A method for evaluating the operating health status of medium voltage distribution networks

Abstract

The present invention relates to an evaluation method for the operation health status of a medium-voltage distribution network, comprising the following steps: obtaining the fault indicators of all distribution equipment for many years and the fault data corresponding to each fault indicator, solving the importance index of each distribution equipment, and screening the key distribution equipment that can characterize the operation health status of the distribution network; obtaining the physical health indicators that characterize the grid and key distribution equipment, and calculating the numerical health indicators: the health index of key distribution equipment and the strong index of the grid; statistically analyzing the meteorological environment characteristics in the distribution network, calculating the environmental correction coefficient, and calculating and correcting the health index of the key distribution equipment; solving the weights of the physical health indicators and the numerical health indicators, and then obtaining the health index of the distribution network to achieve evaluation. Compared with the prior art, the present invention combines the comprehensive influence of the distribution equipment, the health status of the grid and the external natural environment, and has a higher evaluation level and a more objective and scientific evaluation method, and can evaluate the operation health status of the distribution network in real time and quantitatively.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network operation, and in particular to a method for evaluating the operating health status of a medium-voltage distribution network. Background Art

[0002] In the process of building a new power system, the distribution network, as the core role of the transmission system and power users, plays an important role in ensuring reliable power supply, serving users and improving the power consumption experience. The medium-voltage distribution network is widely distributed in the distribution system. Ensuring its safe operation and reliable power supply is of great significance to the planning of the distribution network (grid), the operation and inspection of distribution equipment, and improving the overall operation health of the distribution network. Therefore, in order to better adapt to the construction of the new power system and build an intelligent and digital distribution network, it is of great practical value to conduct an operation health assessment of the medium-voltage distribution network, study its operation health from the distribution network level, and provide technical support for subsequent work.

[0003] The existing distribution network health status assessment technology has the following problems:

[0004] (1) The existing technology mainly focuses on the health status of high-voltage distribution equipment with higher single-unit value. The research object is limited and can only provide guidance for the operation and maintenance of the distribution equipment. There is a lack of a mature research system for the overall operation health status of the medium-voltage distribution network.

[0005] (2) The existing technology does not provide clear evaluation indicators for the operational health status of medium-voltage distribution networks. It mainly focuses on the influencing factors of the operation of single distribution equipment and lacks consideration and combination of the impact of natural environmental factors on the distribution network, resulting in the evaluation accuracy being lower than the actual situation. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method for evaluating the operating health status of a medium-voltage distribution network. The invention combines the comprehensive influence of distribution equipment, grid health status and external natural environment, with a higher evaluation level and a more objective and scientific evaluation method, and can quantitatively evaluate the operating health status of the distribution network.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] The present invention provides a method for evaluating the operating health status of a medium voltage distribution network, comprising the following steps:

[0009] S1: Obtain the fault indicators of all distribution equipment for many years and the fault data corresponding to each fault indicator, build a fault data set for each distribution equipment, and then solve the importance index of each distribution equipment through the Gini index method based on the fault data set, and then select the key distribution equipment that can characterize the healthy operation status of the medium-voltage distribution network;

[0010] S2: Obtain physical health indicators that characterize the grid and key distribution equipment obtained in S1, and construct a distribution network health index association model to calculate numerical health indicators, wherein the numerical health indicators include a health index of key distribution equipment and a grid strength index;

[0011] S3: Count the historical environmental characteristics of the medium-voltage distribution network and collect real-time environmental characteristics. According to the historical and real-time environmental characteristics, build an environmental numerical correction model to calculate the environmental correction coefficient, and calculate and correct the health index of key distribution equipment in S2.

[0012] S4: Perform weight analysis on the physical health index and grid strength index in S2 and the key distribution equipment health index corrected in S3, solve the weight of each health index, and then build a distribution network health index model to obtain the distribution network health index;

[0013] S5: Formulate the health level of the distribution network, combine the distribution network health index obtained in S4 with the distribution network health level to achieve a quantitative assessment of the health of the medium-voltage distribution network, and obtain the health level of the medium-voltage distribution network.

