Smart Electricity Meter Prognostics Based on Lithium Battery RUL Prediction

  • Ye Chen 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China, 2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Ziyi Chen Shenyang Agricultural University, Shenyang 110866, China
  • Mengmeng Zhu 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China ,2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Yaohua Liao 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China ,2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Fang Luo 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China, 2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Xinru Li Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
DOI: https://doi.org/10.13052/10.13052/dgaej2156-3306.3733
Keywords: Smart electricity meters, lithium battery, remaining useful life, particle filtering

Abstract

Smart Electricity Meters (SEMs) are widely used in distributed generation system, and over 67% of its failure are caused by battery low-voltage. Therefore, it is necessary to study the degradation of battery voltage. This work explores the degradation mechanism of lithium battery and proposed to use voltage as degradation index to estimate the health status of the system. Four groups of batteries of the same type and batch are used for the test. The purpose is to use multiple sets of data to train the model parameters and enhance the robustness of the model. The Particle Filtering (PF) based approach is used in this study to estimate the degradation state such that the Remaining Useful Life (RUL) can be predicted. An accurate prediction can provide the proper maintenance/replacement schedule for the SEMs before the failure occurs.

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Author Biographies

Ye Chen, 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China, 2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China

Ye Chen works at Electric Power Research Institute of Yunnan Power Grid Co. Ltd, She received master’s degree in Power systems and automation from Kunming University of Science and Technology in 2017, engaged in electric energy, electrical measurement and thermal engineering professional work. Members of the High-precision Electrical Parameter Laboratory, Spark Power Research Studio, Intelligent Perception Innovation Studio and Key Laboratory of CSG for Electric Power Measurement.

Ziyi Chen, Shenyang Agricultural University, Shenyang 110866, China

Ziyi Chen, born in Liaoning Province, China, in 2003. Undergraduate at Shenyang Agricultural University, mechanical design and manufacturing and automation, like electronic design and circuit related knowledge.

Mengmeng Zhu, 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China ,2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China

Mengmeng Zhu works at Electric Power Research Institute of Yunnan Power Grid Co. Ltd, senior engineer, the research direction is electric energy metering device technology research and power transformer field verification, AC/DC electronic transformer field key test technology application and distribution network fault detection and protection control work.

Yaohua Liao, 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China ,2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China

Yaohua Liao (1992), male, master, engineer, Yunnan Power Grid Co., Ltd. Electric Power Research Institute, engaged in electric energy, electrical measurement, thermal engineering and high voltage measurement professional work, good at solving measurement-related field problems. Members of the High-precision Electrical Parameter Laboratory, Spark Power Research, and Intelligent Perception Innovation Studio participated in the drafting of the Q/CSG 1209013.2-2019 and Q/CSG 1209013.7-2019 corporate standards.

Fang Luo, 1Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China, 2Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China

Fang Luo (1995), female, works in Yunnan Yundian Tongfang Technology Co., LTD. She received her bachelor’s degree in Computer Science and Technology from Yunnan Minzu University in 2017. She is engaged in Web and APP front-end development, artificial intelligence consulting design and project management.

Xinru Li, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Xinru Li was born in Shandong, China, in 1997. She is currently pursuing the Master of Academic degree with the Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China. She is presently working in the fields of Fault prediction and health management.

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Published
2021-12-08
Issue
2022: Vol 37 Iss 3
Section
Renewable Power and Energy Systems