CN104036433A - Method for evaluating running management level of power distribution network - Google Patents

Abstract

The invention discloses a distribution network operation management level evaluation method, comprising the following steps: A: analyzing the factors affecting distribution network effect indicators, and determining the most influential indicators for distribution network power supply reliability; B: respectively Determine the calculation formula of each index in step A; C: use the triangular fuzzy analytic hierarchy process to obtain the weights of each index in the index system in step A through mathematical calculation; D: obtain the weights of each index through the solution of step C; D : Through the solution of step C, the weights of each index are obtained respectively. The invention scientifically and accurately evaluates the operation management level of the distribution network, enriches the establishment of the comprehensive index system of the distribution network, and is beneficial to the safe and stable operation of the distribution network in my country.

Description

A method for evaluating the operation and management level of distribution network

technical field

The invention relates to the field of power distribution systems, in particular to a method for evaluating the operation management level of a distribution network.

Background technique

In recent years, there has been an upsurge in the research and construction of smart grids worldwide. Focusing on the strategic goal of "building a strong smart grid" proposed by the State Grid Corporation of China, a large number of smart grid pilot projects have been carried out in various parts of my country. The more mature pilot projects include smart substations, power distribution automation, and power information collection systems.

As an important part of the power grid, the intelligence of the distribution network has become a new trend in the development of the future power grid, and it plays a decisive role in realizing the overall goal of smart grid construction. Therefore, it is of great significance to realize the evaluation of intelligent distribution network. At present, there is no scientific evaluation method of distribution network operation management level.

Contents of the invention

The purpose of the present invention is to provide a distribution network operation management level evaluation method, which can scientifically and accurately evaluate the distribution network operation management level, enrich the establishment of the distribution network comprehensive index system, and is conducive to the safety and stability of my country's distribution network to run.

The present invention adopts following technical scheme:

A distribution network operation management level evaluation method, comprising the following steps:

A: Analyze the factors affecting the distribution network effect indicators, determine the effect indicators associated with the operation management level as the distribution network power supply reliability rate, and determine that the distribution network operation management level has the greatest impact on the distribution network power supply reliability rate index of;

Among them, the indicators that the operation management level has the greatest impact on the power supply reliability of the distribution network are the data integrity of the PMS system distribution network, the elimination rate of major defects, the proportion of temporary power outages, the rate of live operation and the on-time recovery rate of power outage operations;

B: Determine the calculation formulas of each index in step A respectively;

Among them, the distribution network data integrity of the PMS system refers to the ratio of the distribution network equipment in the production information management system to the total number of all distribution network equipment within the scope of regional operation and maintenance. The calculation formula for the distribution network data integrity of the PMS system is

In the above formula, the pole-mounted switch equipment includes circuit breakers, load switches, isolating switches and fuses, distribution network overhead lines, cable lines, pole-mounted transformers, pole-mounted switch equipment, switch stations, box transformers, and ring network cabinets. Quantity, whose weights are 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.1;

The major defect elimination rate refers to the proportion of the number of major defect items that are eliminated in a timely manner, that is, the proportion of the number of major defects eliminated in a timely manner to the total number of major defects in the same period, which is used to reflect the ability to eliminate serious hidden dangers. The calculation formula for the major defect elimination rate is

The proportion of temporary power outages refers to the proportion of temporary power outages to the total power outages. Since the statistical time span of all indicators is one year, the proportion of temporary power outages refers to the proportion of temporary power outages in a year to the total power outages in a year, which is used to reflect the maintenance plan. Reasonability, the formula for calculating the proportion of temporary power outages is

The rate of live work refers to the proportion of live work projects in all projects implemented this year, which is used to reflect the scope of live work. Live work refers to all kinds of work performed on live electrical devices or close to live parts, especially any part of the worker's body or all operations that use tools, devices or instruments to enter a limited live work area, and its items Including the following aspects: ① Exchange rod knife, lightning arrester, segmented and straight cross arm, fuse tool; ② Live pole, adjusting pole; ③ Live disassembly and installation of fuse upper pile head and cable tail; ④ Load Open and install load switch blades in sections; ⑤Rinse with electrified water;

The calculation formula of live working rate is

The on-time recovery rate of power outage operations refers to the ratio of the number of power outage operations that can be restored in time within the specified time to the total number of power outage operations, which is used to reflect the efficiency of power outage operations. The formula for calculating the on-time recovery rate of power outage operations is

