Comprehensive evaluation method for utilization efficiency of power distribution network equipment

Abstract

The invention discloses a power distribution network equipment utilization efficiency comprehensive evaluation method, and relates to the technical field of power distribution network equipment evaluation, and the method comprises the steps: S1, constructing a multi-dimensional evaluation index system; s2, determining the basic weight of each index by adopting an analytic hierarchy process; s3, adjusting the basic weight by adopting a grey correlation threshold variable weight method; and S4, obtaining original monitoring and statistical data of each index. According to the method, an evaluation index system including equipment aging degree, maintenance cost, operation risk, distributed energy access, electric vehicle charging load and other novel factors is constructed, multi-dimensional and comprehensive evaluation of the utilization efficiency of the power distribution network equipment is realized, the weight determined by the analytic hierarchy process is adjusted by adopting a grey correlation threshold variable weight method, and the utilization efficiency of the power distribution network equipment is improved. More accurate index weight distribution is realized, a comprehensive evaluation value is calculated through combination of multi-dimensional indexes and optimized weights, and evaluation that the utilization efficiency of power distribution network equipment is closer to the actual operation condition is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network equipment evaluation, and particularly relates to a power distribution network equipment utilization efficiency comprehensive evaluation method. BACKGROUND

[0002] The power distribution network is a key link connecting the power transmission network and the user side in the power system, and the power distribution network equipment is a core component of the power distribution network, covering various power facilities such as transformers, switch devices, lines, etc., and its normal operation directly determines whether the power resources can be stably and efficiently delivered to the user end. The power distribution network equipment utilization efficiency comprehensive evaluation refers to a process of comprehensively analyzing and quantitatively evaluating the resource utilization degree and operation performance of the power distribution network equipment in the operation process by constructing a scientific index system and evaluation method. The development of the power distribution network equipment utilization efficiency comprehensive evaluation can help the power operation and maintenance department accurately master the equipment operation state, timely find the weak links in the equipment operation, provide data support for the equipment operation plan formulation and upgrading and transformation decision, and has important significance for improving the overall operation stability of the power distribution network, reducing power loss, and ensuring the reliability of power supply.

[0003] However, the existing power distribution network equipment utilization efficiency comprehensive evaluation technology still has certain defects. The existing power distribution network equipment utilization efficiency evaluation index system is not comprehensive, only focuses on conventional indexes such as load rate and availability, does not include key factors reflecting equipment health and economic operation such as equipment aging degree, maintenance cost and operation risk, and does not consider new power factors such as distributed energy access and electric vehicle charging load, and the evaluation lacks pertinence and foresight. When determining the index weight by the traditional evaluation method, a single method such as the analytic hierarchy process is often used, the correlation between indexes is ignored, the weight distribution deviates from the actual situation, the accuracy of the final evaluation result is insufficient, and the equipment utilization efficiency cannot be truly reflected. Therefore, it is of great significance to develop a power distribution network equipment utilization efficiency comprehensive evaluation method. SUMMARY

[0004] The technical problem to be solved by the present application is to overcome the above-mentioned defects existing in the prior art, and to provide a power distribution network equipment utilization efficiency comprehensive evaluation method which realizes multi-dimensional and comprehensive evaluation of the power distribution network equipment utilization efficiency by constructing an evaluation index system including equipment aging degree, maintenance cost, operation risk and new factors such as distributed energy access and electric vehicle charging load, realizes more accurate index weight distribution by adjusting the weight determined by the analytic hierarchy process by using the gray correlation threshold variable weight method, and realizes more actual operation condition-based evaluation of the power distribution network equipment utilization efficiency by combining multi-dimensional indexes and optimized weight to calculate a comprehensive evaluation value.

