Comprehensive evaluation method for utilization rate of power distribution network equipment

Abstract

The invention discloses a comprehensive evaluation method for the utilization rate of power distribution network equipment. An order relation analysis method, an index correlation-based index weight determination method (CRITIC), an improved grey relational degree method and an ideal solution are combined. Firstly, subjective weights and objective weights of all indexes are calculated through an order relation analysis method and a CRITIC method, and the subjective weights and the objective weights are combined and weighted to obtain comprehensive weights. And then, comprehensively evaluating the utilization rate of the power distribution network equipment by adopting an improved grey comprehensive evaluation model in combination with an ideal solution method and a grey relational degree method. According to the invention, the comprehensive evaluation object can be evaluated more comprehensively; an improved grey comprehensive evaluation model is adopted, factors such as importance, correlation and interaction of all indexes are fully considered, and the advantages of a traditional grey evaluation model and an ideal solution are combined. Through the model, the quality degree of a comprehensive evaluation object can be obtained, and the balance of indexes is considered, so that the evaluation result is more accurate and objective.

Description

Technical Field

[0001] The present invention relates to distribution network technology, and in particular to a comprehensive evaluation method for utilization of distribution network equipment. Background Art

[0002] At present, the utilization rate of power grid equipment is closely related to economic efficiency, which has attracted widespread attention in recent years. As a key infrastructure to ensure regional economic development and improve people's living standards, the distribution network directly faces end users. The utilization rate of distribution network equipment is low.

[0003] At present, power supply companies usually use indicators such as line loss rate, voltage qualification rate and power supply reliability rate to evaluate the performance of distribution systems. The research on distribution network equipment utilization covers a wide range, from indicator system construction to multi-time scale analysis, to data processing and analysis, comprehensive governance technology and prediction and early warning methods. Scholars have conducted in-depth research on the changing laws and influencing factors of equipment utilization through traditional methods and intelligent technologies. In terms of indicator system, a comprehensive indicator system was constructed, taking into account both subjective and objective factors to ensure the comprehensiveness of the evaluation. In terms of multi-time scale analysis, the influencing factors such as long-term changes, seasonal changes and periodic changes in equipment utilization were investigated from the perspectives of day, week and month, providing a basis for the prediction of equipment utilization. In terms of data processing and analysis, machine learning, time series analysis and other methods are used to deeply explore the internal laws of equipment utilization and realize the analysis of long-term changes and irregular changes.

[0004] The traditional ideal solution only considers the deviation between the indicator and the ideal value, but cannot consider the correlation between the indicators. The grey correlation analysis method comprehensively considers the correlation by calculating the correlation between the indicators. Summary of the invention

[0005] The main purpose of the present invention is to provide a comprehensive evaluation method for the utilization of distribution network equipment. The improved grey comprehensive evaluation model not only fully integrates the respective advantages, but also effectively utilizes the information of indicator data, reflects the balance of indicator development, and thus more comprehensively reflects the status quo of the evaluation object and provides more reasonable and accurate evaluation results.

[0006] The technical solution adopted by the present invention is: a comprehensive evaluation method for the utilization of distribution network equipment, comprising:

[0007] Calculation of positive index values: Assume that m similar evaluation samples and n evaluation indicators are comprehensively evaluated; construct the sampled distribution network equipment utilization index standard values ​​into an initial decision matrix. The distribution network equipment utilization index values ​​are divided into two categories, including extremely large indicators and extremely small indicators, and each index value needs to be positively processed. For extremely small indicators, convert them into extremely large indicators:

[0008] x′ij =max(xj)-x ij (i=1,2,…,m,j=1,2,…,n) (1)

[0009] In the formula, max(x j ) is the maximum value of the jth index, x ij is the decision matrix element before forward transformation, x′ ij is the decision matrix element after forward transformation, m refers to the evaluation area, and n refers to the number of distribution network equipment utilization indicators;

[0010] Forward matrix standardization: The decision matrix X after forward transformation is:

[0011]

[0012] Where: x ij (i=1,2,…,m,j=1,2,…,n) is the observed data of the i-th evaluation sample under the j-th evaluation index;