C: Use the triangular fuzzy analytic hierarchy process to obtain the weights of each index in the index system in step A through mathematical calculations. The implementation steps of using the triangular fuzzy analytic hierarchy process to obtain the weights are as follows:

C1: First establish a hierarchical structure;

Establish a hierarchical structure according to the importance of decision-making factors, determine the final goal as the highest level, below the highest level are various influencing factors, then the influencing sub-factor level, and finally the lowest level composed of various schemes;

C2: Construct a triangular fuzzy judgment matrix;

For a certain factor in the k-1th layer, when comparing all n _k factors related to it in the k-th layer, it is quantitatively represented by triangular fuzzy numbers, that is, the fuzzy judgment matrix The element α _ij =(l _ij ,m _ij ,u _ij ) in is a closed interval with m _ij as the median, where i, j are the i-th factor and the j-th factor of the k-th layer, l _ij , U _ij is the lower limit and upper limit of the values of a _ij in turn. The value of m _ij adopts the integer of 1-9 used in the comparison and judgment in the traditional AHP method, and its meaning is:

scale value scale meaning 1 The former is as important as the latter 3 The former is slightly more important than the latter 5 The former is more important than the latter 7 The former is strongly more important than the latter 9 The former is more important than the latter 2, 4, 6, 8 Represents the intermediate value of the above adjacent judgments

;

C3: Calculate the comprehensive fuzzy degree value;

According to the formula: Obtain the comprehensive triangular fuzzy number judgment matrix of the kth layer, where, ${\mathrm{\α}}_{\mathrm{ij}}^t=(l_{\mathrm{ij}}^t,m_{\mathrm{ij}}^t,u_{\mathrm{ij}}^t),i,j=\mathrm{1,2},...,{\mathrm{no}}_k,t=\mathrm{1,2},...,T,$ Indicates the fuzzy number given by the t-th expert comparing the i-th factor with the j-th factor in the k-th layer; yes (t=1,2,...,T) average value;

Then according to the formula: $S_i^k=\underset{j=1}{\overset{\mathrm{no}}{\mathrm{\Σ}}}{m}_{\mathrm{ij}}^k\⊗{\left(\underset{i=1}{\overset{{\mathrm{no}}_k}{\mathrm{\Σ}}}\underset{j=1}{\overset{{\mathrm{no}}_k}{\mathrm{\Σ}}}{m}_{\mathrm{ij}}^k\right)}^{-1},i=\mathrm{1,2},...,{\mathrm{no}}_k$ Calculate the comprehensive fuzzy degree value, is the comprehensive fuzzy degree value of the i-th element in the k-th layer;

C4: Calculate layer weights

First use the formula Calculation,

inferred $V(S_i^k\&Greater\; Equal;S_j^k),i,j=\mathrm{1,2},...,{\mathrm{no}}_k,i\≠j;$ Among them, V means degree of possibility of

$P_{\mathrm{i\; h}}^k\left(A_i^k\right)=\min V(S_i^k\&Greater\; Equal;S_j^k),i,j=\mathrm{1,2},...,{\mathrm{no}}_k,i\≠j;$ in, Indicates the weight of each factor on the kth layer to the hth factor on the k-1th layer, Indicates the i-th factor on the k-th layer;

then to After normalization, the weight of the hth factor on the k-1 layer on the kth layer can be obtained, namely:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; ;</span>$

C5: total composite weight

After calculating the weights of each level, it is known that the ranking weight vector of the k-1th layer to the total target is:

Then the synthetic ranking W ^k of all elements on the k-th layer to the total target is:

$W^k=\underset{j=1}{\overset{{\mathrm{no}}_{k-1}}{\mathrm{\&Sigma;}}}{P}_{\mathrm{ij}}^k{W}_j^{k-1},i=\mathrm{1,2},...{\mathrm{no}}_k,$ Where W ^k is the total weight;

D: Through the solution of step C, the weights of each index are obtained respectively, as shown in the following table:

E: To evaluate the operation and management level of the distribution network. The specific steps for the evaluation of the operation and management level of the distribution network are:

E1: Check the industry standards, convert the marked value into a percentage value according to the ideal state of the distribution network, and formulate the scoring standard;

E2: By collecting the original data of the distribution network to be evaluated, calculate the corresponding index values according to the calculation formulas of each index in step B;

E3: According to the index values calculated in step E2, evaluate the indicators of the distribution network operation and management level by referring to the scoring standards formulated in step 1; and use the weights of each index obtained in step D, combined with The index value calculated in E2 can obtain the weighted value of the operation management level, which can evaluate the operation management level of the distribution network.

