A Fault Prognostic System for the Turbine Guide Bearings of a Hydropower Plant Using Long-Short Term Memory (LSTM)

Hydroelectricity, being a renewable source of energy, globally fulfills the electricity demand. Hence, Hydropower Plants (HPPs) have always been in the limelight of research. The fast-paced technological advancement is enabling us to develop state-of-the-art power generation machines. This has not only resulted in improved turbine efficiency but has also increased the complexity of these systems. In lieu thereof, efficient Operation & Maintenance (O&M) of such intricate power generation systems has become a more challenging task. Therefore, there has been a shift from conventional reactive approaches to more intelligent predictive approaches in maintaining the HPPs. The research is therefore targeted to develop an artificially intelligent fault prognostics system for the turbine bearings of an HPP. The proposed method utilizes the Long Short-Term Memory (LSTM) algorithm in developing the model. Initially, the model is trained and tested with bearing vibration data from a test rig. Subsequently, it is further trained and tested with realistic bearing vibration data obtained from an HPP operating in Pakistan via the Supervisory Control and Data Acquisition (SCADA) system. The model demonstrates highly effective predictions of bearing vibration values, achieving a remarkably low RMSE.

Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms

Farm businesses are increasingly adopting renewables to enhance energy efficiency and reduce reliance on fossil fuels and the grid. This shift aims to decrease dairy farms' dependence on traditional electricity grids by enabling the sale of surplus renewable energy in Peer-to-Peer markets. However, the dynamic nature of farm communities poses challenges, requiring specialized algorithms for P2P energy trading. To address this, the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES) has been developed, providing a platform to experiment with Reinforcement Learning techniques. The simulations demonstrate significant cost savings, including a 43% reduction in electricity expenses, a 42% decrease in peak demand, and a 1.91% increase in energy sales compared to baseline scenarios lacking peer-to-peer energy trading or renewable energy sources.