[0005] The technical solution adopted by the present application to solve the technical problem is as follows: a power distribution network equipment utilization efficiency comprehensive evaluation method, comprising the following steps: S1, construct a multi-dimensional evaluation index system, retain the load rate and availability rate conventional indexes for reflecting the basic running state of the equipment, add the aging degree index reflecting the health state of the equipment, the maintenance cost index reflecting the economic operation situation, and the operation risk index representing the safe operation level, and simultaneously incorporate the distributed energy access index and the electric vehicle charging load index affected by the new power industry form; S2, determine the basic weight of each index by using the analytic hierarchy process, first build a hierarchical structure with the equipment utilization efficiency evaluation as the target layer, the operation state, the health state, the economic operation, the safe operation, and the new power influence as the criterion layer, and the specific indexes as the index layer, invite professionals in the power field to compare and score the importance of each index under the criterion layer, construct a judgment matrix according to the scoring results, conduct consistency test on the judgment matrix, calculate the basic weight of each index after the test is passed; S3, adjust the basic weight by using the grey correlation threshold variable weight method, calculate the grey correlation degree between each index, identify the strong correlation index combination according to the correlation degree, set the correlation threshold according to the actual evaluation requirements, dynamically correct the basic weight of the strong correlation index according to the threshold, and obtain the optimized weight of each index; S4, obtain the original monitoring and statistical data of each index, convert the data into dimensionless evaluation values by using corresponding normalization methods for different types of indexes, multiply the evaluation values of each index by the corresponding optimized weight, and sum them up to obtain the comprehensive evaluation value of the distribution network equipment utilization efficiency.

[0006] Preferably, in step S1, when determining the equipment aging degree index, three types of core data are collected, including collecting equipment cumulative running time data , historical fault frequency data during operation , and performance detection data of insulation components and conductive components , , , , , , , , , , , , ,

[0007] Preferably, in step S1, when determining the maintenance cost index, three types of accounting are divided: daily inspection cost, component replacement cost, and fault repair cost. Daily inspection cost is calculated monthly, covering inspection personnel salary, equipment wear and tear, and consumable cost. Component replacement cost is calculated based on component model, quantity, and corresponding procurement, transportation, and installation cost. Fault repair cost includes labor, consumables, and auxiliary equipment usage cost. The total value of the maintenance cost index is obtained by aggregating the three types of cost data.

[0008] Preferably, in step S1, when determining the operation risk index, risk sub-indices are constructed from three dimensions, including: equipment environment risk sub-index , which is evaluated by referring to environmental data such as annual temperature and humidity range, average annual concentration of corrosive gas, and frequency of extreme weather in the equipment installation area; load fluctuation risk sub-index , which is calculated based on actual equipment operation load data in the past three months, and the monthly maximum value of the difference between daily load peak value and daily load average value is taken as the basis for load fluctuation risk assessment; and component degradation risk sub-index , which is determined based on the ratio of component cumulative operation time to design life and historical degradation rate data of the component. The final value of the operation risk index is calculated by assigning values to each risk sub-index according to their impact on equipment operation, using the formula , where , , are the weight coefficients of each risk sub-index, and the weight coefficients , , are determined based on the proportion of equipment failures caused by environmental factors, load fluctuation, and component degradation in the total number of failures in the past five years.

[0009] Preferably, in step S1, when determining the distributed energy access index, the types of distributed power sources connected to the distribution network are first identified, mainly including photovoltaic power, wind power, and small gas turbine power. Actual output data of each type of distributed power source is collected on a daily basis, and the total output value of each type of distributed power source per day is calculated and denoted as . At the same time, load demand data of the distribution network for the corresponding period is collected and denoted as . The ratio of the total output of distributed power sources to the load demand of the distribution network for the corresponding period is calculated, and the arithmetic mean of the ratio data for each day in a month is taken as the value of the distributed energy access index.

[0010] Preferably, in step S1, when determining the electric vehicle charging load index, the number of charging stations, the power level of charging piles, and the daily average charging duration in the area covered by the distribution network are collected, and the daily average total charging load in the area is calculated. , the calculation process is: statistics of daily charging time data of each charging station in daily cycle, combined with charging pile power level to calculate the daily average charging load of single charging pile, and then the daily average charging load of all charging piles in the region is summarized to obtain the daily average total charging load in the region, and the rated power supply capacity data of the power distribution network in the corresponding period is collected , the value of electric vehicle charging load change rate index is calculated by formula , wherein, is the load influence correction coefficient, the correction coefficient is determined according to the load characteristic curve of power distribution network, the load peak and valley distribution of power distribution network in each time period of each day in the past year is analyzed, the coincidence degree of charging load peak period and overall load peak period of power distribution network is calculated, and the coincidence degree value is taken as the value basis of .