The present invention analyzes the factors affecting the effect indicators of the distribution network, selects the effect indicators associated with the operation management level: the power supply reliability rate of the distribution network, and analyzes the relationship between it and the operation management level of the power grid, thereby establishing an evaluation distribution system. Then use the triangular fuzzy analytic hierarchy process to obtain the weights of each index of the above index system through mathematical calculations, and finally obtain the calculated values of each index and review the established scoring standards. Evaluate the various indicators of the distribution network operation management level; and combine the weight of each indicator to evaluate the distribution network operation management level. The invention scientifically and accurately evaluates the operation management level of the distribution network, enriches the establishment of the comprehensive index system of the distribution network, and is beneficial to the safe and stable operation of the distribution network in my country.

Description of drawings

Fig. 1 is a schematic flow chart of the present invention.

Detailed ways

As shown in Figure 1, the distribution network operation management level evaluation method of the present invention comprises the following steps:

A: Analyze the factors affecting the distribution network effect indicators, determine the effect indicators associated with the operation management level as the distribution network power supply reliability rate, and determine that the distribution network operation management level has the greatest impact on the distribution network power supply reliability rate index of:

The influence of distribution network operation management level on distribution network power supply reliability:

If the operation and management level of the distribution network is high, first, it can reduce the hidden dangers of equipment failures and reduce the frequency of failures; second, it can repair the failures in time to reduce the time of power outages; The number of power outages that come, and the power supply can be restored in time according to the specified time during power outage operations.

Therefore, the indicators that have the greatest impact on the power supply reliability of the distribution network are the distribution network data integrity of the PMS system, the major defect elimination rate, the proportion of temporary power outages, the live operation rate, and the on-time recovery rate of power outage operations, as shown in Table 1. Shown:

Table 1

B: Determine the calculation formulas of each indicator in step A respectively, and the statistical time span of all indicators is one year;

Among them, the distribution network data integrity of the PMS system refers to the ratio of the distribution network equipment in the production information management system to the total number of all distribution network equipment within the scope of regional operation and maintenance. The calculation formula for the distribution network data integrity of the PMS system is

In the above formula, the pole-mounted switch equipment includes circuit breakers, load switches, isolating switches and fuses, distribution network overhead lines, cable lines, pole-mounted transformers, pole-mounted switch equipment, switch stations, box transformers, and ring network cabinets. Quantity, whose weights are 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.1;

The major defect elimination rate refers to the proportion of the number of major defect items that are eliminated in a timely manner, that is, the proportion of the number of major defects eliminated in a timely manner to the total number of major defects in the same period, which is used to reflect the ability to eliminate serious hidden dangers. The calculation formula for the major defect elimination rate is

The proportion of temporary power outages refers to the proportion of temporary power outages to the total power outages. Since the statistical time span of all indicators is one year, the proportion of temporary power outages refers to the proportion of temporary power outages in a year to the total power outages in a year, which is used to reflect the maintenance plan. Reasonability, the formula for calculating the proportion of temporary power outages is

The rate of live work refers to the proportion of live work projects in all projects implemented this year, which is used to reflect the scope of live work. Live work refers to all kinds of work performed on live electrical devices or close to live parts, especially any part of the worker's body or all operations that use tools, devices or instruments to enter a limited live work area, and its items Including the following aspects: ① Exchange rod knife, lightning arrester, segmented and straight cross arm, fuse tool; ② Live pole, adjusting pole; ③ Live disassembly and installation of fuse upper pile head and cable tail; ④ Load Open and install load switch blades in sections; ⑤Rinse with electrified water;

The calculation formula of live working rate is

The on-time recovery rate of power outage operations refers to the ratio of the number of power outage operations that can be restored in time within the specified time to the total number of power outage operations, which is used to reflect the efficiency of power outage operations. The formula for calculating the on-time recovery rate of power outage operations is

C: Utilize the triangular fuzzy analytic hierarchy process to obtain the weights of each index of the index system in step A respectively through mathematical calculations. The triangular fuzzy analytic hierarchy process belongs to the prior art in this field, so it will not be repeated here, and the triangular fuzzy in the present invention will be discussed below The implementation steps of the analytic hierarchy process to obtain the weight are described:

C1: First establish a hierarchical structure; establish a hierarchical structure according to the importance of decision-making factors, and determine the final goal as the highest level. The lowest level of program composition.