[0013] The standardized matrix is ​​recorded as Z, and each element of Z is:

[0014]

[0015]

[0016] Where: z ij is the element of the standardized matrix, and Z is the standardized matrix;

[0017] Calculate the score:

[0018] Define the maximum and minimum values:

[0019] Z + ={max(z 11 ,z 21 ,…,z m1 ),max(z 12 ,z 22 ,…,z m2 ),…,max(z 1n ,z 2n ,…,z mn )} (5)

[0020] Z - ={min(z 11 ,z 21 ,…,z m1 ),min(z 12 ,z 22 ,…,z m2 ),…,min(z 1n ,z2n ,…,z mn )} (6)

[0021] Where: Z + With Z - are the maximum and minimum values ​​in the standardized matrix column vector, representing positive and negative ideal solutions;

[0022] The distance between the i-th evaluation object and the maximum value:

[0023]

[0024] The distance between the i-th evaluation object and the minimum value:

[0025]

[0026] Where: and is the distance between the i-th evaluation object and the positive and negative ideal solutions.

[0027] The importance of each indicator to the evaluation target will change in different scenarios, then:

[0028]

[0029]

[0030] Where: j The weights corresponding to different scenarios;

[0031] Calculate the score of the i-th evaluation object:

[0032]

[0033] In the formula, S i Represents the score of the i-th evaluation object, 0≤S i ≤1, and S i The bigger The smaller it is, the closer it is to the maximum value.

[0034] Furthermore, the comprehensive evaluation method for the utilization of distribution network equipment also includes grey correlation analysis, specifically:

[0035] Using formula (4), we can get the normalized matrix Z, as shown below:

[0036]

[0037] The set of optimal solutions consisting of the optimal values ​​of each indicator is extracted as follows:

[0038] Z 0 =[z 01 ,z02 ,…,z 0n ] (13)

[0039] Where: z ij (i=1,2,…,m,j=1,2,…,n) is the observed data of the i-th evaluation sample under the j-th evaluation index after standardization;

[0040] Calculate the grey correlation coefficient:

[0041] ζ(z 0j ,z ij ) is Z and Z 0 The grey correlation coefficient of the jth index is expressed as:

[0042]

[0043] Where: z 0j is the set of optimal solutions z 0 The elements in z ij is the element in the normalized matrix Z, |z 0j -z ij | for z 0j With z ij The absolute value of the difference, max i max j |z 0j -z ij |withmin i min j |z 0j -z ij | for z 0j With z ij Take the maximum and minimum values ​​respectively, and introduce the resolution coefficient ρ to reduce the influence of extreme values, with the value range ρ∈[0,1];

[0044] Calculate the grey relational degree:

[0045] Remember i Z i With Z 0 The grey relational degree is calculated as follows:

[0046]

[0047] Where: ζ(z 0j ,z ij ) is a single element value z 0j With z ij The grey correlation between them.

[0048] The importance of each indicator to the evaluation target will change in different scenarios, so Z i With Z 0The grey relational degree is ξ ωi , the calculation formula is:

[0049]

[0050] Where: j is the weight corresponding to different scenarios, ξ ωi The size of X′ i With X′ 0 degree of similarity.

[0051] Advantages of the present invention:

[0052] The present invention constructs a comprehensive evaluation model that can more comprehensively evaluate the comprehensive evaluation object; the improved grey comprehensive evaluation model fully considers the importance, correlation and interaction of each indicator, and combines the advantages of the traditional grey evaluation model and the ideal solution. Through this model, the quality of the comprehensive evaluation object can be obtained, and the balance of the indicators can be considered at the same time, making the evaluation result more accurate and objective.