[0011] Preferably, in step S2, when inviting experts to score the two-by-two comparison of indexes, the expert screening conditions are as follows: Engaged in power system operation and maintenance, equipment evaluation and related fields for more than 5 years, with practical experience in power distribution network equipment operation management and evaluation; The number of experts is set to 5-8, covering different professional directions of equipment operation and maintenance, dispatching and evaluation; After collecting the scoring results of all experts, the mean value of two-by-two comparison scoring of each index is calculated; If the score of a certain expert for a certain group of indexes deviates from the mean value by more than 15%, the expert is fed back the deviation and the mean value data, invited to review the scoring basis combined with professional cognition, and adjust the scoring results again; After the review of all expert scoring is completed, the final scoring results are integrated to build a judgment matrix.

[0012] Preferably, in step S4, during the normalization process, the index types are first distinguished. The load rate, availability rate and distributed energy access amount index, the higher the value, the more it can reflect the equipment utilization efficiency advantage, and is divided into positive index. The evaluation value is calculated by formula , the maintenance cost, operation risk and equipment aging degree index, the higher the value, the less conducive to the efficient operation of the equipment, and is divided into negative index. The evaluation value is calculated by formula , wherein, is the evaluation value of the positive index, is the evaluation value of the negative index, is the original actual data, is the optimal value, and the optimal value is determined according to the equipment operation standard released by the industry, the historical optimal data of the same type of equipment or the rated parameters of the equipment design.

[0013] The power distribution network equipment utilization efficiency comprehensive evaluation method has the following beneficial effects: The method constructs an evaluation index system containing device aging degree, maintenance cost, operation risk, distributed energy access, electric vehicle charging load and other new factors, solves the problem of lack of pertinence and foresight of the existing index system, realizes multi-dimensional and comprehensive evaluation of the utilization efficiency of the power distribution network equipment, adjusts the weight determined by the analytic hierarchy process by using the gray correlation threshold variable weight method, solves the problem of inaccurate evaluation results caused by ignoring the correlation between indexes in the traditional method, realizes more accurate index weight distribution, finally calculates the comprehensive evaluation value through the combination of multi-dimensional indexes and optimized weights, realizes more actual operation condition evaluation of the utilization efficiency of the power distribution network equipment, provides more reliable basis for the power department to make operation and maintenance and transformation decisions, and further guarantees the stable, economic and efficient operation of the power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a flowchart of the power distribution network equipment utilization efficiency comprehensive evaluation method embodiment of the present application; Figure 2 is a flowchart of the power distribution network equipment utilization efficiency comprehensive evaluation method embodiment of the present application. DETAILED DESCRIPTION

[0015] The present application will be further described below in conjunction with the embodiments and drawings.

[0016] Embodiment one Applied to the comprehensive evaluation scene of the utilization efficiency of 10kV distribution transformer in urban power distribution network.

