Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection
Omar Farhan Al-Hardanee,
Hüseyin Demirel
Abstract:In 2019, more than 16% of the globe’s total production of electricity was provided by hydroelectric power plants. The core of a typical hydroelectric power plant is the turbine. Turbines are subjected to high levels of pressure, vibration, high temperatures, and air gaps as water passes through them. Turbine blades weighing several tons break due to this surge, a tragic accident because of the massive damage they cause. This research aims to develop predictive models to accurately predict the status of hydroel… Show more
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