[0014] Preferably, the S1 comprises the following steps:

[0015] S1.1: Obtain the fault indicators of all power distribution equipment for many years and the fault data corresponding to each fault indicator, wherein the fault indicators include the number of faults, the number of affected users, the power failure load, the maintenance time and the number of replacements, and construct the fault data set of each power distribution equipment according to the fault data;

[0016] S1.2: Normalize the fault data in the fault data set of each power distribution device respectively, and then solve the proportion weight and Gini value of each fault data of each power distribution device each year respectively, and calculate the importance index of each fault indicator of the power distribution device in the fault data set of the power distribution device according to the Gini value;

[0017] S1.3: Select key power distribution equipment according to the importance index of each device. When the importance index α of the power distribution equipment is greater than 0.5, the power distribution equipment is judged to be a key power distribution equipment.

[0018] Preferably, the formula of the importance index α is:

[0019]

[0020] In the formula, α is the importance index, A is the fault data set of the power distribution equipment, and γ j is the Gini value of the power distribution equipment in each year within N years, j is the index of the year, N is a constant, i is the index of the fault data, and p ijIt is the weight ratio of each fault data of power distribution equipment in each year within N years to all fault data of that year.

[0021] Preferably, S2 comprises the following steps:

[0022] S2.1: Obtain physical health indicators that characterize the grid and key distribution equipment, including load transfer rate, line loss rate, voltage qualification rate, harmonic qualification rate, and power outage duration;

[0023] S2.2: Use extreme value type I function to obtain the failure probability of key power distribution equipment, and solve the health index of key power distribution equipment according to the failure probability;

[0024] S2.3: Obtain and obtain the grid strength index of the medium voltage distribution network based on the load transfer rate of the branches in the distribution network, the qualified R of the grid N-1 load transfer element and the overall anti-destruction index of the distribution network.

[0025] Preferably, the calculation formula of the key power distribution equipment health index is as follows:

[0026]

[0027] In the formula, K g is the fault proportionality coefficient, C g is the fault curvature coefficient, S t is the health index of key power distribution equipment, P I is the failure probability of key power distribution equipment under extreme type I function.

[0028] Preferably, the S3 specifically includes the following steps:

[0029] S3.1: Count the historical environmental characteristics in the medium voltage distribution network and collect real-time environmental characteristics. The environmental characteristics include: sudden change value of ambient temperature, number of lightning strikes, wind speed, precipitation, relative humidity and salt fog. The environmental characteristic data corresponding to each historical environmental characteristic constitute an environmental data set.

[0030] S3.2: Determine the maximum value, minimum value, maximum score value and minimum score value of each historical environmental characteristic according to the environmental data set, and then determine the real-time environmental characteristic score value according to the real-time environmental characteristics, and obtain the impact score value of all environmental characteristics of the key power distribution equipment in operation according to the maximum score value and minimum score value of each historical environmental characteristic combined with the real-time environmental characteristic score value;

[0031] S3.3: Based on the results of S3.2, linearly fit the environmental characteristics with the failure probability of the key distribution equipment in S2.2, and then solve the environmental correction coefficient k, and use the environmental correction coefficient k to calculate and correct the health index of the key distribution equipment in S2.2.

[0032] Preferably, the calculation formula of the impact score value of the key power distribution equipment on all environmental characteristics is as follows:

[0033]

[0034] Where W is the real-time score of the environmental feature data corresponding to each environmental feature, Y′ m is the real-time measured environmental feature data corresponding to the mth environmental feature, W w W is the impact score of key power distribution equipment on all environmental characteristics. max , W min are the maximum and minimum scores of each environmental feature, respectively, m is the weight value of each environmental feature.

[0035] Preferably, the calculation formula of the environmental correction coefficient k is as follows:

[0036]

[0037] In the formula, ρ is the proportionality coefficient and h is the constant coefficient.

