Active learning for accurate analysis of streaming partial discharge data

Partial discharge (PD) is a phenomenon of electric discharge typically caused by the damaged or aged insulation of high voltage equipment in power grids, such as transformers, switch gears, and cable terminals. In the context of Prognostic and Health Management (PHM), detection and monitoring of PD are important to ensure the reliability of electrical assets and to avoid catastrophic failures. Machine learning techniques have been successfully applied to discover features and patterns that correspond to different types of partial discharges [9], [11]. Recently, PD monitoring systems have being deployed for assessing the health condition of these equipments continuously so that the maintenance would require less human effort and fewer maintenance interruptions to the operation. However, such systems require labeled data to build data models for PD detection and classification. Labeled data is expensive to obtain since it requires domain expert's manual inputs. Minimizing the labeling cost is thus an important issue to solve. To the best of our knowledge, this issue has not been properly addressed in this domain. This paper proposes an active learning (AL) approach for accurate analysis of streaming PD data that aims to train an accurate PD classification model with minimum cost through selecting the most informative instances for the human experts to label. Experimental results show that our method is able to achieve the high classification accuracy of 86.9% with only a small labeling budget of 1 %.