[0017] With the large-scale access of urban distributed photovoltaic and wind power projects and the rapid popularization of electric vehicle charging facilities, the traditional evaluation method relying only on load rate and availability cannot accurately reflect the actual operation state of the distribution transformer. A power company needs to carry out efficiency evaluation on the 10kV distribution transformer in the jurisdiction for 10 years to identify low-efficiency operation equipment, develop targeted operation and transformation plan, and guarantee the stability and economy of power supply, such as Figure 1 、 2 As shown in the figure, the power distribution network equipment utilization efficiency comprehensive evaluation method proposed by the present application is used to carry out evaluation work, including the following steps: S1, Construct a multi-dimensional evaluation index system, retain the load rate and availability rate conventional indicators reflecting the basic running state of the equipment, add the aging degree indicator reflecting the health status of the equipment, the maintenance cost indicator reflecting the economic operation situation, the operation risk indicator representing the safety operation level, and the distributed energy access indicator and the electric vehicle charging load indicator affected by the new power industry; wherein, the aging degree indicator is calculated by collecting the cumulative running time of the equipment, the historical fault frequency during operation, and the performance detection data of the insulation and conducting parts, and then normalized and weighted; the maintenance cost indicator covers three types of costs, including daily inspection, component replacement, and fault repair, and the total value is obtained by summarizing the relevant fees; the operation risk indicator is constructed from three dimensions of the environment, load fluctuation, and component degradation, and is calculated by combining the influence degree of each sub-indicator; the distributed energy access indicator is calculated by collecting the actual output of the distributed power and the load demand data of the distribution network on a daily basis, and taking the monthly average value; the electric vehicle charging load indicator is calculated by collecting the number of charging stations, the power level of charging piles, and the daily average charging time, and combining the rated power supply capacity of the distribution network and the correction coefficient; S2, The analytic hierarchy process is used to determine the basic weight of each indicator. First, a hierarchical structure is built with "distribution transformer utilization efficiency evaluation" as the target layer, "operation state", "health state", "economic operation", "safe operation", "new power influence" as the criterion layer, and seven specific indicators as the indicator layer. Seven experts are invited to meet the screening conditions, covering equipment operation and maintenance, dispatching, and evaluation of different professional directions, and having worked in power system operation and maintenance, equipment evaluation, and related fields for more than 5 years, with practical experience in distribution network equipment operation management and evaluation. The importance of each indicator under the criterion layer is compared and scored. After collecting all the expert scoring results, the average score of each indicator is calculated. If the score of a certain group of indicators by a certain expert deviates from the average by more than 15%, the deviation and the average data are fed back to the expert, who is invited to review the scoring basis and adjust the scoring results. After the scoring review of all experts is completed, the final scoring results are integrated to build a judgment matrix, which is subjected to consistency test. After the test is passed, the basic weight of each indicator is calculated. S3, The basic weight is adjusted by using the gray correlation threshold variable weight method. The gray correlation degree between each indicator is calculated. According to the correlation degree, the strong correlation indicator combination is identified. According to the correlation threshold, the basic weight of the strong correlation indicator is dynamically modified to obtain the optimized weight of each indicator. S4, Obtain the original monitoring and statistical data of each indicator. Different types of indicators are converted into dimensionless evaluation values by corresponding normalization methods. The evaluation values of each indicator are multiplied by the corresponding optimized weight and summed up to obtain the utilization efficiency comprehensive evaluation value of each 10kV distribution transformer.

[0018] In step S1, when determining the equipment aging degree index, three types of core data are comprehensively collected, including the cumulative running time data of the target distribution transformer , the historical fault frequency data during operation , and the performance detection data of insulation components and conductive components . The three types of data are normalized to obtain 、 、 . Seven experts with more than 10 years of experience in distribution network equipment operation and maintenance are invited. After three rounds of Delphi method independent scoring and opinion collection, the normalized 、 、 are assigned weights according to the equipment type and operation characteristics 、 、 . The aging degree index value is calculated by the formula .

[0019] In step S1, when determining the maintenance cost index, the maintenance cost index is divided into three types of accounting types: daily inspection cost, component replacement cost, and fault repair cost. The daily inspection cost is calculated monthly, including the inspection personnel salary, equipment wear and tear cost such as infrared temperature measuring instrument, and consumable cost such as insulation gloves. The component replacement cost is calculated based on the type and quantity of components such as tap switch and bushing replaced in the past year, as well as the corresponding procurement, transportation and installation costs. The fault repair cost includes labor cost, replacement of fuses and other consumable costs, and the use cost of auxiliary equipment such as cranes. The total value of the maintenance cost index is obtained by aggregating the three types of cost data.

[0020] In step S1, when determining the operation risk index, risk sub-indices are constructed from three dimensions, including: equipment environment risk sub-index , which is evaluated based on environmental data such as annual variation range of temperature and humidity in the transformer installation area, annual average concentration of corrosive gas around industrial areas, and frequency of extreme weather such as heavy rain and snow; load fluctuation risk sub-index , which is calculated based on the actual operation load data of the transformer in the past three months, and the difference between the daily load peak value and the daily load average value is taken as the basis for load fluctuation risk assessment; component degradation risk sub-index , which is determined based on the ratio of the cumulative running time of components such as core and winding to the designed life, as well as the historical degradation rate reflected by the dielectric loss and direct current resistance data in previous years. Based on the distribution network equipment fault case library, the proportion of transformer faults caused by environmental factors, load fluctuation and component degradation in the total number of faults in the past 5 years is calculated, and the weight coefficient of each risk sub-index is obtained after normalization 、 、 , according to the influence degree of each risk sub-index on the operation of the equipment, the final value of the operation risk index is calculated through the formula .