[0038] Preferably, the S4 specifically includes the following steps:

[0039] S4.1: construct a multi-attribute feature matrix with the health indicator data corresponding to seven health indicators, namely, load transfer rate, line loss rate, voltage qualification rate, harmonic qualification rate, power outage duration, corrected key distribution equipment health index and grid strength index; normalize the multi-attribute feature matrix to obtain a normalized matrix; construct a weight vector and assign initial values ​​to the seven indicators in an average weighted manner; then construct a weighted normative matrix; perform weight calculation on the elements in the weighted normative matrix to obtain the weight value of each health indicator;

[0040] S4.2: A set of seven health indicator data is collected in real time. Based on the real-time collected health indicator data, the health indicators of S4.1 and the weight values ​​corresponding to each health indicator, a distribution network health index model is constructed and trained through the maximum likelihood method to obtain the distribution network health index.

[0041] Preferably, the calculation formula for the numerical value of the distribution network health index is as follows:

[0042]

[0043] In the formula, ω b is the weight value corresponding to each health indicator in S4.1, ω0 is the distribution network health model parameter, and HI is the value of the distribution network health index.

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] 1. The present invention is not limited to the health status assessment of a single distribution device, but rather assesses the overall operational health status of the medium-voltage distribution network, rising from the level of a single device to the level of the distribution network, and formulating corresponding distribution network health assessment levels, which can quantitatively assess the overall operational health status of the medium-voltage distribution network and provide technical support for medium-voltage distribution network planning, operation and maintenance, etc.

[0046] 2. The present invention comprehensively considers the medium-voltage distribution equipment and grid conditions, develops a series of physical health indicators and calculates numerical health indicators. Compared with the prior art, the evaluation indicators of the present invention are more comprehensive, and the operational health evaluation of the medium-voltage distribution network is more scientific and accurate.

[0047] 3. The present invention not only takes into account the influence of internal factors during the operation of the medium-voltage distribution network, but also fully considers the impact of external environmental factors on the operation health of the distribution equipment. It constructs an environmental numerical correction model to correct the operation health index of the medium-voltage distribution equipment, thereby further improving the accuracy of the distribution equipment and network operation health assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of a method for evaluating the operating health status of a medium-voltage distribution network provided in this embodiment. DETAILED DESCRIPTION

[0049] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Example

[0051] This embodiment provides a method for evaluating the operating health status of a medium voltage distribution network, comprising the following steps:

[0052] S1: Obtain the fault indicators of all distribution equipment for many years and the fault data corresponding to each fault indicator, build a fault data set for each distribution equipment, and then solve the importance index α of each distribution equipment through the Gini index method based on the fault data set, and then select the key distribution equipment that can characterize the healthy operation status of the medium-voltage distribution network;

[0053] As an optional implementation manner, three years of fault indicators of all power distribution equipment and the fault data corresponding to each fault indicator are obtained.

[0054] S1.1: Obtain the fault indicators of all power distribution equipment for many years and the fault data corresponding to each fault indicator. The fault indicators include the number of faults, the number of affected users, the power failure load, the maintenance time and the number of replacements. According to the fault data, construct the fault data set A of each power distribution equipment.

[0055] S1.2: normalize the fault data in the fault data set of each power distribution device respectively, and then solve the Gini value of each fault data of each power distribution device each year respectively, and then calculate the importance index of each fault indicator in the fault data set according to the Gini value;

[0056] Among them, for a power distribution equipment, the fault data set A of the power distribution equipment is:

[0057] A={X ij}, i=1,2,…,5, j=1,2,…,N;

[0058] In the formula, i is the index of the fault data, j is the index of the year, and N is a constant;

[0059] The specific formula for normalizing the fault data in the fault data set of the power distribution equipment is:

[0060]

[0061] In the formula, X ij is the i-th fault data in the j-th year, and the X-th i.max For X i The maximum value, X i.min For X i The minimum value, X i ′ is the normalized parameter value.

[0062] In the kth year, the formula for the proportional weight of each fault data of the power distribution equipment is as follows:

[0063]

[0064] In the formula, p ik is the weight of the proportion of the ith failure data in the kth year to all the failure data in the kth year, and k is any constant within 1, 2, …, N.

