摘要
基于故障率数据的可靠度评估是智能电能表健康状态管理与维修的重要依据。然而,异常值及故障率的小样本特性限制了传统智能电能表可靠度预测模型的评估性能。为此,提出一种基于加权局部离群因子与高斯过程回归的多环境应力下智能电能表故障率预估模型。首先,建模采用一种加权局部离群因子识别并剔除故障率数据集中的异常值;然后,选用不同的核函数匹配典型环境下的多应力输入特征,选取最优核;最后,以高斯过程后验分布预测故障率的置信度95%的区间变化,并据此估计智能电能表可靠度。采用2个典型环境地区的智能电能表故障样本进行实例分析,结果表明所提模型可有效预测智能电能表在多环境应力下故障率变化趋势,并能准确求解其可靠度。
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.
作者
陈祉如
代燕杰
杜艳
董贤光
张志
荆臻
CHEN Zhiru;DAI Yanjie;DU Yan;DONG Xianguang;ZHANG Zhi;JING 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)
出处
《电力科学与技术学报》
CAS
CSCD
北大核心
2023年第1期218-225,共8页
Journal of Electric Power Science And Technology
基金
国网山东省电力公司科技项目(520626200021)。
关键词
智能电能表
故障率
小样本
高斯过程
可靠度预估
smart meter
failure rate
small sample
gaussian process
reliability prediction