A risk assessment method for distribution network equipment based on MARCOS method

Abstract

The present invention discloses a distribution network equipment risk assessment method based on the MARCOS method, including: selecting various assessment indicators involved in the risk assessment of distribution network equipment; conducting relative importance assessment on the selected assessment indicators; establishing a judgment matrix, calculating the subjective weight of each assessment indicator; scoring the risk degree of the distribution equipment involved in the assessment; constructing an evaluation matrix, processing the evaluation matrix using the CRITIC method, and calculating the objective weight of each assessment indicator; combining the improved AHP and CRITIC methods to obtain the comprehensive subjective and objective weights of each assessment indicator; using the evaluation matrix and the MARCOS method to calculate the utility function of each distribution equipment, and ranking the comprehensive risk degree of each distribution equipment. The present invention combines subjective weights with objective weights in the assessment indicators, can refer to the rich experience of experts, can also make full use of original data, can comprehensively consider multiple factors when ranking the risk of equipment, and improve the rationality and accuracy of the assessment results.

Description

Technical Field

[0001] The present invention relates to the field of risk assessment of power distribution equipment, and in particular to a risk assessment method for power distribution network equipment based on MARCOS. Background Art

[0002] As the main component of the network, the stable operation and good maintenance of distribution network equipment are the basis for the reliable operation of the distribution network. In recent years, the number and types of distribution network equipment have increased rapidly, the network structure of the distribution network has become increasingly complex, the operation mode has become more variable, and the risk issues faced are increasing. How to accurately combine multiple factors to conduct risk assessment of distribution equipment, reasonably arrange the transformation and upgrading order of risk equipment, and achieve safe and reliable operation of the distribution network has become a key issue that power supply companies need to solve urgently.

[0003] The risk assessment of distribution network equipment involves many indicators and factors, which is a multi-criteria comprehensive evaluation problem. Zhao Huiru, Li Nana and Guo Sen and other scholars divide the risk factors of equipment into internal factors such as equipment factors and operation factors, as well as external factors such as meteorological factors and accidental damage. Lin Zizhao, Pan Kaiyan and Zhou Mingyu and other scholars selected multiple risk indicators from the two aspects of distribution network operation risk and grid risk, respectively, so as to comprehensively consider the real-time risk of distribution network operation and the potential risk of equipment. However, there is often a certain correlation between the various indicators of risk assessment. The above scholars did not consider this aspect or only used a simple logarithmic synthesis method to deal with it. In order to reasonably determine the weight of each indicator in the process of risk assessment, the analytic hierarchy process (AHP) can well analyze the problem of subjective weight, but this is only based on the subjective judgment of experts and is still limited in accuracy.

[0004] After obtaining the weights of each indicator, in the final stage of risk assessment, how to use the existing information to rank the risk level of each device is of utmost importance. Traditional methods include simple weighting method, grey correlation method, fuzzy evaluation method, TOPSIS method, etc. The evaluation accuracy of these methods needs to be improved. Summary of the invention

[0005] Purpose of the invention: In order to overcome the deficiencies in the prior art, a distribution network equipment risk assessment method based on MARCOS is provided, which combines subjective weights with objective weights in the assessment indicators, can refer to the rich experience of experts, can make full use of original data, can take into account the correlation between various indicators, and can comprehensively consider various factors when ranking the risk of equipment, thereby improving the rationality and accuracy of the assessment results.

[0006] Technical solution: To achieve the above purpose, the present invention provides a distribution network equipment risk assessment method based on the MARCOS method, comprising the following steps:

[0007] S1: Select various evaluation indicators involved in the risk assessment of distribution network equipment;

[0008] S2: Evaluate the relative importance of each evaluation indicator selected in step S1;

[0009] S3: Use the improved AHP method to establish a judgment matrix and calculate the subjective weight of each evaluation indicator;

[0010] S4: According to the subjective weight of each evaluation index, the risk level of the power distribution equipment involved in the evaluation is scored;

[0011] S5: Construct an evaluation matrix based on the risk level scores, process the evaluation matrix using the CRITIC method, and calculate the objective weights of each evaluation indicator;

[0012] S6: Combine the improved AHP and CRITIC methods to obtain the comprehensive subjective and objective weights of each evaluation index;

[0013] S7: Use the evaluation matrix and MARCOS method to calculate the utility function of each distribution equipment, and rank the comprehensive risk level of each distribution equipment according to the size of the utility function.