C2: Construct a triangular fuzzy judgment matrix;

For a certain factor in the k-1th layer, when comparing all n _k factors related to it in the k-th layer, it is quantitatively represented by triangular fuzzy numbers, that is, the fuzzy judgment matrix The element α _ij =(l _ij ,m _ij ,u _ij ) in is a closed interval with m _ij as the median, where i, j are the i-th factor and the j-th factor of the k-th layer, l _ij , U _ij is the lower limit and upper limit of the values of a _ij in turn. The value of m _ij adopts the integers from 1 to 9 used in the comparison and judgment in the traditional AHP method, and its meaning is shown in Table 2:

scale value scale meaning 1 The former is as important as the latter 3 The former is slightly more important than the latter 5 The former is more important than the latter 7 The former is strongly more important than the latter 9 The former is more important than the latter 2, 4, 6, 8 Represents the intermediate value of the above adjacent judgments

Table 2 Scale of comparative judgment matrix

C3: Calculate the comprehensive fuzzy degree value;

According to the formula:

${\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}^{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&CirclePlus;</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&CirclePlus;</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; \&CirclePlus;</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; )</span>$

Obtain the comprehensive triangular fuzzy number judgment matrix of the kth layer, where, ${\mathrm{\&alpha;}}_{\mathrm{ij}}^t=(l_{\mathrm{ij}}^t,m_{\mathrm{ij}}^t,u_{\mathrm{ij}}^t),i,j=\mathrm{1,2},...,{\mathrm{no}}_k,t=\mathrm{1,2},...,T,$ Indicates the fuzzy number given by the t-th expert comparing the i-th factor with the j-th factor in the k-th layer; yes (t=1,2,...,T) mean.

Then according to the formula:

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; \&CircleTimes;</span>{<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; ij</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1,2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}$

Calculate the comprehensive fuzzy degree value, is the comprehensive fuzziness value of the i-th element in the k-th layer.

C4: Calculate layer weights

First use the formula Calculation,

inferred $V(S_i^k\&Greater\; Equal;S_j^k),i,j=\mathrm{1,2},...,{\mathrm{no}}_k,i\&NotEqual;j;$

Among them, V means degree of possibility.

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i\; h</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1,2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ;</span>$

in, Indicates the weight of each factor on the kth layer to the hth factor on the k-1th layer, Indicates the i-th factor on the k-th layer.

then to After normalization, the weight of the hth factor on the k-1 layer on the kth layer can be obtained, namely:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; ;</span>$

C5: total composite weight

After calculating the weights of each level, it is known that the ranking weight vector of the k-1th layer to the total target is:

Then the synthetic ranking W ^k of all elements on the k-th layer to the total target is:

$W^k=\underset{j=1}{\overset{{\mathrm{no}}_{k-1}}{\mathrm{\&Sigma;}}}{P}_{\mathrm{ij}}^k{W}_j^{k-1},i=\mathrm{1,2},...{\mathrm{no}}_k---\left(10\right),$ Where W ^k is the total weight;

D: Through the solution of step C, the weights of each index are obtained respectively, as shown in Table 3;

table 3

E: Conduct distribution network operation management level evaluation; the specific steps for distribution network operation management level evaluation are:

E1: Consult the industry standard, convert the marked value of the indicator under the ideal state of the distribution network into a percentage value, and formulate the scoring standard;

The scoring standard refers to the conversion of various raw data into a directly comparable standardized format through a certain scale system; the scoring scale can use a hundred-point system, a ten-point system, and a five-point system. In this embodiment, a percentage system is adopted, and the higher the score, the better the performance.

The index scoring standard mainly refers to the type and ideal value of the index. The index types are divided into positive index, negative index and intermediate value index. The positive index means that the higher the value of the index, the better, and the negative index means that the lower the value, the better. , and the median index is best to take a certain value or interval in the middle.

In the process of evaluating the distribution network, it is necessary to determine the reasonable range of the index value. When a certain parameter is within this reasonable range, it means that from this point of view, the parameters of the power grid or the system operation status represented by it basically meet the requirements; otherwise, it is not. The reasonable range of the index is the ideal value of the index.

