based on each index in the comprehensive calculation index system by using an improved analytic hierarchy processijTo compare the elements in matrix a, i, j are the row and column values,
then, the importance ranking index r is calculated by adding the elements in the comparison matrix Ai：
Figure BDA0002999323960000031
In the formula: r isiRepresenting factor AiComparison of importance with all factors where rmax＝max{ri},rmin＝min{ri}；
Then, a judgment matrix B with element B is constructed according to the comparison matrix AijThe following formula is followed:
Figure BDA0002999323960000032
wherein the content of the first and second substances,
Figure BDA0002999323960000033
determining a transfer matrix C based on the decision matrix B, the elements C of whichijThe following formula is followed:
cij＝lgbij(i,j＝1,2,...,n)
finding an optimal transfer matrix D based on the transfer matrix C, the elements D of whichijThe following formula is followed:
Figure BDA0002999323960000041
obtaining a pseudo-optimal consistent matrix B ' from the optimal transfer matrix D, and obtaining elements B ' of the pseudo-optimal consistent matrix B 'ijThe following formula is followed:
Figure BDA0002999323960000042
finally, calculating the characteristic vector W of the quasi-optimal consistent matrix by using a sum-product method1jI.e. W1jFor a first weight of each determined index, W1jJ in (d) represents the jth index.
Further, the operation procedure of step S4 is:
calculating the specific gravity P of the index value of the ith item under the jth indexij，
Figure BDA0002999323960000043
Calculating the entropy e of the jth indexj，
Figure BDA0002999323960000044
Wherein k is 1/lnm;
calculating the entropy weight w of the jth indexjAs a second weight of the index,
Figure BDA0002999323960000045
after the second weights of all indexes are calculated, the second weights are written into a vector W2jIn, W2jJ in (d) represents the jth index.
Further, the first weight and the second weight of each index are combined to obtain a combined weight W of each indexj＝αW1j+(1-α)W2jAnd alpha is a weighting factor.
Further, the α method is calculated as:
establishing an objective function with the purpose of minimizing the deviation sum of squares of the first weight, the second weight and the combined weight
Figure BDA0002999323960000051
To calculate alpha.
Further, the operation of step S6 is:
carrying out non-dimensionalization processing on each index data, obtaining an evaluation matrix R by using a range transformation formula for a decision matrix X consisting of m rating schemes and n evaluation indexes, and calculating in the decision matrix X according to the types of the indexes:
Figure BDA0002999323960000052
or:
Figure BDA0002999323960000053
in the formula, xijIs an original value, yijFor normalized values, minxijMinimum value of the same index, maxxijThe evaluation matrix R is the maximum value of the same index and is formed by yijComposition is carried out;
determining a positive ideal solution and a negative ideal solution, and selecting a scheme consisting of optimal index values from each index as the positive ideal solution and the negative ideal solution;
determining the Euclidean distance between the positive ideal solution and the negative ideal solution:
Figure BDA0002999323960000054
Figure BDA0002999323960000055
in the formula (I), the compound is shown in the specification,
Figure BDA0002999323960000056
and
Figure BDA0002999323960000057
to evaluate weighted Euclidean distances, W, of the solution i from the positive and negative ideal solutionsjIs the combined weight;
calculating the relative closeness of each target as a comprehensive calculation result:
Figure BDA0002999323960000061
the size of the evaluation solution is used as a standard for evaluating the quality of the evaluation solution.
Furthermore, main indexes influencing the line loss of the power grid are determined according to the comprehensive calculation result, and the power grid operation system is controlled based on the main indexes so as to reduce the line loss of the power grid in the region.
The invention also provides a line loss data processing device based on the combined weighted TOPSIS, which comprises a processor, a memory and a display device, wherein the processor executes a program on the memory to realize any method.
The present invention also proposes a computer-readable storage medium having a computer program stored thereon, the processor executing the program on the storage medium to implement any of the above methods.