[0014] Furthermore, the expression of the judgment matrix in step S3 is:

[0015]

[0016] Among them, t i is the scale value between the two indicators. At the same time, each element in the matrix should satisfy the following: ij >0;r ji =1 / r ij ; r ii =1; r ij =r ik r kj ,i,j,k=1,2,…,n, where r ij Represents the scale value comparing the i-th element to the j-th element.

[0017] Furthermore, the calculation formula of the subjective weight of each evaluation index in step S3 is:

[0018]

[0019] Among them, α i is the weight value of the i-th indicator; Represents the product of all elements in the i-th row of the matrix R.

[0020] Furthermore, in step S5, when the selected evaluation indicators include both positive indicators and negative indicators, the indicators need to be converted into the same direction. The conversion method is:

[0021]

[0022] Among them, max|X i | represents the maximum value of the i-th indicator; λ is the coordination coefficient, which is generally set to 0.1. After the above processing, the evaluation matrix X′ after all indicators are positively converted is obtained.

[0023] Furthermore, the objective weights of the evaluation indicators in step S5 are calculated as follows:

[0024] Convert each index value in the evaluation matrix X' to the same standard. The conversion method is:

[0025]

[0026] Through the standard matrix X″, the standard deviation σ of each indicator is obtained i The correlation coefficient between the index and ij , the conversion method is:

[0027]

[0028] ρ ij =cov(X″ i ,X″ j ) / (σ i σ j )i=1,2,…,n

[0029] in, Represents the mean of the i-th indicator; cov(X″ i ,X″ j ) represents the covariance between the i-th indicator and the j-th indicator in the standard matrix X″, and then the information G contained in each indicator is calculated by the standard deviation of the indicator and the correlation coefficient between the indicators i , the conversion method is: i=1,2,…,n;

[0030] The objective weights of various evaluation indicators are calculated using the following formula: Among them, G i is the information amount of the calculated evaluation index i.

[0031] Furthermore, the calculation method of the comprehensive subjective and objective weights of each evaluation index in step S6 is:

[0032]

[0033] Among them, αi is the subjective weight of indicator i, β i is the objective weight of indicator i.

[0034] Furthermore, the calculation method of the utility function of each power distribution device in step S7 is:

[0035] When using the MARCOS method, the evaluation matrix is ​​first transformed into an expanded matrix according to the properties of different indicators, and its form is:

[0036]

[0037] Among them, AI represents the ideal solution, which means the solution with the highest risk assessment; AAI represents the negative ideal solution, which means the solution with the lowest risk assessment; the definitions of AAI and AI are as follows:

[0038]

[0039]

[0040] Among them, B represents a set of maximization criteria, that is, we hope that the score of this criterion is as high as possible, while C represents a set of minimization criteria, that is, we hope that the score of this criterion is as low as possible;

[0041] The expanded matrix is ​​standardized as follows:

[0042]

[0043]

[0044] After matrix normalization, the weight matrix V = [v ij ] m×n , each element in the weighted matrix is ​​normalized by the weight coefficient w of each element of the matrix and each indicator j Multiply them together to get the weight coefficient, which is the subjective and objective comprehensive weight obtained;

[0045] Calculate the utility of the device to be evaluated relative to the ideal solution (AI) and the negative ideal solution (AAI) using the following calculation method:

[0046]

[0047]

[0048] Among them, S i represents the sum of the elements in the i-th row of the weighted matrix V;

[0049] The utility function of the device to be evaluated is determined according to the utility degree, and the calculation method is:

[0050]

[0051] Among them, the calculation method of the utility function related to the ideal solution (AI) and the negative ideal solution (AAI) is:

[0052]

[0053]

[0054] Furthermore, in step S7, the devices are sorted according to the utility function size after risk assessment, and then the subsequent upgrade and transformation strategies are arranged in order of risk level.

