Reliability prediction model based on small sample failure rate of smart meter

Authors

Zhiru CHEN, State Grid Shandong Electric Power Company Marketing Service Center (Metering Center), Jinan 250000, China;Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250000, China
Yanjie DAI, State Grid Shandong Electric Power Company Marketing Service Center (Metering Center), Jinan 250000, China;Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250000, ChinaFollow
Yan DU, State Grid Shandong Electric Power Company Marketing Service Center (Metering Center), Jinan 250000, China;Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250000, China
Xianguang DONG, State Grid Shandong Electric Power Company Marketing Service Center (Metering Center), Jinan 250000, China;Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250000, China
Zhi ZHANG, State Grid Shandong Electric Power Company Marketing Service Center (Metering Center), Jinan 250000, China;Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250000, China
Zhen JING, State Grid Shandong Electric Power Company Marketing Service Center (Metering Center), Jinan 250000, China;Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250000, China

Keywords

smart meter, failure rate, small sample, gaussian process, reliability prediction

Abstract

Reliability evaluation based on the failure rate data is an important basis for the health status management and maintenance of smart meters. However, the small sample characteristics of outliers and failure rates limit the evaluation performance of traditional smart energy meter reliability prediction models. Therefore, a prediction model of smart meter failure rate under multi?environment stress based on weighted local outlier factor and Gaussian process regression is proposed in this paper. Firstly, a weighted local outlier factor is employed with the model to identify and then delete potential outliers in failure rate data sets; then, different kernel functions are selected to match the characteristics of multiple stress inputs in typical environments, and choose the best one. Finally, the interval change of the 95% confidence level of the failure rate is predicted by the posterior distribution of the Gaussian process, and the interval reliability is obtained based on this. Case analysis of fault samples of smart meters in two typical environmental areas shows that the proposed model could effectively predict the trend of failure rate of smart meters under multi?environmental stress, and could accurately solve its reliability.

DOI

10.19781/j.issn.1673-9140.2023.01.025

First Page

218

Last Page

225

Recommended Citation

CHEN, Zhiru; DAI, Yanjie; DU, Yan; DONG, Xianguang; ZHANG, Zhi; and JING, Zhen (2023) "Reliability prediction model based on small sample failure rate of smart meter," Journal of Electric Power Science and Technology: Vol. 38: Iss. 1, Article 25.
DOI: 10.19781/j.issn.1673-9140.2023.01.025
Available at: https://jepst.researchcommons.org/journal/vol38/iss1/25