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| A comprehensive evaluation and trend prediction method of health degree for electric energy measuring devices |
| DOI：10.7667/PSPC180967 |
| Key Words:electric energy measuring devices status maintenance sequential relation analysis grey model |
| Author Name | Affiliation | | LIU Chunyu | Electric Power Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China | | LIU Zifa | School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China | | LUO Qun | Electric Power Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China | | GE Leijiao | School of Electrical Information and Engineering, Tianjin University, Tianjin 300072, China | | DENG Wendong | Yantai Orient Wisdom Electric Co.Ltd, Yantai 264003, China | | WANG Yueming | Electric Power Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China |
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| Abstract:In order to solve the problems of electric energy measuring devices effectively, including heavy maintenance task, duplicate detecting, and being targeted weakly, a comprehensive evaluation and trend prediction method of health degree of electric energy measuring devices is proposed, which provides an auxiliary decision for the state online maintenance optimization of electric energy measuring devices. Firstly, a comprehensive evaluation indicator system is proposed from running state, configuration mode and operation condition of electric energy measuring devices. The order relation analysis method is used to calculate the weighting of comprehensive evaluation indicator system, so as to realize the comprehensive evaluation of the health degree of electric energy measuring devices. Secondly, based on the comprehensive evaluation conclusion of electric energy measuring devices, a trend prediction method based on the grey GM(1,1) model is proposed to predict the health degree of electric energy measuring devices. Finally, it is verified by actual operation in Tianjin. The results of health degree evaluation and trend prediction can effectively guide to the field status maintenance of electric energy measuring devices and discover the hidden dangers, which can provide a theoretical basis for the strategy optimization of state maintenance of electric energy measuring devices. This work is supported by National Key Research and Development Program of China (No. 2016YFB0900105). |
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