[0055] The present invention takes into account the correlation between indicators, which is reflected by the CRITIC method. In the CRITIC method, the correlation between indicators is reflected by conflict and the correlation coefficient ρ ij To quantify this conflict, the greater the correlation between indicators, the smaller the conflict and the smaller the weight.

[0056] Beneficial effects: Compared with the prior art, the present invention combines subjective weights with objective weights in evaluation indicators, which can not only refer to the rich experience of experts, but also make full use of original data, and also take into account the correlation between indicators. It can comprehensively consider various factors when ranking the risks of equipment, thereby improving the rationality and accuracy of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0058] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0059] The present invention provides a distribution network equipment risk assessment method based on the MARCOS method, such as Figure 1 As shown, it includes the following steps:

[0060] S1: Select various evaluation indicators involved in the risk assessment of distribution network equipment:

[0061] In this embodiment, the evaluation indicators include the importance of power supply load, equipment load rate, new load, equipment aging, power supply radius exceeding the limit and voltage drop exceeding the limit.

[0062] S2: Collect expert opinions and evaluate the relative importance of each evaluation indicator selected in step S1:

[0063] S3: Using the improved AHP method and comprehensive expert opinions, the relative importance of risk indicators is compared pairwise according to the scale values ​​given in Table 1, and the judgment matrix R is established to calculate the subjective weight of each evaluation indicator:

[0064] Table 1 Meaning of scale values

[0065]

[0066] The expression of judgment matrix R is:

[0067]

[0068] Among them, t i is the scale value between the two indicators. At the same time, each element in the matrix should satisfy the following: ij >0;r ji =1 / r ij ; r ii =1; r ij =r ik r kj ,i,j,k=1,2,…,n, where r ij Represents the scale value of the i-th element compared with the j-th element;

[0069] Based on the expert opinions, a judgment matrix is ​​formed;

[0070] In this embodiment, the expert opinions collected can be ranked as follows: power supply load importance > equipment load rate > new load > equipment aging > power supply radius exceeds limit > voltage drop exceeds limit. According to the scale value data given in Table 1, the expert opinions of the evaluation indicators under subjective judgment are: 12 =1.8,r 23 =1.6,r 34 =1.4,r 45 =1.2,r 56 =1.2.

[0071] The resulting judgment matrix is:

[0072]

[0073] The calculation formula for the subjective weight of each evaluation indicator is:

[0074]

[0075] Among them, α i is the weight value of the i-th indicator; represents the product of all elements in the i-th row of the matrix R;

[0076] In this embodiment, according to the calculation formula of subjective weight, the subjective weight of each risk indicator is calculated as α1=0.3953, α2=0.2196, α3=0.1373, α4=0.0980, α5=0.0817, and α6=0.0681.

[0077] S4: According to the subjective weight of each evaluation indicator, the risk level of the power distribution equipment involved in the evaluation is scored:

[0078] The score table in this embodiment uses the scores given by Xiao Bai, Li Panpan and others for each risk item of medium-risk equipment in a certain urban area in Northeast China.

[0079] S5: Construct an evaluation matrix based on the risk level scores, process the evaluation matrix using the CRITIC method, and calculate the objective weights of each evaluation indicator:

[0080] The elements in the evaluation matrix are obtained by expert scoring. For example, each device is scored under the conditions of new load, equipment load rate, equipment age, power supply load importance, power supply radius exceeding the limit, and voltage drop exceeding the limit. The score ranges from 0, 2, 4, 6, 8, and 10 to represent the severity of each situation. The increasing score represents the increasing severity. The specific scoring is shown in Table 2.

[0081] Table 2 Ratings of various equipment indicators

[0082]

[0083]

[0084] When the selected evaluation indicators include both positive and negative indicators, the indicators need to be converted into the same direction. The conversion method is:

[0085]

[0086] Among them, max|X i | represents the maximum value of the i-th indicator; λ is the coordination coefficient, which is set to 0.1 in this embodiment. After the above processing, the evaluation matrix X′ after all the indicators are positively converted is obtained.

