Extended Isolation Forests for Fault Detection in Small Hydroelectric Plants

• Main Authors: Rodrigo Barbosa de Santis, Marcelo Azevedo Costa
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Sustainability
• Subjects: hydroelectric power plant; condition-based maintenance; machine learning; early fault detection; decision tree algorithm
• Online Access: https://www.mdpi.com/2071-1050/12/16/6421

Maintenance in small hydroelectric plants is fundamental for guaranteeing the expansion of clean energy sources and supplying the energy estimated to be necessary for the coming years. Most fault diagnosis models for hydroelectric generating units, proposed so far, are based on the distance between the normal operating profile and newly observed values. The extended isolation forest model is a model, based on binary trees, that has been gaining prominence in anomaly detection applications. However, no study so far has reported the application of the algorithm in the context of hydroelectric power generation. We compared this model with the PCA and KICA-PCA models, using one-year operating data in a small hydroelectric plant with time-series anomaly detection metrics. The algorithm showed satisfactory results with less variance than the others; therefore, it is a suitable candidate for online fault detection applications in the sector.