By searching the relevant industry standards of the distribution network, the index types and ideal values of the operation management level evaluation system (as shown in Table 4) and the scoring standards of the operation management level evaluation system (as shown in Table 5) are formulated:

Index types and ideal values of the operation management level evaluation system

Table 4

Scoring criteria for the evaluation system of operation management level

table 5

E2: By collecting the original data of the distribution network to be evaluated, calculate the corresponding index values according to the calculation formulas of each index in step B;

E3: According to the index values calculated in step E2, evaluate the indicators of the distribution network operation and management level by referring to the scoring standards formulated in step 1; and use the weights of each index obtained in step D, combined with The index value calculated in E2 can obtain the weighted value of the operation management level, which can evaluate the operation management level of the distribution network.

For example, using this method to evaluate the operation management level of a distribution network, the original data and calculation results are shown in Table 6. It is calculated that the distribution network data integrity index value of the PMS system is 89.45% (233552/261094), and the score is 69.80 by referring to the scoring standard. It can be seen that the PMS system data integrity of the distribution network is good and the score is high. At the same time, by multiplying the scores of each indicator by their respective weight values, and then summing up, the score of the distribution network operation management level is 67.71 (69.80*0.20+100*0.25+32.61*0.15+23.09*0.20+96.18*0.20). The evaluation score belongs to the qualified level, indicating that the operation and management level of the distribution network is relatively good, and the sub-indicators that have a greater impact are the proportion of temporary power outages and the live operation rate, which need to be greatly improved.

Table 6 Evaluation example of operation management level

Claims (1)