[0065] According to the above formula, we can calculate the weight p of each fault data of the power distribution equipment in N years, which accounts for the proportion of all fault data of the year. ij .

[0066] In the kth year, the Gini value of the power distribution equipment is:

[0067]

[0068] In the formula, γ k is the Gini value of the distribution equipment in the kth year.

[0069] According to the above formula, the Gini value γ of the power distribution equipment in each year within N years is obtained j, and then find the importance index of each fault data of the power distribution equipment in the fault data set A. When N=3, the formula of the importance index is:

[0070]

[0071] S1.3: Select key distribution equipment based on the importance index of each device, considering that the importance of a single distribution equipment must account for more than half of the total distribution equipment. When the importance index α of a distribution equipment is greater than 0.5, the distribution equipment is considered to be a key distribution equipment.

[0072] S2: From the two dimensions of safety and reliability that characterize the health status of the distribution network, obtain the physical health indicators that characterize the grid and the key distribution equipment obtained in S1, build a distribution network health index correlation model to formulate and calculate numerical health indicators, which include: key distribution equipment health index S t and grid strength index W t ;

[0073] S2.1: Obtain physical health indicators that characterize the grid and key distribution equipment, including load transfer rate, line loss rate, voltage qualification rate, harmonic qualification rate, and power outage duration;

[0074] Specifically, the calculation formulas for load transfer rate, line loss rate, voltage qualification rate and harmonic qualification rate are as follows:

[0075] T s =P s / (P s +P lost )×100%

[0076] C s =(ΔP / P∑)×100%

[0077] ε=(1-g / G)×100%

[0078] ε x =(1-g x / G x )×100%

[0079] Where, T s is the load transfer rate, P s is the load that the system can successfully transfer, P lost is the load that failed to be transferred successfully, and the sum of the two is the total load affected; C s is the line loss rate, ΔP is the line loss power, P Σ is the total power supply; ε is the voltage qualification rate, g is the total number of nodes whose voltage does not meet the standard at the current time section, and G is the number of voltage nodes in the distribution network; ε x is the harmonic pass rate, gx G is the number of nodes whose voltage total harmonic distortion rate does not meet the standard at the current time section, x is the total number of load access points in the distribution network.

[0080] S2.2: Obtain and solve the key power distribution equipment health index S based on the failure probability of the key power distribution equipment t ;

[0081] Specifically, as a traditional physical quantity that can characterize the reliability of equipment operation, the failure probability can better evaluate its operating status. Preferably, this embodiment uses an extreme value type I function to solve the operating failure probability of key power distribution equipment, and the calculation formula is as follows:

[0082]

[0083] Where P I is the failure probability of key power distribution equipment under extreme value type I function, u d is the design allowable value of each key power distribution equipment, w d is the actual measurement of the design value of the key power distribution equipment, and both values ​​are normalized; a s is the scale parameter of the extreme value type I distribution, u0 is the location parameter of the extreme value type I distribution, σ d is the standard deviation between the design allowable value and the actual measurement.

[0084] As an optional implementation, a s The value is 0.25 and the value of u0 is 0.2.

[0085] Specifically, the calculation formula for solving the health index of key power distribution equipment is as follows:

[0086]

[0087] In the formula, K g is the fault proportionality coefficient, C g is the fault curvature coefficient, S t It is the health index of key power distribution equipment.

[0088] By associating the failure probability of key power distribution equipment with its health index through the above formula, the traditional failure probability form can be transformed into a health index that can be quantified and evaluated.

[0089] Assume that the minimum failure rate λ calculated from historical statistics of key power distribution equipment min The corresponding health index is 4.9, and the historical statistical average failure probability λ a The corresponding health index is 3; the fault proportion coefficient and fault curvature coefficient in the above formula are calculated as follows:

[0090]

[0091]

[0092] By associating the failure and health status of key power distribution equipment through the above model, the health index S of key power distribution equipment can be calculated: t .