The invention relates to a line loss data processing method, a device and a medium based on combined weighted TOPSIS, wherein the method comprises the following steps: step S1, analyzing factors influencing the line loss rate of the power distribution network in a certain area, and establishing a line loss comprehensive calculation index system under four dimensions of a planning level, a management level, an operation level and a technical level; step S2, collecting data of the line loss of the regional power distribution network from the power distribution network according to the determined comprehensive calculation index system; step S3, calculating a first weight of each index by using an improved analytic hierarchy process according to the comprehensive calculation index system; step S4, calculating a second weight of each index based on the collected data of the line loss of the regional power distribution network by using an entropy weight method; step S5, combining the first weight and the second weight of each index to obtain a combined weight of each index; step S6, obtaining a comprehensive calculation result by using a TOPSIS method according to the combined weight of each index and the collected data of the line loss of the regional power distribution network; and step S7, controlling the power grid operation system according to the comprehensive calculation result so as to reduce the power grid line loss of the region. The invention combines the weight calculated by the analytic hierarchy process and the weight calculated by the entropy weight method, and then calculates a comprehensive calculation result by using the combined weight of each index and the collected line loss data of the regional power distribution network based on the TOPSIS method, so that the line loss estimation of the region is more accurate and objective, and human factors are avoided, thereby more accurately controlling the power grid to reduce the line loss, the indexes used by the invention are objective indexes, because the subjective indexes are difficult to control in the power grid control, namely, the indexes involved in the calculation of the invention are objective indexes, the invention uses the improved analytic hierarchy process to construct a comparison matrix based on each index in the comprehensive calculation index system and then calculates a judgment matrix to reduce the influence of human subjective judgment ambiguity on an evaluation result, and improves the accuracy of the calculation result, and in the calculation process, the invention obtains a quasi-optimal consistent matrix through the optimal transfer matrix, omits the consistency check process, simplifies the calculation process, improves the calculation efficiency, reduces the occupation of a processor, namely improves the calculation performance of a computer. According to the invention, the TOPSIS method is used, the comprehensive evaluation calculation of the line loss is carried out on the basis of the combination weight obtained by the combination calculation of the improved analytic hierarchy process and the entropy weight method and the collected data of the power grid system, so that the evaluation calculation of the line loss is more objective and accurate, dimensionless data conversion is carried out in the calculation process, and the accuracy of the line loss evaluation is improved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings.
Fig. 1 is a flowchart of a line loss data processing method based on combined weighted TOPSIS according to an embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a line loss data processing method based on combined weighted TOPSIS, which includes the following steps:
step S1, analyzing factors influencing the line loss rate of the power distribution network in a certain area, and establishing a line loss comprehensive calculation index system under four dimensions of a planning level, a management level, an operation level and a technical level;
step S2, collecting data of the line loss of the regional power distribution network from the power distribution network according to the determined comprehensive calculation index system;
step S3, calculating a first weight of each index by using an improved analytic hierarchy process according to the comprehensive calculation index system;
step S4, calculating a second weight of each index based on the collected data of the line loss of the regional power distribution network by using an entropy weight method;
step S5, combining the first weight and the second weight of each index to obtain a combined weight of each index;
step S6, obtaining a comprehensive calculation result by using a TOPSIS method according to the combined weight of each index and the collected data of the line loss of the regional power distribution network;
and step S7, controlling the power grid operation system according to the comprehensive calculation result so as to reduce the power grid line loss of the region.
The invention combines the weight calculated by the improved analytic hierarchy process with the weight calculated by the entropy weight method, and then calculates the comprehensive calculation result by using the combined weight of each index and the collected line loss data of the distribution network in the area based on the TOPSIS method, so that the line loss estimation of the area is more accurate and objective, and human factors are avoided, thereby more accurately controlling the power grid to reduce the line loss.
In one embodiment, the objective indexes, namely, the indexes under the comprehensive calculation index system, according to the present invention include: the indexes selected from the planning level are the power supply radius qualification rate and the 10kV distribution transformer reactive compensation rate, the indexes selected from the management level are the old low-voltage electric energy meter ratio, the metering fault error rate, the line loss abnormity handling rate and the line loss abnormity rate, the indexes selected from the operation level are the line heavy and light load proportion, the transformer heavy and light load proportion, the comprehensive voltage qualification rate and the power factor qualification rate, and the indexes selected from the technical level are the high loss distribution transformer proportion, the transformer substation reactive compensation device availability rate and the energy-saving main transformation rate.
In one embodiment, in step S2, after data of line loss of the regional distribution network is collected from the distribution network, the data quality is checked, and the data is screened, for example, a big data analysis method is used to clean the data and remove obviously abnormal data, and the data inspection, screening, and cleaning are well-established technologies.