1. A distribution network operation management level evaluation method, is characterized in that, comprises the following steps: A: Analyze the factors affecting the distribution network effect indicators, determine the effect indicators associated with the operation management level as the distribution network power supply reliability rate, and determine that the distribution network operation management level has the greatest impact on the distribution network power supply reliability rate index of; Among them, the indicators that the operation management level has the greatest impact on the power supply reliability of the distribution network are the data integrity of the PMS system distribution network, the elimination rate of major defects, the proportion of temporary power outages, the rate of live operation and the on-time recovery rate of power outage operations; B: Determine the calculation formulas of each index in step A respectively; Among them, the distribution network data integrity of the PMS system refers to the ratio of the distribution network equipment in the production information management system to the total number of all distribution network equipment within the scope of regional operation and maintenance. The calculation formula for the distribution network data integrity of the PMS system is In the above formula, the pole-mounted switch equipment includes circuit breakers, load switches, isolating switches and fuses, distribution network overhead lines, cable lines, pole-mounted transformers, pole-mounted switch equipment, switch stations, box transformers, and ring network cabinets. Quantity, whose weights are 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.1; The major defect elimination rate refers to the proportion of the number of major defect items that are eliminated in a timely manner, that is, the proportion of the number of major defects eliminated in a timely manner to the total number of major defects in the same period, which is used to reflect the ability to eliminate serious hidden dangers. The calculation formula for the major defect elimination rate is The proportion of temporary power outages refers to the proportion of temporary power outages to the total power outages. Since the statistical time span of all indicators is one year, the proportion of temporary power outages refers to the proportion of temporary power outages in a year to the total power outages in a year, which is used to reflect the maintenance plan. Reasonability, the formula for calculating the proportion of temporary power outages is The rate of live work refers to the proportion of live work projects in all projects implemented this year, which is used to reflect the scope of live work. Live work refers to various operations performed on live electrical devices or close to live parts, especially any part of the worker's body or all operations that use tools, devices or instruments to enter a limited live work area, and its items Including the following aspects: ① Exchange rod knife, lightning arrester, segmented and straight cross arm, fuse tool; ② Live pole, adjusting pole; ③ Live disassembly and assembly of the pile head and cable tail on the fuse; ④ With load Open and install load switch blades in sections; ⑤Rinse with electrified water; The calculation formula of live working rate is The on-time recovery rate of power outage operations refers to the ratio of the number of power outage operations that can be restored in time within the specified time to the total number of power outage operations, which is used to reflect the efficiency of power outage operations. The formula for calculating the on-time recovery rate of power outage operations is C: Use the triangular fuzzy analytic hierarchy process to obtain the weights of each index in the index system in step A through mathematical calculations. The implementation steps of using the triangular fuzzy analytic hierarchy process to obtain the weights are as follows: C1: First establish a hierarchical structure; Establish a hierarchical structure according to the importance of decision-making factors, determine the final goal as the highest level, below the highest level are various influencing factors, then the influencing sub-factor level, and finally the lowest level composed of various schemes; C2: Construct a triangular fuzzy judgment matrix; For a certain factor in the k-1th layer, when comparing all nk factors related to it in the k-th layer, it is quantitatively represented by a triangular fuzzy number, that is, the fuzzy judgment matrix The element α _ij =(l _ij ,m _ij ,u _ij ) in is a closed interval with m _ij as the median, where i, j are the i-th factor and the j-th factor of the k-th layer, l _ij , U _ij is the lower limit and upper limit of the values of a _ij in turn. The value of m _ij adopts the integer of 1-9 used in the comparison and judgment in the traditional AHP method, and its meaning is: scale value scale meaning 1 The former is as important as the latter 3 The former is slightly more important than the latter 5 The former is more important than the latter 7 The former is strongly more important than the latter 9 The former is more important than the latter 2, 4, 6, 8 Represents the intermediate value of the above adjacent judgments ; C3: Calculate the comprehensive fuzzy degree value; According to the formula: Obtain the comprehensive triangular fuzzy number judgment matrix of the kth layer, where, ${\mathrm{\&alpha;}}_{\mathrm{ij}}^t=(l_{\mathrm{ij}}^t,m_{\mathrm{ij}}^t,u_{\mathrm{ij}}^t),i,j=\mathrm{1,2},...,{\mathrm{no}}_k,t=\mathrm{1,2},...,T,$ Indicates the fuzzy number given by the t-th expert comparing the i-th factor with the j-th factor in the k-th layer; yes (t=1,2,...,T) average value; Then according to the formula: $S_i^k=\underset{j=1}{\overset{\mathrm{no}}{\mathrm{\&Sigma;}}}{m}_{\mathrm{ij}}^k\&CircleTimes;{\left(\underset{i=1}{\overset{{\mathrm{no}}_k}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{{\mathrm{no}}_k}{\mathrm{\&Sigma;}}}{m}_{\mathrm{ij}}^k\right)}^{-1},i=\mathrm{1,2},...,{\mathrm{no}}_k$ Calculate the comprehensive fuzzy degree value, is the comprehensive fuzzy degree value of the i-th element in the k-th layer; C4: Calculate layer weights First use the formula Calculation, inferred $V(S_i^k\&Greater\; Equal;S_j^k),i,j=\mathrm{1,2},...,{\mathrm{no}}_k,i\&NotEqual;j;$ Among them, V means degree of possibility of $P_{\mathrm{i\; h}}^k\left(A_i^k\right)=\min V(S_i^k\&Greater\; Equal;S_j^k),i,j=\mathrm{1,2},...,{\mathrm{no}}_k,i\&NotEqual;j;$ in, Indicates the weight of each factor on the kth layer to the hth factor on the k-1th layer, Indicates the i-th factor on the k-th layer; then to After normalization, the weight of the hth factor on the k-1 layer on the kth layer can be obtained, namely: ${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; h</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; T</span>}}<span\; class="notranslate">\; ;</span>$ C5: total composite weight After calculating the weights of each level, it is known that the ranking weight vector of the k-1th layer to the total target is: Then the synthetic ranking W ^k of all elements on the k-th layer to the total target is: $W^k=\underset{j=1}{\overset{{\mathrm{no}}_{k-1}}{\mathrm{\&Sigma;}}}{P}_{\mathrm{ij}}^k{W}_j^{k-1},i=\mathrm{1,2},...{\mathrm{no}}_k,$ Where W ^k is the total weight; D: Through the solution of step C, the weights of each index are obtained respectively, as shown in the following table: E: To evaluate the operation and management level of the distribution network. The specific steps for the evaluation of the operation and management level of the distribution network are: E1: Check the industry standards, convert the marked value into a percentage value according to the ideal state of the distribution network, and formulate the scoring standard; E2: By collecting the original data of the distribution network to be evaluated, calculate the corresponding index values according to the calculation formulas of each index in step B; E3: According to the index values calculated in step E2, evaluate the indicators of the distribution network operation and management level by referring to the scoring standards formulated in step 1; and use the weights of each index obtained in step D, combined with The index value calculated in E2 can obtain the weighted value of the operation management level, which can evaluate the operation management level of the distribution network.