[0021] In step S1, when determining the distributed energy access index, it is first determined that the type of the distributed power accessed in the power distribution transformer power supply area is photovoltaic power and small wind power, the actual output data of the two types of distributed power is collected in daily cycles, and the total output value of the two types of distributed power per day is counted , and the load demand data of the transformer in the corresponding period is collected , the ratio of the daily distributed power and is calculated, and the arithmetic average value of the daily ratio data in a month is taken as the value of the distributed energy access index.

[0022] In step S1, when determining the electric vehicle charging load index, the number of charging stations in the power supply area, the power level of each station charging pile and the daily average charging time are collected, the daily average charging time data of each station is counted in daily cycles, the daily average charging load of a single charging pile is calculated combined with the power level of the charging pile, and the daily average total charging load in the region is obtained by summarizing the daily average charging load of all charging piles in the region , the rated power supply capacity data of the transformer in the corresponding period is collected , the load peak and valley distribution of the transformer in each period per day in the past year is analyzed, the coincidence degree of the charging load peak period and the overall load peak period of the transformer is calculated, and the coincidence degree value is taken as the value basis of the load influence correction coefficient , the value of the electric vehicle charging load change rate index is calculated through the formula .

[0023] In step S4, when normalizing, first distinguish the index type, the load rate, the availability rate, and the distributed energy access index are divided into positive indexes, the evaluation value is calculated through the formula , the maintenance cost, the operation risk, and the equipment aging degree index, the higher the value, the less conducive to the efficient operation of the equipment, are divided into negative indexes, and the evaluation value is calculated through the formula , wherein is the original actual data, is the optimal value determined according to the industry equipment operation standard and the historical optimal data of the same type transformer.

[0024] In summary, the embodiment of the present application realizes multi-dimensional comprehensive evaluation of the utilization efficiency of urban 10kV distribution transformers. Compared with the traditional evaluation method, not only the key factors such as equipment health, economic operation and the influence of new power industry are included, but also the index weight distribution is optimized by the grey correlation threshold weight method, avoiding the limitation of single weight determination method. The comprehensive evaluation value obtained finally can accurately reflect the actual operation efficiency of the transformer, providing reliable data support for power companies to identify inefficient equipment, develop operation and maintenance plans and make transformation decisions, which helps to improve the overall operation stability of the distribution network, reduce power loss and operation cost, and promote the development needs of the distribution network to adapt to the new power system.