In one embodiment, the operation of calculating the first weight of each index using the improved analytic hierarchy process is:
firstly, a comparison matrix A, A is constructed based on each index in the comprehensive calculation index system by using an improved analytic hierarchy processijFor comparing the elements in the matrix A, i and j are row and column values, then calculating the importance ranking index r by adding the elements in the comparison matrix Ai：
Figure BDA0002999323960000101
In the formula: r isiRepresenting factor AiAs a result of the comparison of the importance of all factors,wherein r ismax＝max{ri},rmin＝min{ri}。
Then, a judgment matrix B with element B is constructed according to the comparison matrix AijThe following formula is followed:
Figure BDA0002999323960000102
wherein the content of the first and second substances,
Figure BDA0002999323960000103
then, a transfer matrix C is obtained based on the judgment matrix B, the elements C of whichijThe following formula is followed:
cij＝lgbij(i,j＝1,2,...,n)
finding an optimal transfer matrix D based on the transfer matrix C, the elements D of whichijThe following formula is followed:
Figure BDA0002999323960000104
obtaining a pseudo-optimal consistent matrix B ' from the optimal transfer matrix D, and obtaining elements B ' of the pseudo-optimal consistent matrix B 'ijThe following formula is followed:
Figure BDA0002999323960000111
finally, calculating the characteristic vector W of the quasi-optimal consistent matrix by using a sum-product method1jI.e. W1jFor a first weight of each determined index, W1jJ in (d) represents the jth index.
The invention uses the improved analytic hierarchy process to construct the comparison matrix A based on each index in the comprehensive calculation index system, and then calculates the judgment matrix B to reduce the influence of man-made subjective judgment ambiguity on the evaluation result, thereby improving the accuracy of the calculation result.
In one embodiment, the calculation process of the entropy weight method, i.e. the operation process of step S4, is as follows: and determining a second weight of each index by using an entropy weight method according to the data of each acquired index:
calculating the specific gravity P of the index value of the ith item under the jth indexij，
Figure BDA0002999323960000112
Calculating the entropy e of the jth indexj，
Figure BDA0002999323960000113
Wherein k is 1/lnm;
calculating the entropy weight w of the jth indexjAs a second weight of the index,
Figure BDA0002999323960000114
after the second weights of all indexes are calculated, the second weights are written into a vector W2jIn, W2jJ in (d) represents the jth index.
In the invention, the second weight of each index is calculated by using the objective data of each index collected from the power grid system, so that the calculation of each index is objective and accurate, which is another important invention point of the invention.
In one embodiment, the first weight and the second weight of each index are combined to obtain a combined weight W of each indexj＝αW1j+(1-α)W2jAnd alpha is a weighting factor, wherein the alpha is calculated by the following method:
establishing an objective function with the purpose of minimizing the deviation sum of squares of the first weight, the second weight and the combined weight
Figure BDA0002999323960000121
To calculate alpha.
In the invention, the advantages of the analytic hierarchy process and the entropy weight process are comprehensively improved, so that an objective function is established by taking the minimum deviation square sum of the first weight, the second weight and the combined weight as the aim, and the alpha value is calculated when the target value is minimum, so that the weights are integrated with the advantages of the two methods, which is another important invention point of the invention.
In one embodiment, the operation of step S6 is:
because the evaluation indexes usually have different dimensions, the evaluation indexes need to be subjected to non-dimensionalization, the data of each index are subjected to non-dimensionalization, for a decision matrix X consisting of m rating schemes and n evaluation indexes, an evaluation matrix R is obtained by utilizing a range transformation formula, and in the decision matrix X, the calculation is carried out according to the types of the indexes:
Figure BDA0002999323960000122
or:
Figure BDA0002999323960000131
the type of the index can be divided into inherent objective data of the power grid, such as indexes of high loss distribution transformation ratio, substation reactive compensation device availability ratio, energy-saving main transformation ratio and the like, the index is calculated by using the formula (2), and the variable index such as comprehensive voltage qualification rate and power factor qualification rate is calculated by using the formula (1).