[0093] S2.3: Obtain and calculate the load transfer rate η of the branch in the distribution network i′ , the qualified rate R of the load transfer element of the grid N-1 and the overall invulnerability index G′ of the distribution network z , and then obtain the grid strength index W of the medium voltage distribution network t .

[0094] Specifically, the load transfer rate of the branch in the distribution network is calculated to examine the safety and flexibility of the grid to the load power supply. The calculation formula of the load transfer rate of the branch in the distribution network is as follows:

[0095]

[0096] Where L′ i′ is the total load capacity of branch i′; L′ i′t It is the load capacity that can be successfully transferred when branch i′ fails or is under maintenance.

[0097] Specifically, the grid implements N-1 component qualification rate verification. When m' lines among the n' lines of the grid fail or are under maintenance, the downstream lines they carry can be successfully transferred to the adjacent power supply path. The calculation formula of the grid N-1 load transfer component qualification rate R is as follows:

[0098]

[0099] Specifically, the overall invulnerability index G′ of the distribution network is calculated z The specific process is as follows:

[0100] Calculate the importance G of each network node o in the distribution network z (o):

[0101] G z (o) = f o / l o

[0102] In the formula, f o is the edge weight of node o, l o is the location importance of node o.

[0103] According to the importance of each network node o in the distribution network, the overall invulnerability index G′ of the distribution network is calculated. z .

[0104]

[0105]

[0106]

[0107]

[0108] In the formula, is the sum of the importance of O nodes in the distribution network; is the sum of the importance of O nodes in the fully connected network with edge weight 1 in the distribution network; G z is the initial value of the invulnerability index; is the mean importance of network nodes; D′ is the standard deviation of the importance of network nodes, λ ε is the anti-destruction coefficient.

[0109] As an optional implementation, in this embodiment, ε The value is 20.

[0110] Specifically, the grid strength index W of the medium voltage distribution network is t The formula is:

[0111]

[0112] S3: Count the historical environmental characteristics of the medium-voltage distribution network and collect real-time environmental characteristics. According to the historical and real-time environmental characteristics, build an environmental numerical correction model to calculate the environmental correction coefficient k, and calculate and correct the health index of key distribution equipment in S2.

[0113] Specifically, the key power distribution equipment is exposed to the natural environment. This embodiment takes into account the impact of the natural environment on the key power distribution equipment and calculates and corrects its health index.

[0114] S3.1: Count the historical environmental characteristics in the medium voltage distribution network and collect real-time environmental characteristics. The environmental characteristics include: sudden change value of ambient temperature, number of lightning strikes, wind speed, precipitation, relative humidity and salt fog. The environmental characteristic data corresponding to each historical environmental characteristic constitute the environmental data set B.

[0115] B={Y mn}, m=1,2,…,6; n=1,2,…,N*24*365

[0116] In the formula, m is the index of the environmental feature, n is the index of the environmental feature data, and Y mn is the nth environmental feature data, which corresponds to the mth environmental feature.

[0117] Preferably, a set of environmental characteristic data is collected every hour, and the collection period is N years.

[0118] S3.2: Determine the maximum value, minimum value, maximum score value and minimum score value of each historical environmental characteristic based on the environmental data set, and then determine the real-time environmental characteristic score value based on the real-time environmental characteristics. Obtain the impact score of all environmental characteristics of the key distribution equipment in operation based on the maximum score value and the minimum score value combined with the real-time environmental characteristic score value.

[0119] Specifically, the calculation formulas for the maximum and minimum scores of each environmental feature are as follows:

[0120]

[0121]

[0122]

[0123] Where Y m.max is the maximum value of the mth environmental feature, Y m.min is the minimum value of the mth environmental feature, λ m is the weight value of each environmental feature, is the standard deviation of the mth environmental characteristic, is the average value of the mth environmental feature, W max , W min are the maximum and minimum score values ​​of each environmental feature, respectively.

[0124] Specifically, since the six environmental characteristics all play a role in reducing the health of distribution equipment, Y m.max and Y m.min All take positive values.

