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

doi:10.19781/j.issn.1673-9140.2023.01.025

Home > Archive>Volume 38, Issue 1, 2023 >218-225. DOI:10.19781/j.issn.1673-9140.2023.01.025

Reliability prediction model based on small sample failure rate of smart meter
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                        10.19781/j.issn.1673-9140.2023.01.025
                    
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                        CHEN Zhiru1,2CHEN Zhiru
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
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DAI Yanjie1,2DAI Yanjie
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
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DU Yan1,2DU Yan
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
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DONG Xianguang1,2DONG Xianguang
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
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ZHANG Zhi1,2ZHANG Zhi
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
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JING Zhen1,2JING Zhen
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
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Affiliation:(1.State Grid Shandong Electric Power Company Marketing Service Center (Metering Center), Jinan 250000, China;2.Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250000, China)
Clc Number:TM933.4
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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.

Key words:smart meter; failure rate; small sample; gaussian process; reliability prediction

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陈祉如,代燕杰,杜艳,董贤光,张志,荆臻.基于小样本故障率的智能电能表可靠度预估模型[J].电力科学与技术学报英文版,2023,38(1):218-225. CHEN Zhiru, DAI Yanjie, DU Yan, DONG Xianguang, ZHANG Zhi, JING Zhen. Reliability prediction model based on small sample failure rate of smart meter[J]. Journal of Electric Power Science and Technology,2023,38(1):218-225.

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Online: April 10,2023
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