[0087] The objective weights of each evaluation indicator are calculated as follows:

[0088] Convert each index value in the evaluation matrix X' to the same standard. The conversion method is:

[0089]

[0090] Through the standard matrix X″, the standard deviation σ of each indicator is obtained i The correlation coefficient between the index andij , the conversion method is:

[0091]

[0092] ρ ij =cov(X″″,X″ j ) / (σ i σ j )i=1,2,…,n

[0093] in, Represents the mean of the i-th indicator; cov(X″ i ,X″ j ) represents the covariance between the i-th indicator and the j-th indicator in the standard matrix X″, and then the information G contained in each indicator is calculated by the standard deviation of the indicator and the correlation coefficient between the indicators i , the conversion method is: i=1,2,…,n;

[0094] The objective weights of various evaluation indicators are calculated using the following formula: Among them, G i is the information amount of the calculated evaluation index i.

[0095] In this embodiment, the objective weights of various indicators are obtained after the above processing based on the data in Table 2 as shown in Table 3.

[0096] Table 3 Objective weights based on CRITIC

[0097]

[0098] S6: Combine the improved AHP and CRITIC methods to obtain the comprehensive subjective and objective weights of each evaluation index:

[0099] The calculation method is:

[0100]

[0101] Among them, α i is the subjective weight of indicator i, β i is the objective weight of indicator i;

[0102] In this embodiment, the subjective and objective comprehensive weight vector of each risk index obtained after the above method is processed is ω = [0.3684, 0.2105, 0.1287, 0.1237, 0.0958, 0.0729] T .

[0103] S7: Use the evaluation matrix and MARCOS method to calculate the utility function of each distribution equipment, and rank the comprehensive risk level of each distribution equipment according to the size of the utility function:

[0104] When using the MARCOS method, the evaluation matrix is ​​first transformed into an expanded matrix according to the properties of different indicators, and its form is:

[0105]

[0106] Among them, AI represents the ideal solution, which means the solution with the highest risk assessment; AAI represents the negative ideal solution, which means the solution with the lowest risk assessment; the definitions of AAI and AI are as follows:

[0107]

[0108]

[0109] Among them, B represents a set of maximization criteria, that is, we hope that the score of this criterion is as high as possible, while C represents a set of minimization criteria, that is, we hope that the score of this criterion is as low as possible;

[0110] The expanded matrix is ​​standardized as follows:

[0111]

[0112]

[0113] After matrix normalization, the weight matrix V = [v ij ] m×n , each element in the weighted matrix is ​​normalized by the weight coefficient w of each element of the matrix and each indicator j Multiply them together to get the weight coefficient, which is the subjective and objective comprehensive weight obtained;

[0114] Calculate the utility of the device to be evaluated relative to the ideal solution (AI) and the negative ideal solution (AAI) using the following calculation method:

[0115]

[0116]

[0117] Among them, S i represents the sum of the elements in the i-th row of the weighted matrix V;

[0118] The utility function of the device to be evaluated is determined according to the utility degree, and the calculation method is:

[0119]

[0120] Among them, the calculation method of the utility function related to the ideal solution (AI) and the negative ideal solution (AAI) is:

[0121]

[0122]

[0123] The equipment is sorted according to the size of its utility function after risk assessment, and the subsequent upgrade and transformation strategies are arranged in order of risk level.

[0124] In this embodiment, the data in Table 2 is processed as described above to obtain the utility function of each device, wherein the top ten devices in terms of risk level and their utility function values ​​are shown in Table 4.

[0125] Table 4 Risk assessment values ​​of the top ten devices

[0126]

[0127] As shown in Table 4, the results obtained after the evaluation using the MARCOS method are: the top three risky equipment are the agricultural line, the bridge line and the Xinaijia line, and the risk assessment values ​​are 0.7673, 0.6100 and 0.5856 respectively. According to the score sheet, it can be analyzed that the agricultural line faces serious problems of new loads and equipment overload, and in addition to the equipment overload problem, the loads connected to the bridge line are also very important, indicating that they are in urgent need of renovation measures such as maintenance or replacement and upgrading. Based on the reference of the evaluation results, within the limited time and resources, giving priority to the equipment ranked high in the upgrade and renovation risk assessment can also improve the stability of the power system more efficiently and more accurately and reasonably.