[0025] Embodiment two The method is applied to the comprehensive evaluation of the utilization efficiency of 10kV high-voltage switch cabinets in industrial parks. The production load of enterprises in industrial parks fluctuates greatly, and in recent years, distributed photovoltaic power stations and electric vehicle charging stations have been gradually connected. The traditional evaluation method relying only on load rate and availability cannot cover the influence of equipment health loss, economic operation cost and new power factors on the operation efficiency of high-voltage switch cabinets. The power operation and maintenance department of a certain park needs to carry out efficiency evaluation on the 10kV high-voltage switch cabinet that has been in operation for 8 years, in order to check potential operation problems, optimize operation and maintenance resource allocation, and ensure the continuity of park production power supply, such as Figure 1 、 2 The method for comprehensive evaluation of the utilization efficiency of distribution network equipment proposed by the present application is used to carry out evaluation work, including the following steps: S1, a multi-dimensional evaluation index system is constructed, the conventional indexes of load rate and availability for reflecting the basic operation state of the equipment are retained, the aging degree index reflecting the health state of the equipment, the maintenance cost index reflecting the economic operation situation, and the operation risk index reflecting the safety operation level are newly added, and the distributed energy access index and the electric vehicle charging load index affected by the new power industry are also included. Among them, the aging degree index is calculated by collecting the cumulative operation period, historical fault frequency and performance detection data of insulation and conducting parts during operation, normalizing and assigning weights; the maintenance cost index includes three types of costs: daily inspection, component replacement and fault repair, and the total value is obtained by summarizing the relevant fees; the operation risk index is constructed from three dimensions of equipment environment, load fluctuation and component degradation, and is calculated by combining the influence degree of each sub-index; the distributed energy access index is collected according to the actual output of distributed power and the load demand data of the distribution network every day, and the monthly average value is calculated; the electric vehicle charging load index is calculated by collecting the number of charging stations, the power level of charging piles and the daily average charging time, and combining the rated power supply capacity of the distribution network and the correction coefficient; S2, determine the basic weight of each index by using the analytic hierarchy process, first build a hierarchical structure with "high-voltage switchgear utilization efficiency evaluation" as the target layer, "operation state", "health state", "economic operation", "safe operation" and "new power influence" as the criterion layer, and seven specific indexes as the index layer, invite 7 experts who meet the screening conditions, covering equipment operation and maintenance, dispatching, evaluation of different professional directions, and have been engaged in power system operation and maintenance, equipment evaluation and related fields for more than 5 years, have practical experience in distribution network equipment operation management and evaluation, compare the importance of each index under the criterion layer and score, collect all expert scoring results, calculate the average score of each index, if the deviation of a certain expert's score from the average is more than 15%, feedback the deviation and average data to the expert, invite him to review the scoring basis and adjust the scoring results, after all experts complete the review, integrate the final scoring results to build the judgment matrix, and conduct consistency test on the judgment matrix, and calculate the basic weight of each index after the test is passed; S3, adjust the basic weight by using the grey correlation threshold variable weight method, calculate the grey correlation degree between each index, identify the strong correlation index combination according to the correlation degree, set the correlation threshold according to the emphasis on safety operation and load adaptability of this high-voltage switchgear evaluation, dynamically correct the basic weight of strong correlation index according to the threshold, and get the optimized weight of each index; S4, obtain the original monitoring and statistical data of each index, convert the data into dimensionless evaluation value by corresponding normalization method for different types of indexes, multiply the evaluation value of each index by the corresponding optimized weight, and sum up to get the utilization efficiency comprehensive evaluation value of each 10kV high-voltage switchgear.

[0026] In step S1, when determining the equipment aging degree index, three types of core data are collected, including collecting the cumulative running time data of the target high-voltage switchgear , , , , , , invite 7 experts who have been engaged in distribution network equipment operation for more than 10 years, after three rounds of Delphi method independent scoring and opinion summary, allocate weights to the normalized , , , , , , calculate the aging degree index value by formula .

[0027] In step S1, when determining the maintenance cost index, three types of accounting are divided: daily inspection cost, component replacement cost, and fault repair cost. Daily inspection cost is calculated monthly, covering inspection personnel salaries, device wear and tear costs such as loop resistance testers, and consumable costs such as voltage detectors. Component replacement cost is calculated based on the types and quantities of components such as circuit breakers and disconnectors replaced in the past year, as well as corresponding procurement, transportation, and installation costs. Fault repair cost includes labor costs during fault repair, replacement of consumables such as vacuum arc chambers, and the use of auxiliary equipment such as high-voltage test vehicles. The total value of the maintenance cost index is obtained by aggregating the three types of cost data.

[0028] In step S1, when determining the operation risk index, risk sub-indices are constructed from three dimensions, including: equipment environment risk sub-index , which is evaluated based on environmental data such as the annual variation range of temperature and humidity in the installation area of the high-voltage switch cabinet, the annual average concentration of corrosive gases around the chemical park, and the frequency of extreme weather such as high temperature and high humidity; load fluctuation risk sub-index , which is calculated based on the actual operation load data of the high-voltage switch cabinet in the past three months, and the difference between the daily load peak and the daily load average is taken as the basis for load fluctuation risk assessment; component degradation risk sub-index , which is determined based on the ratio of the cumulative operation time of components such as circuit breakers and insulating parts to the design life, as well as historical degradation rates reflected by historical contact resistance and dielectric loss data. Based on the distribution network equipment fault case library, the proportion of high-voltage switch cabinet failures caused by environmental factors, load fluctuations, and component degradation in the total number of failures in the past five years is calculated, and the weight coefficients of each risk sub-index are obtained after normalization 、 、 , the final value of the operation risk index is calculated based on the influence degree of each risk sub-index on equipment operation .