In the formula, xijIs an original value, yijFor normalized values, minxijMinimum value of the same index, maxxijThe evaluation matrix R is the maximum value of the same index and is formed by yijComposition is carried out;
determining a positive ideal solution and a negative ideal solution, and selecting a scheme consisting of optimal index values from each index as the positive ideal solution and the negative ideal solution;
determining the Euclidean distance between the positive ideal solution and the negative ideal solution:
Figure BDA0002999323960000132
Figure BDA0002999323960000133
in the formula (I), the compound is shown in the specification,
Figure BDA0002999323960000134
and
Figure BDA0002999323960000135
to evaluate weighted Euclidean distances, W, of the solution i from the positive and negative ideal solutionsjIs the combined weight;
calculating the relative closeness of each target as a comprehensive calculation result:
Figure BDA0002999323960000136
the size of the evaluation solution is used as a standard for evaluating the quality of the evaluation solution.
In the invention, the TOPSIS method is used, the comprehensive evaluation calculation of the line loss is carried out on the basis of the combination weight obtained by the combination calculation of the improved analytic hierarchy process and the entropy weight method and the collected data of the power grid system, so that the evaluation calculation of the line loss is more objective and accurate, and the dimensionless data conversion is carried out in the calculation process, thereby improving the accuracy of the line loss evaluation, which is another important invention point of the invention.
In one embodiment, main indexes influencing the line loss of the power grid are determined according to the comprehensive calculation result, and the power grid operation system is controlled based on the main indexes so as to reduce the line loss of the power grid in the region.
The invention also provides a line loss data processing device based on the combined weighted TOPSIS, which comprises a processor, a memory and a display device, wherein the processor executes a program on the memory to realize any method.
The present invention also proposes a computer-readable storage medium having a computer program stored thereon, the processor executing the program on the storage medium to implement any of the above methods.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present application.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made thereto without departing from the spirit and scope of the invention and it is intended to cover in the claims the invention as defined in the appended claims.

Claims (10)

1. A line loss data processing method based on combined weighted TOPSIS is characterized in that: the method comprises the following steps:
step S1, analyzing factors influencing the line loss rate of the power distribution network in a certain area, and establishing a line loss comprehensive calculation index system under four dimensions of a planning level, a management level, an operation level and a technical level;
step S2, collecting data of the line loss of the regional power distribution network from the power distribution network according to the determined comprehensive calculation index system;
step S3, calculating a first weight of each index by using an improved analytic hierarchy process according to the comprehensive calculation index system;
step S4, calculating a second weight of each index based on the collected data of the line loss of the regional power distribution network by using an entropy weight method;
step S5, combining the first weight and the second weight of each index to obtain a combined weight of each index;
step S6, obtaining a comprehensive calculation result by using a TOPSIS method according to the combined weight of each index and the collected data of the line loss of the regional power distribution network;
and step S7, controlling the power grid operation system according to the comprehensive calculation result so as to reduce the power grid line loss of the region.
2. The method of claim 1, wherein the comprehensive computing index system comprises: the indexes selected from the planning level are the power supply radius qualification rate and the 10kV distribution transformer reactive compensation rate, the indexes selected from the management level are the old low-voltage electric energy meter ratio, the metering fault error rate, the line loss abnormity handling rate and the line loss abnormity rate, the indexes selected from the operation level are the line heavy and light load proportion, the transformer heavy and light load proportion, the comprehensive voltage qualification rate and the power factor qualification rate, and the indexes selected from the technical level are the high loss distribution transformer proportion, the transformer substation reactive compensation device availability rate and the energy-saving main transformation rate.
3. The method of claim 2, wherein the operation of calculating the first weight for each indicator using modified analytic hierarchy process is:
firstly, a comparison matrix A, A is constructed based on each index in the comprehensive calculation index system by using an improved analytic hierarchy processijFor comparing the elements in the matrix A, i and j are row and column values, then calculating the importance ranking index r by adding the elements in the comparison matrix Ai：
Figure FDA0002999323950000021
In the formula: r isiRepresenting factor AiComparison of importance with all factors where rmax＝max{ri},rmin＝min{ri}；
Then, a judgment matrix B with element B is constructed according to the comparison matrix AijThe following formula is followed:
Figure FDA0002999323950000022
wherein the content of the first and second substances,
Figure FDA0002999323950000023
then, a transfer matrix C is obtained based on the judgment matrix B, the elements C of whichijThe following formula is followed:
cij＝lgbij(i,j＝1,2,...,n)
finding an optimal transfer matrix D based on the transfer matrix C, the elements D of whichijThe following formula is followed:
Figure FDA0002999323950000024
obtaining a pseudo-optimal consistent matrix B ' from the optimal transfer matrix D, and obtaining elements B ' of the pseudo-optimal consistent matrix B 'ijThe following formula is followed:
Figure FDA0002999323950000031
finally, calculating the characteristic vector W of the quasi-optimal consistent matrix by using a sum-product method1jI.e. W1jFor a first weight of each determined index, W1jJ in (d) represents the jth index.
