CN115146981A - Active power distribution network operation health level comprehensive evaluation method based on big data - Google Patents

Active power distribution network operation health level comprehensive evaluation method based on big data Download PDF

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CN115146981A
CN115146981A CN202210828198.7A CN202210828198A CN115146981A CN 115146981 A CN115146981 A CN 115146981A CN 202210828198 A CN202210828198 A CN 202210828198A CN 115146981 A CN115146981 A CN 115146981A
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朱凯
李雅晴
沈晓峰
吴继健
方祺
顾华
孙进
徐伟
许婧琦
张佳栋
郑真
沈伟
朱能
梁晟
冯若愚
黄冠
杨冰芳
董玥
王梓萌
刘方蕾
陈敬德
徐琳
胡振华
戴勃文
王振华
翟莺鸽
梅凡
沈佳
杨祎涛
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Abstract

The invention discloses a big data-based comprehensive evaluation method for the running health level of an active power distribution network, which comprises the following steps: step 1, processing bad data, and converting the bad data into credible multi-source data; step 2, constructing a comprehensive evaluation index system for evaluating the daily scheduling operation condition of the power distribution network; step 3, simplifying the third-level indexes in the second-level indexes; step 4, determining the fuzzy membership degree of each three-level index; step 5, determining the weight of each index; and 6, carrying out fuzzy synthesis according to the obtained fuzzy membership and the index weight, and finally obtaining the operation evaluation result of the power distribution network. The method can comprehensively reflect the healthy operation level of the power distribution network, and provides reference and basis for optimizing the risk of the power distribution network.

