Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm
With the expansion of intelligent distribution network and the increasing complexity of power grid structure, the amount of data in power system increases rapidly, and new challenges rise from the checking and monitoring evaluation of power equipment. Based on the principle of big data mining analys...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2020-11-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201907096 |
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| author | Yingying CHENG Jie DU Quan ZHOU Jiaming ZHANG Xiaoyong ZHANG Gang LI |
| author_facet | Yingying CHENG Jie DU Quan ZHOU Jiaming ZHANG Xiaoyong ZHANG Gang LI |
| author_sort | Yingying CHENG |
| collection | DOAJ |
| description | With the expansion of intelligent distribution network and the increasing complexity of power grid structure, the amount of data in power system increases rapidly, and new challenges rise from the checking and monitoring evaluation of power equipment. Based on the principle of big data mining analysis, this paper proposes a method based on random matrix theory and clustering algorithm to evaluate the running state of electric energy meter. Firstly, time series data of various indicators are characterized and then integrated by real-time separation window technology. Based on the random matrix theory, the random matrix-based analysis model is constructed to calculate and analyze the characteristics with multi-dimensional statistical timing in real time. Further, an improved DTW (dynamic time warping) clustering algorithm is used to analysis the linear feature statistics of the output of the random matrix. Finally, according to the clustering result, the state of the electric energy meter is obtained and outputted as different classes. The experiments show that compared with the traditional Principal Component Analysis evaluation method, the proposed method has good robustness, reliability and timeliness, which provides a new idea for the application research of power grid detection technology. |
| format | Article |
| id | doaj-art-9c7d89974f6240b2bd39c4d36732ecb9 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2020-11-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-9c7d89974f6240b2bd39c4d36732ecb92025-08-20T02:59:19ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492020-11-01531111612510.11930/j.issn.1004-9649.201907096zgdl-53-1-chengyingyingEvaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering AlgorithmYingying CHENG0Jie DU1Quan ZHOU2Jiaming ZHANG3Xiaoyong ZHANG4Gang LI5State Grid Chongqing Electric Power Research Institute, Chongqing 401121, ChinaState Grid Chongqing Electric Power Research Institute, Chongqing 401121, ChinaState Grid Chongqing Electric Power Research Institute, Chongqing 401121, ChinaState Grid Chongqing Electric Power Research Institute, Chongqing 401121, ChinaState Grid Chongqing Electric Power Research Institute, Chongqing 401121, ChinaState Grid Chongqing Electric Power Research Institute, Chongqing 401121, ChinaWith the expansion of intelligent distribution network and the increasing complexity of power grid structure, the amount of data in power system increases rapidly, and new challenges rise from the checking and monitoring evaluation of power equipment. Based on the principle of big data mining analysis, this paper proposes a method based on random matrix theory and clustering algorithm to evaluate the running state of electric energy meter. Firstly, time series data of various indicators are characterized and then integrated by real-time separation window technology. Based on the random matrix theory, the random matrix-based analysis model is constructed to calculate and analyze the characteristics with multi-dimensional statistical timing in real time. Further, an improved DTW (dynamic time warping) clustering algorithm is used to analysis the linear feature statistics of the output of the random matrix. Finally, according to the clustering result, the state of the electric energy meter is obtained and outputted as different classes. The experiments show that compared with the traditional Principal Component Analysis evaluation method, the proposed method has good robustness, reliability and timeliness, which provides a new idea for the application research of power grid detection technology.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201907096random matrix theoryclustering algorithmmoving-split windowtime sequence datapower system big datarunning state of electric energy meter |
| spellingShingle | Yingying CHENG Jie DU Quan ZHOU Jiaming ZHANG Xiaoyong ZHANG Gang LI Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm Zhongguo dianli random matrix theory clustering algorithm moving-split window time sequence data power system big data running state of electric energy meter |
| title | Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm |
| title_full | Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm |
| title_fullStr | Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm |
| title_full_unstemmed | Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm |
| title_short | Evaluation Method for Running State of Electricity Meters Based on Random Matrix Theory and Clustering Algorithm |
| title_sort | evaluation method for running state of electricity meters based on random matrix theory and clustering algorithm |
| topic | random matrix theory clustering algorithm moving-split window time sequence data power system big data running state of electric energy meter |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.201907096 |
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