Data-Driven Condition Monitoring on Water Conduit Systems of Hydropower Plants

Recent developments and trends in power systems have increased the importance of dynamic modeling and monitoring of system components. Increased penetration of renewable energy sources and battery storage systems makes grid operation challenging. Being environment-friendly and fast-responding, hydroelectric power plants will participate in the generation as a balancing factor while introducing inertia. They will operate dynamically—as a reserve in frequency regulation and load-generation balancing— due to the intermittent characteristics of wind and photovoltaics (PVs). Therefore, their condition monitoring and health assessment should be performed regularly or in real time to ensure that the plant is ready whenever needed. In this research, a data-driven condition monitoring method is introduced in which the health status of the water conduit system is assessed from the turbine’s startup process. The proposed “PbyGate Analysis” method briefly obtains the expected behavior and healthy/anomalous operation regions from the historical data. Then the unit is monitored in real time with the online SCADA measurements. The method is developed and tested on three different hydroelectric turbine data. Startups are tagged as healthy or anomalous with 84.5% accuracy.