A Composite Anomaly Detection System for Data-Driven Power Plant Condition Monitoring

Abstract

Data-driven condition monitoring is an essential function for power plant because of its potential to enhance asset longevity and reduce the operation and maintenance costs. This article explains the complicated relationship in multiplex power plant data as a mixture of temporal dependency and cross-variable association and proposes a composite anomaly detection system that incorporates the two data relationships on a probabilistic basis for more reliable power plant condition monitoring. It is able to dynamically capture the most significant relationship to develop more reliable normal condition interval, based on which the potential faults can be timely detected and the abnormal variable can be accurately identified. The proposed system was tested on a realistic thermal power plant. The testing results demonstrate its reliable condition monitoring and accurate anomaly detection performance, which necessitates the composite modeling of temporal dependency and cross-variable association in data-driven power plant condition monitoring.