Claims

1. A distribution network equipment risk assessment method based on the MARCOS method, characterized in that: The steps include: S1: Select various evaluation indicators involved in the risk assessment of distribution network equipment; The various evaluation indicators include the importance of power supply load, equipment load rate, new load, equipment ageing, power supply radius exceeding the limit and voltage drop exceeding the limit; S2: Evaluate the relative importance of each evaluation indicator selected in step S1; S3: Use the improved AHP method to establish a judgment matrix and calculate the subjective weight of each evaluation indicator; S4: According to the subjective weight of each evaluation index, the risk level of the power distribution equipment involved in the evaluation is scored; S5: Construct an evaluation matrix based on the risk level scores, process the evaluation matrix using the CRITIC method, and calculate the objective weights of each evaluation indicator; S6: Combine the improved AHP and CRITIC methods to obtain the comprehensive subjective and objective weights of each evaluation index; S7: Using the evaluation matrix and the MARCOS method to calculate the utility function of each distribution device, and rank the comprehensive risk level of each distribution device according to the size of the utility function; The expression of the judgment matrix in step S3 is: Among them, t i is the scale value between the two indicators. At the same time, each element in the matrix should satisfy the following: ij >0;r ji =1 / r ij ; r ii =1; r ij =r ik r kj ,i,j,k=1,2,…,n, where r ij Represents the scale value of the i-th element compared with the j-th element; The calculation formula of the subjective weight of each evaluation index in step S3 is: Among them, α i is the weight value of the i-th indicator; represents the product of all elements in the i-th row of the matrix R; The calculation method of the utility function of each power distribution device in step S7 is: When using the MARCOS method, the evaluation matrix is ​​first transformed into an expanded matrix according to the properties of different indicators, and its form is: Among them, AI represents the ideal solution, which means the solution with the highest risk assessment; AAI represents the negative ideal solution, which means the solution with the lowest risk assessment; the definitions of AAI and AI are as follows: Among them, B represents a set of maximization criteria, and C represents a set of minimization criteria; The expanded matrix is ​​standardized as follows: After matrix normalization, the weight matrix V = [v ij ] m×n , each element in the weighted matrix is ​​normalized by the weight coefficient w of each element of the matrix and each indicator j Multiply them together to get the weight coefficient, which is the subjective and objective comprehensive weight obtained; Calculate the utility of the device to be evaluated relative to the ideal solution and the negative ideal solution. The calculation method is: Among them, S i represents the sum of the elements in the i-th row of the weighted matrix V; The utility function of the device to be evaluated is determined according to the utility degree, and the calculation method is: Among them, the calculation method of the utility function related to the ideal solution and the negative ideal solution is:

2. A distribution network equipment risk assessment method based on the MARCOS method according to claim 1, characterized in that: In step S5, when the selected evaluation indicators include both positive indicators and negative indicators, the indicators need to be converted into the same direction. The conversion method is: Among them, max|X i | represents the maximum value of the i-th indicator; λ is the coordination coefficient. After the above processing, the evaluation matrix X′ after all the indicators are positively converted is obtained.

3. A distribution network equipment risk assessment method based on the MARCOS method according to claim 2, characterized in that: The objective weights of the evaluation indicators in step S5 are calculated as follows: Convert each index value in the evaluation matrix X' to the same standard. The conversion method is: Through the standard matrix X″, the standard deviation σ of each indicator is obtained i The correlation coefficient between the index and ij , the conversion method is: r ij =cov(X″ i "X" j ) / (s i s j )i=1,2,…,n in, Represents the mean of the i-th indicator; cov(X″ i ,X″ j ) represents the covariance between the i-th indicator and the j-th indicator in the standard matrix X″, and then the information G contained in each indicator is calculated by the standard deviation of the indicator and the correlation coefficient between the indicators i , the conversion method is: The objective weights of various evaluation indicators are calculated using the following formula: Among them, G i is the information amount of the calculated evaluation index i.

4. A distribution network equipment risk assessment method based on the MARCOS method according to claim 1, characterized in that: The calculation method of the comprehensive subjective and objective weights of each evaluation index in step S6 is: Among them, α i is the subjective weight of indicator i, β i is the objective weight of indicator i.

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