A Clustering-Based Method to Anomaly Detection in Thermal Power Plants
Patricia Drapal, Jullya Clemente, Dailys Maite Aliaga Reyes, Starch Melo de Souza, Anthony José Da Cunha Carneiro Lins, Ricardo B. C. Prudêncio · 2022 International Joint Conference on Neural Networks (IJCNN) · 2022
Thermal Power Plants (TPPs) produce electricity by burning fuels such as coal and oil. In Brazil, TPPs are crucial to guarantee the energy supply in periods of critical climatic conditions, as a complement to hydroelectric generation. Failures in a TPP system can cause unscheduled interruption for a long period of time, which can harm the electricity supply and result in excessive costs. The current paper addresses the problem of detecting anomalies in sensor data used to monitor the condition of TPP equipments. In the developed work, we combined clustering and statistical techniques, evaluated to detect anomalies in the cooling water system of a Brazilian TPP company. Experiments were performed in real cases of unplanned shutdown in the TPP in order to verify whether the anomaly detection method could anticipate the observed failures. The developed clustering-based method was able to detect the anomalous cases several minutes in advance, at same time obtaining fewer false alarms compared to a baseline method in literature.