[0125] Specifically, the calculation formula for the impact score of all environmental characteristics of key power distribution equipment in operation is as follows:

[0126]

[0127] Where W is the real-time score of the environmental feature data corresponding to each environmental feature, Y′ m is the real-time measured environmental feature data corresponding to the mth environmental feature, W w Provides a score for the impact of all environmental characteristics on critical power distribution equipment.

[0128] S3.3: According to the results of S3.2, linearly fit the environmental characteristics with the failure probability of the key power distribution equipment in S2.2, and then solve the environmental correction coefficient k. Use the environmental correction coefficient k to calculate and correct the health index of the key power distribution equipment in S2 to obtain the corrected health index kS of the key power distribution equipment. t .

[0129] Specifically, the change of environmental factors has a negative impact on the power distribution equipment exposed to the outside air, that is, the higher the degree of change of environmental factors and the more frequent the change, the more unfavorable the health of the power distribution equipment is, which is linearly correlated. Therefore, in addition to considering the maximum and minimum values ​​of the environmental characteristic data, the environmental characteristic data in the environmental data set B should be linearly fitted with the failure probability of the key power distribution equipment to solve the environmental correction coefficient k.

[0130] Specifically, the calculation formula of the environmental correction coefficient k is as follows:

[0131]

[0132] In the formula, ρ is the proportionality coefficient and h is the constant coefficient.

[0133] As an optional implementation, this embodiment uses the gradient descent linear fitting algorithm of the prior art combined with the environmental data set to solve and obtain ρ=0.75; h=0.18 is taken to ensure that when the environment is harsh, the corrected distribution network health index is at the lowest (0,1) level and still maintains a certain maintenance capability.

[0134] S4: Establish and perform weight analysis on the physical health index and grid strength index in S2 and the health index of key distribution equipment modified in S3 based on the AHP-TOPSIS model, and solve the weight of each health index ω b , and then construct the distribution network health index model to obtain the health index Z of the medium voltage distribution network.

[0135] S4.1: The health index data corresponding to the seven health indicators, namely, load transfer rate, line loss rate, voltage qualification rate, harmonic qualification rate, power outage duration, corrected key distribution equipment health index and grid strength index, are used to construct a multi-attribute feature matrix D = [d ab ]; Normalize the multi-attribute feature matrix D to obtain the normalized matrix D′=[d′ ab ],in:

[0136]

[0137] Where a = 1, 2, ..., N * 365 * 24; b = 1, 2, ..., 7; a is the index of time in hours, b is the index of health indicators, d′ ab is the normalized health indicator data, dab Health indicator data.

[0138] Considering the numerical health indicators: the health index of key distribution equipment and the grid strength index are the most basic indicators of the health of the medium-voltage distribution network, the weight vector β = [β1, β2, β3, β4, β5, β6, β7] is constructed. T , and the initial value weighting of the 7 indicators is performed in the average weighting method to obtain the weight vector β = [0.1, 0.1, 0.1, 0.1, 0.1, 0.25, 0.25]; then the weighted normative matrix C = [c ab ],in:

[0139]

[0140] After the weighted normalized matrix C is calculated, the weights of the elements in C are solved. The calculation formula is as follows:

[0141]

[0142] In the formula, ω b (b=1, 2, 3, 4, 5, 6, 7) are the weight values ​​of the 7 health indicators finally solved.

[0143] S4.2: Health indicators based on S4.1 and the weight values ​​ω corresponding to each health indicator b , construct the distribution network health index model and obtain the distribution network health index Z.

[0144] Specifically, a set of 7 health indicator data are collected in real time, denoted as: b , (b=1,2,3,4,5,6,7); and perform linear combination to form the joint state quantity:

[0145]

[0146] Exponentially concatenate them to get the likelihood ratio:

[0147]

[0148] In the formula, x b is the real-time health index, b=1,2,…,7; ω0,ω1,ω2,…,ω7 are model parameters, where ω1,ω2,…,ω7 are the weights of the seven health index feature quantities, and ω0 is used to determine the horizontal position of the Logistic function.

