Machine-Learning-Based Modeling of a Hydraulic Speed Governor for Anomaly Detection in Hydropower Plants

Abstract

Hydroelectric power plants (HEPPs) are renewable energy power plants with the highest installed power in the world. The control systems are responsible for stopping the relevant unit safely in case of any malfunction while ensuring the desired operating point. Conventional control systems detect anomalies at certain limits or predefined threshold values by evaluating analog signals regardless of differences caused by operating conditions. In this study, using real data from a large hydro unit (>150 MW), a normal behavior model of a hydraulic governor’s oil circulation in an operational HEPP is created using several machine learning methods and historical data obtained from the HEPP’s SCADA system. Model outputs resulted in up to 96.45% success of prediction with less than 1% absolute deviation from actual measurements and an R2 score of 0.985 with the random forest regression method. This novel approach makes the model outputs far more appropriate to use as an active threshold value changing for different operating conditions, eliminating insufficiency of the constant threshold, resulting in the detection of anomalies in early stages and taking precautions accordingly. The study fills the gap in the literature on anomaly detection in hydraulic power units, which is a demanding task with state-of-the-art methods.