Description

Active power distribution network operation health level comprehensive evaluation method based on big data
Technical Field
The invention relates to a big data-based comprehensive evaluation method for the running health level of an active power distribution network, which is used in the field of operation and maintenance of the power distribution network.
Background
The traditional distribution network operation mode mainly aims at the passive response of faults or abnormity, and the early warning pre-control, the real-time monitoring and the rolling investigation of network risks or hidden dangers such as weak points of a network frame, resource bearing capacity and the like are lacked. When the active power distribution network is abnormal in fault or needs to be adjusted in mode, in order to enable the potential risk investigation business mode of the active power distribution network to have instantaneity and comprehensiveness, a technical auxiliary means with the capability of intuitively and effectively perceiving the power grid risk is urgently needed to be researched. Therefore, the user must stand at a height which reduces the operation risk of the power grid as much as possible, establish a scientific and reasonable power grid operation state evaluation and risk assessment model, realize risk pre-control, enhance the pertinence and effectiveness of equipment maintenance, and ensure the safe and stable operation of the power system. The traditional power distribution network evaluation system has the disadvantages of complex evaluation index setting, single evaluation characteristic and low reliability of evaluation results.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a comprehensive evaluation method for the operation health level of an active power distribution network based on big data, which considers the risk level of real-time operation of the power distribution network and the potential risk caused by the grid structure defect of the power distribution network, comprehensively reflects the health operation level of the power distribution network, and provides reference and basis for the optimization of the risk of the power distribution network.
One technical scheme for achieving the above purpose is as follows: a method for comprehensively evaluating the running health level of an active power distribution network based on big data comprises the following steps:
step 1, processing bad data, and converting the bad data into credible multi-source data;
step 2, constructing a comprehensive evaluation index system for evaluating the daily scheduling operation condition of the power distribution network, wherein the safety of the primary index comprises the voltage quality, the frequency quality, the network frame risk, the operation risk, the physical component risk and the new energy access risk of the secondary index; the first-level index reliability comprises a second-level index network structure, equipment level and reliability indexes; the first-level index economy comprises a second-level index investment index and a benefit index; the first-level index flexibility comprises a second-level index power supply capacity index and a utilization efficiency index; the first-level index coordination comprises a coordination index of the development of a second-level index power grid and a load and development coordination index of each voltage level of the power grid; the environment influence of the primary index comprises a secondary index energy-saving emission-reducing index;
step 3, simplifying the third-level indexes in the second-level indexes, wherein the evaluation indexes after the simplification are shown in the following table
Comprehensive evaluation index system table for active power distribution network
Figure BDA0003744831610000021
Step 4, determining fuzzy membership of each three-level index, as shown in the following table:
Figure BDA0003744831610000031
step 5, determining the weight of each index, and selecting an analytic hierarchy process to obtain the weight of each index of the criterion layer; selecting an analytic hierarchy process to calculate subjective weights of all indexes of an index layer according to the weights of all indexes of the index layer, then selecting an entropy weight process to obtain objective weights corresponding to all indexes by combining the volatility of the indexes, and finally integrating the subjective and objective weights to obtain subjective and objective integrated weights of all indexes;
and 6, performing fuzzy synthesis according to the obtained fuzzy membership and the index weight to finally obtain the operation evaluation result of the power distribution network, wherein the reliability is more than 0.9 min, the basic reliability is more than 0.8 min, and the unreliability is less than 0.8 min.
Further, step 1, the data types of bad data processing are null record, abnormal record and repeated record;
step 1.1, processing the blank record, and performing interpolation processing on the blank record by adopting a Lagrange interpolation method: recording x in accordance with null of dataset i Establishing a second-order equidistant Lagrange interpolation equation P between the first two records and the next record 2 (x i ) The null record is interpolated as follows:
Figure BDA0003744831610000032
step 1.2, processing the abnormal record, firstly processing the abnormal record by adopting a Laite test method, exporting the record value, and solving the record value (X) 1 ,X 2 ,X 3 ,...,X n ) Mean value of
Figure BDA0003744831610000041
And residual V i And calculating the standard deviation sigma according to a Bessel formula as follows:
Figure BDA0003744831610000042
Figure BDA0003744831610000043
Figure BDA0003744831610000044
then judging the recorded value, if it satisfies | V i If the value is greater than 3 sigma, the error is gross error, the data is considered to be abnormal, and deletion of the data is not adopted;
and step 1.3, processing the repeated records, and checking and merging repeated data.
According to the active power distribution network operation health level comprehensive evaluation method based on the big data, an active power distribution network operation health level evaluation index system is established according to actual information such as voltage levels, influence radiuses and region composition of various power grid work, accidents and risks. The invention provides a comprehensive evaluation method for the running health level of an active power distribution network, which can comprehensively reflect the running health state of the power distribution network.
In the calculation process of each index of the health level of the active power distribution network, an analytic hierarchy process and an entropy weight method are used to obtain the comprehensive weight of the index. The subjective method and the objective method are combined, so that the error of a single method can be well made up, and the accuracy of the final analysis result is greatly improved.
Dividing each index into a positive index, an inverse index and a moderate index according to the characteristics of each index, and determining the fuzzy membership of each index by respectively adopting different trapezoid distribution linear membership functions; and obtaining a comprehensive evaluation result by adopting a common multiplier and adder. The problems that the evaluation indexes of a traditional power distribution network evaluation system are set complicatedly, the evaluation characteristics are single, the reliability of the evaluation result is not high and the like are solved.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is made by specific examples:
for example, the evaluation of operation data of a distribution network 2021 year old in Qingpu, shanghai is given. The method comprises the following steps:
step 1, processing bad data and converting the bad data into credible multi-source data.