[0053] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0055] Figure 1 It is a flow chart of the comprehensive evaluation method for distribution network equipment utilization of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] refer to Figure 1 ,

[0058] A comprehensive evaluation method for utilization of distribution network equipment, comprising:

[0059] Calculation of positive index values: Assume that m similar evaluation samples and n evaluation indicators are comprehensively evaluated; construct the sampled distribution network equipment utilization index standard values ​​into an initial decision matrix. The distribution network equipment utilization index values ​​are divided into two categories, including extremely large (benefit) indicators and extremely small (cost) indicators, and each index value needs to be positively processed. For extremely small indicators, convert them into extremely large indicators:

[0060] x′ ij =max(xj)-x ij (i=1,2,…,m,j=1,2,…,n) (1)

[0061] In the formula, max(x j ) is the maximum value of the jth index, x ij is the decision matrix element before forward transformation, x′ ij is the decision matrix element after forward transformation, m refers to the evaluation area, and n refers to the number of distribution network equipment utilization indicators;

[0062] Normalization of the forward matrix: Since the dimensions of the indicators of the utilization rate of distribution network equipment are different, in order to intuitively evaluate and compare the indicators, it is necessary to normalize the values ​​of the indicators. For the convenience of expression, the forward-normalized decision matrix X is:

[0063]

[0064] Where: x ij (i=1,2,…,m,j=1,2,…,n) is the observed data of the i-th evaluation sample under the j-th evaluation index;

[0065] The standardized matrix is ​​recorded as Z, and each element of Z is:

[0066]

[0067]

[0068] Where: z ij is the element of the standardized matrix, and Z is the standardized matrix.

[0069] Calculate the score:

[0070] Define the maximum and minimum values:

[0071] Z + ={max(z 11 ,z 21 ,…,z m1 ),max(z 12 ,z 22 ,…,z m2 ),…,max(z1n ,z 2n ,…,z mn )} (5)

[0072] Z - ={min(z 11 ,z 21 ,…,z m1 ),min(z 12 ,z 22 ,…,z m2 ),…,min(z 1n ,z 2n ,…,z mn )} (6)

[0073] Where: Z + With Z - are the maximum and minimum values ​​in the standardized matrix column vector, representing positive and negative ideal solutions;

[0074] The distance between the i-th evaluation object and the maximum value:

[0075]

[0076] The distance between the i-th evaluation object and the minimum value:

[0077]

[0078] Where: and is the distance between the i-th evaluation object and the positive and negative ideal solutions.

[0079] The importance of each indicator to the evaluation target will change in different scenarios, then:

[0080]

[0081]

[0082] Where: j The weights corresponding to different scenarios;

[0083] Calculate the score of the i-th evaluation object:

[0084]

[0085] In the formula, S i Represents the score of the i-th evaluation object, 0≤S i ≤1, and S i The bigger The smaller it is, the closer it is to the maximum value.

[0086] Since the ideal solution usually does not consider the relationship and correlation between indicators, it may ignore the complex relationship between multiple indicators. Grey correlation analysis can consider the correlation between multiple indicators at the same time, which has advantages for comprehensive evaluation of multiple indicators. Specifically, it includes the following steps:

[0087] Using formula (4), we can get the normalized matrix Z, as shown below:

[0088]

[0089] The set of optimal solutions consisting of the optimal values ​​of each indicator is extracted as follows:

[0090] Z 0 =[z 01 ,z 02 ,…,z 0n ] (13)

[0091] Where: z ij (i=1,2,…,m,j=1,2,…,n) is the observed data of the i-th evaluation sample under the j-th evaluation index after standardization;

[0092] Calculate the grey correlation coefficient:

[0093] ζ(z 0j ,z ij ) is Z and Z 0 The grey correlation coefficient of the jth index is expressed as:

[0094]

[0095] Where: z 0j is the set of optimal solutions z 0 The elements in z ij is the element in the normalized matrix Z, |z 0j -z ij | for z 0j With z ij The absolute value of the difference, max i max j |z 0j -z ij |withmin i min j |z 0j -z ij | for z 0j With z ij Take the maximum and minimum values ​​respectively, and introduce the resolution coefficient ρ to reduce the influence of extreme values, with the value range ρ∈[0,1];

[0096] Calculate the grey relational degree:

[0097] Rememberi Z i With Z 0 The grey relational degree is calculated as follows:

[0098]

[0099] Where: ζ(z 0j ,z ij ) is a single element value z 0j With z ij The grey correlation between them.

