基于TSVM模型的智能电能表自动化检定系统异常检测

Anomaly Detection of Automatic Verification System for Smart Meter Based on TSVM Model

智能电能表自动化检定系统在长期运行过程中可能出现异常,但定期人工检测方法无法及时获悉风险信息,缩短人工检查的周期又将降低自动化检定的工作效率。在多数情况下异常表位样本数据是无标记的,通常采用无监督式异常检测算法筛选异常表位,为降低无监督异常检测的误报率、减少人工检查的代价,提出请求对无监督筛选的“异常表位”进行人工检查,在排除表位故障的同时获得少量标记样本,利用标记和无标记样本构建半监督方式直推式支持向量机(TSVM)异常检测模型,在以后的自动化检定工作过程中不断获取新的标记样本与未标记样本,可继续按照半监督训练方式对TSVM模型进行扩展及优化。使用提出的方法对国网上海市电力公司自动化检定数据进行了分析,对比人工检查结果,验证了方法的有效性。

Automatic verification system of smart meter may be abnormal in the long-term operation process,but the regular manual detection method can not timely learn the risk information,shortening the period of manual inspection will reduce the work efficiency of automatic verification. In most cases,the sample data of abnormal epitopes are unmarked,and the unsupervised anomaly detection algorithm is usually used to screen abnormal epitopes. In order to reduce the false positive rate of unsupervised anomaly detection and the cost of manual inspection,a request for manual inspection of unsupervised screening "abnormal epitopes" was proposed,in the process of eliminating epitope faults,a small number of labeled samples were obtained,and semi-supervised transductive support vector machine(TSVM) anomaly detection model was constructed by using labeled and unlabeled samples. In the future automatic verification work process,new labeled samples and unlabeled samples were obtained continuously,the TSVM model could be extended and optimized according to semi-supervised training method. Using the proposed method,the automatic verification data of state grid Shanghai electric power company was analyzed,and compared with the manual inspection results,the effectiveness of the method was verified.