A Study on Reliability of Smart Meters based on Monte-Carlo Method and Fault Trees

  • Ye Chen Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China and Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Ziyi Chen Shenyang Agricultural University, Shenyang 110866, China
  • Yaohua Liao Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China and Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Mengmeng Zhu Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China and Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Zhihu Hong Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China and Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China
  • Zhangnan Jiang Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Keywords: Smart Meters, Reliability Prediction, Monte-Carlo Sampling, Fault Tree Analysis.

Abstract

Smart meters are widely used in the power supply system, and their operational reliability is closely related to the user’s power supply reliability. It is difficult for intelligent power metering equipment to accurately predict its operational reliability and lifespan based on the existing technical specifications. In order to improve the accuracy of predicting the reliability and the maintenance cycle of the smart meter, this paper proposes a method for predicting the reliability of the smart meter based on the Monte Carlo method and fault tree. Firstly, the occurrence time of the bottom sampling event is simulated by the Monte-Carlo method based on the statistical data of the annual failure rate of each module of the smart meter. Then, according to the Fault Tree analysis of smart meters, the occurrence of the event is transformed into the fault time of the whole smart meters. The interval statistics are used to obtain the reliability value of the smart meter. In the end, the curve of the reliability function is obtained after fitting the reliability value. The results show that the reliability of the smart meter obeys the exponential distribution during the operation of 100 years. When it comes to the tenth year, the reliability is 0.9519. This algorithm provides a guide for accurately predicting its reliability and maintenance cycles by modularly analyzing the faults of smart meters.

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Author Biographies

Ye Chen, Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China and Key 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, mainly research electronic design and circuit related fields.

Yaohua Liao, Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China and Key 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.

Mengmeng Zhu, Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China and Key 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.

Zhihu Hong, Electric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 650217, China and Key Laboratory of CSG for Electric Power Measurement, Kunming 650217, China

Zhihu Hong, born in Yunnan Province, China, in 1993. He received a master’s degree in electrical engineering from Southwest Jiaotong University in 2018, and currently works as a high voltage researcher of Yunnan Electric Power Research Institute of China Southern Power Grid. His research interests include insulation and condition assessment of high voltage electrical equipment, multi physical field finite element simulation of high voltage electrical equipment.

Zhangnan Jiang, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Zhangnan Jiang was born in Yunnan, China, in 1993. He received the Bachelor of Engineering degree in electrical engineering and automation from Kunming University of Science and Technology, China, in 2015. He is currently pursuing the Master of Engineering degree in instrumentation engineering from Kunming University of Science and Technology. His fields of research interests are mainly focused on fiber bragg grating instrumentation and hot-spot temperature of transformer winding.

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Published
2021-10-21
Section
Renewable Power and Energy Systems