[0029] In step S1, when determining the distributed energy access index, first, it is determined that the types of distributed power sources connected to the high-voltage switch cabinet in the power supply area are photovoltaic power and small gas turbine power. Actual output data of the two types of distributed power sources are collected at a daily level, and the total output value of the two types of distributed power sources per day is calculated . At the same time, the load demand data of the high-voltage switch cabinet in the corresponding period is collected . The ratio of the daily distributed power to is calculated, and the arithmetic mean of the daily ratio data in a month is taken as the value of the distributed energy access index.

[0030] In step S1, when determining the electric vehicle charging load index, the number of charging stations in the power supply area, the power level of each site charging pile and the daily charging time are collected, the daily average charging time data of each site is counted according to the daily level cycle, the daily average charging load of a single charging pile is calculated combined with the power level of the charging pile, and the daily average total charging load of all charging piles in the region is summarized to obtain the daily average total charging load in the region , the rated power supply capacity data of the high-voltage switch cabinet corresponding to the period is collected , the load peak and valley distribution of the high-voltage switch cabinet in each period of the past year is analyzed, the coincidence degree of the charging load peak period and the overall load peak period of the high-voltage switch cabinet is calculated, and the coincidence degree value is taken as the value basis of the load influence correction coefficient , and the value of the electric vehicle charging load change rate index is calculated by formula .

[0031] In step S4, during the normalization process, the index types are first distinguished, the load rate, the availability rate and the distributed energy access index are classified as positive indexes, the evaluation value is calculated by formula , the maintenance cost, the operation risk and the equipment aging degree index are classified as negative indexes, and the evaluation value is calculated by formula , wherein, is the original actual data, is the optimal value determined according to the industry equipment operation standard and the historical optimal data of the same type of high-voltage switch cabinet.

[0032] In summary, the embodiment of the present application realizes the multi-dimensional accurate evaluation of the utilization efficiency of the 10kV high-voltage switch cabinet in the industrial park. Compared with the traditional evaluation method, not only the key dimensions such as equipment health status and economic operation cost are supplemented, but also the influence of distributed energy and electric vehicle charging load is fully considered, and the limitation of single weight determination method is solved by the gray correlation threshold variable weight method. The comprehensive evaluation value obtained finally can truly reflect the actual operation efficiency of the high-voltage switch cabinet in the complex power consumption scene of the industrial park, and provides reliable data support for the maintenance department to check equipment hidden dangers, optimize operation and maintenance plan, and develop equipment renovation scheme, effectively improves the operation stability and economic applicability of the distribution network in the industrial park, and meets the fine demand of the industrial park for the evaluation of the distribution network equipment under the new type of power industry.

Claims

1. A power distribution network equipment utilization efficiency comprehensive evaluation method, characterized in that, Comprise the following steps: S1, construct multi-dimensional evaluation index system, retain the load rate, available rate of conventional index for reflecting the basic running state of equipment, add aging degree index reflecting the health state of equipment, maintenance cost index reflecting the economic operation situation, operation risk index representing the safety operation level, and also include distributed energy access index and electric vehicle charging load index affected by new power industry; S2, determine the basic weight of each index by using analytic hierarchy process, first build a hierarchical structure with equipment utilization efficiency evaluation as the target layer, operation state, health state, economic operation, safety operation, and new power influence as the criterion layer, and each specific index as the index layer, invite professionals in the field of electric power to compare and score the importance of each index under the criterion layer, construct the judgment matrix according to the scoring results, and conduct consistency test on the judgment matrix, calculate the basic weight of each index after the test is passed; S3, adjust the basic weight by using grey correlation threshold variable weight method, calculate the grey correlation degree between each index, identify the strong correlation index combination according to the correlation degree, set the correlation threshold according to the actual evaluation demand, dynamically correct the basic weight of the strong correlation index according to the threshold, and obtain the optimized weight of each index; S4, obtain the original monitoring and statistical data of each index, convert the data into dimensionless evaluation value by using corresponding normalization method for different types of indexes, multiply the evaluation value of each index by the corresponding optimized weight, and sum to obtain the comprehensive evaluation value of the utilization efficiency of distribution network equipment.