[0149] Determine the model parameters of Logistic:

[0150] The health index is trained by the maximum likelihood method on 7 real-time collected health indicators x bThe conditional probability r is achieved under the action of b =1, and then find the model parameter ω0 that can determine the horizontal position of the Logistic function; the likelihood function of the parameters ω0, ω1, ω2, …, ω7 is:

[0151]

[0152] Taking the natural logarithm of the above formula, we get:

[0153]

[0154] When the above logarithmic formula reaches the maximum likelihood estimate, that is, the maximum value, the solved model parameter ω0 is the required quantity.

[0155] By associating the seven health indicators of the distribution network and the Logistic model training, the calculation formula of the distribution network health index Z is finally obtained as follows:

[0156]

[0157] S5: Formulate the health level of the distribution network, and combine the distribution network health index Z obtained by S4 with the distribution network health level to realize the quantitative evaluation of the health status of the distribution network, that is, according to the numerical value HI of the distribution network health index Z obtained by S4 and the health index range corresponding to each health level of the distribution network, it is determined to which health level the numerical value HI of the distribution network health index Z obtained by S4 belongs, thereby realizing the quantitative evaluation of the health status of the distribution network.

[0158] Specifically, the formulation of the distribution network health level specifically refers to Q / GDW645-2011 "Guidelines for Distribution Network Equipment Status Evaluation" and supplements it, and then formulates five distribution network health levels: severe state, abnormal state, general health, sub-health and health. The corresponding health level quantitative value range is (0, 5). The specific description is shown in Table 1.

[0159] Table 1 Classification of health levels of medium voltage distribution network

[0160]

[0161]

[0162] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for evaluating the operating health status of a medium voltage distribution network, characterized in that: The following steps are involved: S1: Obtain the fault indicators of all distribution equipment for many years and the fault data corresponding to each fault indicator, build a fault data set for each distribution equipment, and then solve the importance index of each distribution equipment through the Gini index method based on the fault data set, and then select the key distribution equipment that can characterize the healthy operation status of the medium-voltage distribution network; S2: Obtain physical health indicators that characterize the grid and key distribution equipment obtained in S1, and construct a distribution network health index association model to calculate numerical health indicators, wherein the numerical health indicators include a health index of key distribution equipment and a grid strength index; S3: Count the historical environmental characteristics of the medium-voltage distribution network and collect real-time environmental characteristics. According to the historical and real-time environmental characteristics, build an environmental numerical correction model to calculate the environmental correction coefficient, and calculate and correct the health index of key distribution equipment in S2. S4: Perform weight analysis on the physical health index and grid strength index in S2 and the key distribution equipment health index corrected in S3, solve the weight of each health index, and then build a distribution network health index model to obtain the distribution network health index; S5: Formulate the health level of the distribution network, combine the distribution network health index obtained in S4 with the distribution network health level to achieve a quantitative assessment of the operation health of the medium-voltage distribution network, and obtain the health level of the operation health of the medium-voltage distribution network; The S3 specifically includes the following steps: S3.1: Count the historical environmental characteristics in the medium voltage distribution network and collect real-time environmental characteristics. The environmental characteristics include: sudden change value of ambient temperature, number of lightning strikes, wind speed, precipitation, relative humidity and salt fog. The environmental characteristic data corresponding to each historical environmental characteristic constitute an environmental data set. S3.2: Determine the maximum value, minimum value, maximum score value and minimum score value of each historical environmental characteristic according to the environmental data set, and then determine the real-time environmental characteristic score value according to the real-time environmental characteristics, and obtain the impact score value of all environmental characteristics of the key power distribution equipment in operation according to the maximum score value and minimum score value of each historical environmental characteristic combined with the real-time environmental characteristic score value; S3.3: Based on the results of S3.2, linearly fit the environmental characteristics with the failure probability of the key power distribution equipment in S2.2, and then solve the environmental correction coefficient k, and use the environmental correction coefficient k to calculate and correct the health index of the key power distribution equipment in S2.2; The calculation formula for the impact score of the key power distribution equipment on all environmental characteristics is as follows: Where W is the real-time score of the environmental feature data corresponding to each environmental feature, Y m ′ is the real-time measured environmental feature data corresponding to the mth environmental feature, W w W is the impact score of key power distribution equipment on all environmental characteristics. max , W min are the maximum and minimum scores of each environmental feature, respectively, m is the weight value of each environmental feature; The calculation formula of the environmental correction coefficient k is as follows: In the formula, ρ is the proportionality coefficient and h is the constant coefficient.

