电测与仪表2026，Vol.63Issue(4)：173-181,9.DOI:10.19753/j.issn1001-1390.2026.04.018
基于少量无标签样本的电能表自动检定装置在线异常识别方法研究
Study on the online anomaly detection method for automatic verification device of electricity meters based on a small number of unlabeled samples
摘要
Abstract
The automatic verification device for energy meters conducts quality checks through periodic verifications and interim verifications,leading to issues such as low operational efficiency,long verification cycles,and the ina-bility to promptly identify and rectify anomalies.Therefore,implementing online anomaly detection for the automat-ic verification device for electricity meters holds significant importance.In this study,a method for online anomaly detection based on a small number of unlabeled samples is proposed.It involves the online collection of positional error data from the automatic verification device for electricity meters,construction of error features,extraction of crucial data components using principal component analysis.Subsequently,it employs the isolation forest algorithm to mark anomalous meter positions.Following this,a support vector machine(SVM)model is trained based on the marked results,and the hyperparameters of the SVM are optimized using k-fold cross-validation and Bayesian opti-mization.With the use of only a small number of support vectors,the method achieves anomaly detection for meter positions with an accuracy rate of 99.44%,demonstrating effective anomaly detection.关键词
电能表自动检定装置/在线异常识别/孤立森林/支持向量机/无标签样本Key words
automatic verification device for electricity meters/online anomaly detection/isolation forest/support vector machine/unlabeled samples分类
信息技术与安全科学引用本文复制引用
朱葛,林聪,高丽萍,何兆磊..基于少量无标签样本的电能表自动检定装置在线异常识别方法研究[J].电测与仪表,2026,63(4):173-181,9.基金项目
云南电网有限责任公司科技项目(YNKJXM20230134) （YNKJXM20230134）