[0100] The importance of each indicator to the evaluation target will change in different scenarios, so Z i With Z 0 The grey relational degree is ξ ωi , the calculation formula is:

[0101]

[0102] Where: j is the weight corresponding to different scenarios, ξ ωi The size of X′ i With X′ 0 degree of similarity.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for distribution network equipment utilization, characterized in that: include: Calculation of positive index value: Assume that m similar evaluation samples and n evaluation indicators are comprehensively evaluated; The sampled standard values ​​of the distribution network equipment utilization index are constructed into an initial decision matrix; the distribution network equipment utilization index values ​​are divided into two categories, including extremely large indexes and extremely small indexes. Each index value needs to be positively processed, and the extremely small index is converted into an extremely large index: x′ ij =max(x j )-x ij (i=1,2,…,m,j=1,2,…,n) (1) In the formula, max(x j ) is the maximum value of the jth index, x ij is the decision matrix element before forward transformation, x′ ij is the decision matrix element after forward transformation, m refers to the evaluation area, and n refers to the number of distribution network equipment utilization indicators; Forward matrix standardization: The decision matrix X after forward transformation is: Where: x ij (i=1,2,…,m,j=1,2,…,n) is the observed data of the i-th evaluation sample under the j-th evaluation index; The standardized matrix is ​​recorded as Z, and each element of Z is: Where: z ij is the element of the standardized matrix, and Z is the standardized matrix; Calculate the score: Define the maximum and minimum values: WITH + ={max(from 11 ,With 21 ,…,With m1 ),max(of 12 ,With 22 ,…,With m2 ),…,max(of 1n ,With 2n ,…,With mn )} (5) WITH - ={min(z 11 ,With 21 ,…,With m1 ),min(of 12 ,With 22 ,…,With m2 ),…,min(of 1n ,With 2n ,…,With mn )} (6) Where: Z + and Z- are the maximum and minimum values ​​in the standardized matrix column vector, representing positive and negative ideal solutions; The distance between the i-th evaluation object and the maximum value: The distance between the i-th evaluation object and the minimum value: Where: and is the distance between the i-th evaluation object and the positive and negative ideal solutions; The importance of each indicator to the evaluation target will change in different scenarios, then: Where: j The weights corresponding to different scenarios; Calculate the score of the i-th evaluation object: In the formula, S i Represents the score of the i-th evaluation object, 0≤S i ≤1, and S i The bigger The smaller it is, the closer it is to the maximum value.

2. The comprehensive evaluation method for distribution network equipment utilization according to claim 1 is characterized in that: It also includes grey relational analysis, specifically: Using formula (4), we can get the normalized matrix Z, as shown below: The set of optimal solutions consisting of the optimal values ​​of each indicator is extracted as follows: Z0=[z 01 ,With 02 ,…,With 0n ] (13) Where: z ij (i=1,2,…,m,j=1,2,…,n) is the observed data of the i-th evaluation sample under the j-th evaluation index after standardization; Calculate the grey correlation coefficient: ζ(z 0j ,z ij ) is the grey correlation coefficient between Z and Z0 in the jth index, expressed as: Where: z 0j is an element in the set z0 of the optimal solution, z ij is the element in the normalized matrix Z, |z 0j -z ij | for z 0j With z ij The absolute value of the difference, max i max j |z 0j -z ij |withmin i min j |z 0j -z ij | for z 0j With z ij Take the maximum and minimum values ​​respectively, and introduce the resolution coefficient ρ to reduce the influence of extreme values, with the value range ρ∈[0,1]; Calculate the grey relational degree: Remember i Z i The grey correlation degree with Z0 is calculated as follows: Where: ζ(z 0j ,z ij ) is a single element value z 0j With z ij Grey correlation between The importance of each indicator to the evaluation target will change in different scenarios, so Z i The grey relational degree with Z0 is ξ ωi , the calculation formula is: Where: j is the weight corresponding to different scenarios, ξ ωi The size of X′ i The degree of similarity with X′0.

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