Step 1.1, process the null record. The empty recording is usually caused by the abnormal work of the sensing device, so that the data is not successfully recorded. The related processing method of the missing data can be divided into two types of null removal and completion. For unimportant null records, they can be searched and then removed from the data set summary. For important null records, they need to be complemented to prevent them from adversely affecting the analysis results. And (3) carrying out interpolation processing on the blank record by adopting a Lagrange interpolation method: recording x in accordance with the null of the dataset i Establishing a second-order equidistant Lagrange interpolation equation P between the first two records and the next record 2 (x i ) The null record is interpolated as follows:
Figure BDA0003744831610000051
and step 1.2, performing related processing on the abnormal record by adopting a Latt test method. First, a record value is derived to obtain a record value (X) 1 ,X 2 ,X 3 ,...,X n ) Mean value of
Figure BDA0003744831610000052
And residual V i And calculating the standard deviation sigma according to a Bessel formula as follows:
Figure BDA0003744831610000053
Figure BDA0003744831610000054
Figure BDA0003744831610000055
then the recorded value is processedJudging if | V is satisfied i If | is greater than 3 σ, the error is coarse, and the data is considered to be abnormal, and the deletion is not adopted.
And step 1.3, processing repeated records, wherein the repeated records are mostly caused by backup in storage or repeated collection of some data, and although the repeated records do not cause corresponding influence on the accuracy of an analysis result, if the repeated records are more, the repeated records occupy more memory space, so that the calculation performance is greatly reduced, and the repeated data needs to be checked and merged.
Carrying out big data preprocessing on basic operation data of a power distribution network in a certain region of Qingpu in 2021 years, and finding that 154354 pieces of data in 1646885 records are bad data, 2454 pieces of data in 154354 pieces of bad data are null record data, 115291 pieces of data are abnormal record data, and 14549 pieces of data are repeated record data. The data quality before processing is 90.63%, and after data preprocessing, the data quality reaches 96.15%, the improvement effect is obvious, and the method is greatly helpful for the accuracy of index evaluation and analysis.
And 2, constructing a comprehensive evaluation index system for evaluating the daily scheduling operation condition of the power distribution network. By combining the requirements of a power grid company on safety, reliability, economy, flexibility, harmony and environmental influence, the factors which can reflect the operation state of the power distribution network most are selected from the actual data which can be obtained by the daily operation of the power grid, including the data collected by a production management system, a dispatching management system, a power utilization information collection system and the like, and appropriate secondary and tertiary indexes are designed, so that a comprehensive evaluation index system which is moderate in scale and can comprehensively reflect the daily dispatching operation condition of the power distribution network is formed finally
According to the method, the first-level index safety comprises the second-level index voltage quality, the frequency quality, the net rack risk, the operation risk, the physical component risk and the new energy access risk; the first-level index reliability comprises a second-level index network structure, equipment level and reliability indexes; the first-level index economy comprises a second-level index investment index and a benefit index; the first-level index flexibility comprises a second-level index power supply capacity index and a utilization efficiency index; the first-level index coordination comprises a coordination index of the development of a second-level index power grid and a load and development coordination index of each voltage level of the power grid; the environmental influence of the primary index comprises a secondary index energy-saving emission-reducing index.
Step 3, simplifying the third-level indexes in the second-level indexes, wherein the evaluation indexes after the simplification are shown in the following table
Comprehensive evaluation index system table for active power distribution network
Figure BDA0003744831610000071
Step 4, dividing all indexes into positive indexes, inverse indexes and moderate indexes, as shown in the following table
Index classification after index reduction
Figure BDA0003744831610000072
Figure BDA0003744831610000081
The data for 12 months in 2021 in this region are shown in the following table.
Data table of 12 month indexes in 2021
Figure BDA0003744831610000082
Figure BDA0003744831610000091
Step 5, determining the weight of each index, and selecting an analytic hierarchy process to obtain the weight of each index of the criterion layer; and for the weight of each index of the index layer, selecting an analytic hierarchy process to calculate the subjective weight of each index, then combining the volatility of the index, selecting an entropy weight process to obtain the objective weight corresponding to each index, and finally integrating the subjective and objective weights to obtain the subjective and objective integrated weight of each index.
The subjective weights of the indices are shown in the following table:
subjective weighting table
Figure BDA0003744831610000092
Figure BDA0003744831610000101
The objective weights of the indexes of the index layer obtained by the entropy weight method are shown in the following table:
objective weight
Figure BDA0003744831610000102
The subjective and objective weights of each index of the index layer are synthesized to obtain the subjective and objective comprehensive weights corresponding to each index, which is shown in the following table:
composite weight
Figure BDA0003744831610000103
Figure BDA0003744831610000111
And 6, carrying out fuzzy synthesis according to the obtained fuzzy membership and the index weight. The following table is obtained. Finally, the operation evaluation result of the power distribution network is obtained, and the operation evaluation result of the power distribution network in 2021 and 12 months in Qingpu in a certain area can be obtained. The evaluation results are shown in the following table. More than 0.9 minutes is reliable, more than 0.8 minutes is basically reliable, and less than 0.8 minutes is unreliable.
Evaluation results
Figure BDA0003744831610000112
Figure BDA0003744831610000121
As can be seen from the table, the operation evaluation result of the power distribution network in Qingpu in 2021 and 12 months is 0.814, and the scores of all the first-level indexes are about 0.8. This can result in: in 12 months in 2021, the distribution network in the area is in a good operation state on the whole, and the health level of the distribution network is higher.
The highest score among the six first-level indexes is the safety index, and the score reaches 0.903. The second is an economic index and a reliability index, and the scores are 0.836 point and 0.832 point respectively. The scores of the other three primary indexes are all greater than 0.7. Therefore, the distribution network in the area has higher safety and reliability level. The evaluation result of the new energy access risk index is 0.756 score, and the score is lower compared with the rest secondary indexes in the safety index, so that the area needs to pay attention to the new energy access condition, and the new energy access risk is reduced. The evaluation result of the structural index is 0.732 points, the score is low, and the structural index needs to be focused in order to further improve the reliability of the distribution network in the area. The scores of other secondary indexes are all about 0.8, and a larger promotion space still exists, so that corresponding measures need to be taken for some details (such as new energy power receiving and generating quantity, distribution transformation capacity expansion, line interconnection rate and the like) in the next step, so that the risk potential of the power distribution network in the region is reduced, and the health level of the power distribution network is promoted.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.