2. A method for evaluating the operating health status of a medium voltage distribution network according to claim 1, characterized in that: The S1 comprises the following steps: S1.1: Obtain the fault indicators of all power distribution equipment for many years and the fault data corresponding to each fault indicator, wherein the fault indicators include the number of faults, the number of affected users, the power failure load, the maintenance time and the number of replacements, and construct the fault data set of each power distribution equipment according to the fault data; S1.2: Normalize the fault data in the fault data set of each power distribution device respectively, and then solve the proportion weight and Gini value of each fault data of each power distribution device each year respectively, and calculate the importance index of each fault indicator of the power distribution device in the fault data set of the power distribution device according to the Gini value; S1.3: Select key power distribution equipment according to the importance index of each device. When the importance index α of the power distribution equipment is greater than 0.5, the power distribution equipment is judged to be a key power distribution equipment.

3. A method for evaluating the operating health status of a medium voltage distribution network according to claim 2, characterized in that: The formula of the importance index α is: In the formula, α is the importance index, A is the fault data set of the power distribution equipment, and γ j is the Gini value of the power distribution equipment in each year within N years, j is the index of the year, N is a constant, i is the index of the fault data, and p ij It is the weight ratio of each fault data of power distribution equipment in each year within N years to all fault data of that year.

4. A method for evaluating the operating health status of a medium voltage distribution network according to claim 2, characterized in that: The S2 comprises the following steps: S2.1: Obtain physical health indicators that characterize the grid and key distribution equipment, including load transfer rate, line loss rate, voltage qualification rate, harmonic qualification rate, and power outage duration; S2.2: Use extreme value type I function to obtain the failure probability of key power distribution equipment, and solve the health index of key power distribution equipment according to the failure probability; S2.3: Obtain and obtain the grid strength index of the medium voltage distribution network based on the load transfer rate of the branches in the distribution network, the qualified R of the grid N-1 load transfer element and the overall anti-destruction index of the distribution network.

5. A method for evaluating the operating health status of a medium voltage distribution network according to claim 4, characterized in that: The calculation formula of the key power distribution equipment health index is as follows: In the formula, K g is the fault proportionality coefficient, C g is the fault curvature coefficient, S t is the health index of key power distribution equipment, P I is the failure probability of key power distribution equipment under extreme type I function.

6. A method for evaluating the operating health status of a medium voltage distribution network according to claim 1, characterized in that: The S4 specifically comprises the following steps: S4.1: construct a multi-attribute feature matrix with the health indicator data corresponding to seven health indicators, namely, load transfer rate, line loss rate, voltage qualification rate, harmonic qualification rate, power outage duration, corrected key distribution equipment health index and grid strength index; normalize the multi-attribute feature matrix to obtain a normalized matrix; construct a weight vector and assign initial values ​​to the seven indicators in an average weighted manner; then construct a weighted normative matrix; perform weight calculation on the elements in the weighted normative matrix to obtain the weight value of each health indicator; S4.2: A set of seven health indicator data is collected in real time. Based on the real-time collected health indicator data, the health indicators of S4.1 and the weight values ​​corresponding to each health indicator, a distribution network health index model is constructed and trained through the maximum likelihood method to obtain the distribution network health index.

7. A method for evaluating the operating health status of a medium voltage distribution network according to claim 6, characterized in that: The calculation formula of the numerical value of the distribution network health index is as follows: In the formula, ω b is the weight value corresponding to each health indicator in S4.1, ω0 is the distribution network health model parameter, and HI is the value of the distribution network health index.

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