4. The method according to claim 3, wherein the operation procedure of step S4 is as follows:
calculate j (th)Specific gravity P of index value of ith item under each indexij，
Figure FDA0002999323950000032
Calculating the entropy e of the jth indexj，
Figure FDA0002999323950000033
Wherein k is 1/lnm;
calculating the entropy weight w of the jth indexjAs a second weight of the index,
Figure FDA0002999323950000034
after the second weights of all indexes are calculated, the second weights are written into a vector W2jIn, W2jJ in (d) represents the jth index.
5. The method of claim 4, wherein the first weight and the second weight of each indicator are combined to obtain a combined weight W of each indicatorj＝αW1j+(1-α)W2jAnd alpha is a weighting factor.
6. The method of claim 5, wherein the alpha method is calculated as:
establishing an objective function with the purpose of minimizing the deviation sum of squares of the first weight, the second weight and the combined weight
Figure FDA0002999323950000035
To calculate alpha.
7. The method according to claim 6, wherein the operation of step S6 is:
carrying out non-dimensionalization processing on each index data, obtaining an evaluation matrix R by using a range transformation formula for a decision matrix X consisting of m rating schemes and n evaluation indexes, and calculating in the decision matrix X according to the types of the indexes:
Figure FDA0002999323950000041
or:
Figure FDA0002999323950000042
in the formula, xijIs an original value, yijMin x is the normalized valueijMinimum value of the same index, max xijThe evaluation matrix R is the maximum value of the same index and is formed by yijComposition is carried out;
determining a positive ideal solution and a negative ideal solution, and selecting a scheme consisting of optimal index values from each index as the positive ideal solution and the negative ideal solution;
determining the Euclidean distance between the positive ideal solution and the negative ideal solution:
Figure FDA0002999323950000043
Figure FDA0002999323950000044
in the formula (I), the compound is shown in the specification,
Figure FDA0002999323950000045
and
Figure FDA0002999323950000046
to evaluate the solution i from the positive ideal solution sumWeighted Euclidean distance, W, of negative ideal solutionjIs the combined weight;
calculating the relative closeness of each target as a comprehensive calculation result:
Figure FDA0002999323950000051
the size of the evaluation solution is used as a standard for evaluating the quality of the evaluation solution.
8. The method of claim 7, wherein a primary index affecting the grid line loss is determined from the combined calculation, and the grid operating system is controlled based on the primary index to reduce the grid line loss in the region.
9. A combined weighted TOPSIS based line loss data processing apparatus, characterized in that the apparatus comprises a processor, a memory and a display device, the processor executing a program on the memory to implement the method of any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program, the processor executing the program on the storage medium to implement the method of any one of claims 1-8.
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Cited By (3)

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CN113762751A (en) * 2021-08-30 2021-12-07 国网冀北电力有限公司电力科学研究院 Unit power regulation parameter weight determination method and device
CN113964846A (en) * 2021-09-10 2022-01-21 国网浙江宁波市鄞州区供电有限公司 Dynamic reactive power compensation site selection method suitable for multi-feed-in direct current system
CN115545440A (en) * 2022-09-23 2022-12-30 国网冀北电力有限公司经济技术研究院 Differential evaluation method for bidding project quantity and project settlement quantity construction cost of power transmission and transformation project

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113762751A (en) * 2021-08-30 2021-12-07 国网冀北电力有限公司电力科学研究院 Unit power regulation parameter weight determination method and device
CN113964846A (en) * 2021-09-10 2022-01-21 国网浙江宁波市鄞州区供电有限公司 Dynamic reactive power compensation site selection method suitable for multi-feed-in direct current system
CN113964846B (en) * 2021-09-10 2024-04-02 国网浙江宁波市鄞州区供电有限公司 Dynamic reactive compensation site selection method suitable for multi-feed direct current system
CN115545440A (en) * 2022-09-23 2022-12-30 国网冀北电力有限公司经济技术研究院 Differential evaluation method for bidding project quantity and project settlement quantity construction cost of power transmission and transformation project

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