Anomaly Detection for Hydroelectric Generating Units by Fast Robust Random Cut Forest with Fast Feature Selection Considering Characteristics of Operating Data and Random Cut Trees

Abstract

This paper proposes anomaly detection for hydroelectric generating units by Fast Robust Random Cut Forest with a fast feature selection method by considering characteristics of operating data and Random Cut Trees. Hydroelectric generating units are renewable electric generating sources for electricity supply. Therefore, it is crucial to accurately detect anomalies of hydroelectric generating units. Moreover, effective features for anomaly detection should be selected to reduce operation costs of anomaly detection services. The proposed anomaly detection method can detect anomalies approximately thirty times faster than the conventional Robust Random Cut Forest with an accuracy comparative to that of the conventional method. The proposed feature selection method can select effective features almost fifty times faster than the conventional feature selection method.