Claims (2)

1. A method for comprehensively evaluating the running health level of an active power distribution network based on big data is characterized by comprising the following steps:
step 1, processing bad data and converting the bad data into credible multi-source data;
step 2, constructing a comprehensive evaluation index system for evaluating the daily scheduling operation condition of the power distribution network, wherein the safety of the primary index comprises the voltage quality, the frequency quality, the network frame risk, the operation risk, the physical component risk and the new energy access risk of the secondary index; the first-level index reliability comprises a second-level index network structure, equipment level and reliability indexes; the first-level index economy comprises a second-level index investment index and a benefit index; the first-level index flexibility comprises a second-level index power supply capacity index and a utilization efficiency index; the first-level index coordination comprises a coordination index of the development of a second-level index power grid and a load and development coordination index of each voltage level of the power grid; the environment influence of the first-level index comprises a second-level index energy-saving emission-reduction index;
step 3, simplifying the third-level indexes in the second-level indexes, wherein the evaluation indexes after the simplification are shown in the following table
Comprehensive evaluation index system table for active power distribution network
Figure FDA0003744831600000011
Figure FDA0003744831600000021
Step 5, determining the weight of each index, and selecting an analytic hierarchy process to obtain the weight of each index of the criterion layer; selecting an analytic hierarchy process to calculate subjective weights of all indexes of an index layer according to the weights of all indexes of the index layer, then selecting an entropy weight process to obtain objective weights corresponding to all indexes by combining the volatility of the indexes, and finally integrating the subjective and objective weights to obtain subjective and objective integrated weights of all indexes;
and 6, carrying out fuzzy synthesis according to the obtained fuzzy membership and the index weight to finally obtain the operation evaluation result of the power distribution network, wherein the reliability is more than 0.9, the basic reliability is more than 0.8, and the unreliability is less than 0.8.
2. The active power distribution network operation health level comprehensive evaluation method based on big data as claimed in claim 1, wherein in step 1, the data types of bad data processing are null record, abnormal record and repeated record;
step 1.1, processing the blank record, and performing interpolation processing on the blank record by adopting a Lagrange interpolation method: recording x in accordance with the null of the dataset i Establishing a second-order equidistant Lagrange interpolation equation P between the first two records and the next record 2 (x i ) The null record is interpolated as follows:
Figure FDA0003744831600000022
step 1.2, processing the abnormal record, firstly processing the abnormal record by a Latt test method, exporting the record value, and obtaining the record value (X) 1 ,X 2 ,X 3 ,...,X n ) Mean value of
Figure FDA0003744831600000023
And residual V i And calculating the standard deviation sigma according to a Bessel formula as follows:
Figure FDA0003744831600000031
Figure FDA0003744831600000032
Figure FDA0003744831600000033
then judging the recorded value, if satisfying | V i If | > 3 σ, then the errorIf the difference is gross error, the data is considered to have abnormality, and deletion of the data is not adopted;
and step 1.3, processing the repeated records, and checking and combining the repeated data.
CN202210828198.7A 2022-07-13 2022-07-13 Active power distribution network operation health level comprehensive evaluation method based on big data Pending CN115146981A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115600933A (en) * 2022-12-13 2023-01-13 浙江万胜智能科技股份有限公司（Cn） Electric meter power quality detection method and system based on Internet of things
CN116451875A (en) * 2023-06-14 2023-07-18 国网吉林省电力有限公司经济技术研究院 Optical storage and filling integrated station capacity optimization configuration method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115600933A (en) * 2022-12-13 2023-01-13 浙江万胜智能科技股份有限公司（Cn） Electric meter power quality detection method and system based on Internet of things
CN116451875A (en) * 2023-06-14 2023-07-18 国网吉林省电力有限公司经济技术研究院 Optical storage and filling integrated station capacity optimization configuration method

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