2. The power distribution network equipment utilization efficiency comprehensive evaluation method of claim 1, wherein, In step S1, when determining the equipment aging degree index, three types of core data are comprehensively collected, including collecting equipment cumulative running time data , historical fault frequency data during operation , and performance detection data of insulation components and conductive components , normalizing the three types of data respectively to obtain , , , assigning weights to the normalized , , according to the equipment type and operation characteristics , , , and calculating the aging degree index value through the formula .

3. The power distribution network equipment utilization efficiency comprehensive evaluation method according to claim 1 or 2, characterized in that, In step S1, when determining the maintenance cost index, three accounting types of daily inspection cost, component replacement cost and fault repair cost are divided; the daily inspection cost is monthly, covering inspection personnel salary, equipment wear and tear and consumable cost; the component replacement cost is calculated by combining component type, quantity and corresponding procurement, transportation and installation cost; the fault repair cost includes labor, consumables and auxiliary equipment usage cost, and the total value of the maintenance cost index is obtained by summarizing the three types of cost data.

4. The power distribution network equipment utilization efficiency comprehensive evaluation method according to claim 1 or 2, characterized in that, In step S1, when determining the operation risk index, risk sub-indices are constructed from three dimensions, including: an environment risk sub-index of the equipment , a load fluctuation risk sub-index , and a component degradation risk sub-index . According to the influence degree of each risk sub-index on the operation of the equipment, values are assigned, and the final value of the operation risk index is calculated by a formula , wherein , , are weight coefficients of the risk sub-indices.

5. The power distribution network equipment utilization efficiency comprehensive evaluation method according to claim 1 or 2, characterized in that, In step S1, when determining the distributed energy access index, the type of the distributed power accessed by the power distribution network is first determined, mainly including photovoltaic power, wind power, and small gas turbine power. Actual output data of various types of distributed power is collected in daily cycles, and the total output value of various types of distributed power per day is calculated and recorded as At the same time, the load demand data of the corresponding period of the power distribution network is collected and recorded as The ratio of the total output of the distributed power per day to the load demand of the power distribution network in the period is calculated, and the arithmetic average value of the ratio data per day in a month is taken as the value of the distributed energy access index.

6. The power distribution network equipment utilization efficiency comprehensive evaluation method according to claim 1 or 2, characterized in that, In step S1, when determining the electric vehicle charging load index, the number of charging stations, the charging pile power level and the daily average charging time in the power distribution network coverage area are collected, and the daily average total charging load in the area is calculated The rated power supply capacity data of the power distribution network corresponding to the period is collected The value of the electric vehicle charging load change rate index is calculated by the formula , wherein is a load influence correction coefficient.

7. The power distribution network equipment utilization efficiency comprehensive evaluation method according to claim 1 or 2, characterized in that, In step S2, when inviting experts to compare and score the indexes, the expert screening conditions are as follows: Engaged in electric power system operation and maintenance, equipment evaluation and related fields for more than 5 years, with practical experience in distribution network equipment operation management and evaluation; The number of experts is set to 5-8, covering different professional directions of equipment operation and maintenance, dispatching and evaluation; After collecting the scoring results of all experts, the average value of the two-by-two comparison and scoring of each index is calculated; If the deviation of a certain expert's score for a certain group of indexes from the average value is more than 15%, the expert is invited to review the scoring basis and adjust the scoring results again after receiving the feedback on the deviation and the average value; After the review of all expert scoring is completed, the final scoring results are integrated to construct the judgment matrix.

8. The power distribution network equipment utilization efficiency comprehensive evaluation method according to claim 1 or 2, characterized in that, In step S4, in the normalization process, first distinguish the index type, load rate, availability rate, distributed energy access amount index, the higher the value, the more it reflects the equipment utilization efficiency advantage, and is divided into positive index. The evaluation value is calculated by formula Maintenance cost, operation risk, and equipment aging degree index. The higher the value, the less conducive to efficient operation of the equipment, and is divided into a negative index. The evaluation value is calculated by formula wherein, is the positive index evaluation value, is the negative index evaluation value, is the original actual data, is the optimal value.

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