ANN CONTROLLER FOR PREDICTIVE CONTROL OF PMSG-BASED HYDRO-ELECTRIC GENERATION WITH BATTERY-SUPPORTED UPQC

This research introduces an Artificial Neural Network (ANN)-based predictive control approach for hydroelectric power generation using Permanent Magnet Synchronous Generators (PMSG) integrated with a battery-supported Unified Power Quality Conditioner (UPQC).The proposed control system is designed to enhance power quality, ensure voltage and frequency stability, and maintain smooth grid interaction under dynamic load and source variations.The ANN controller is employed within the predictive control framework to optimize UPQC switching, enabling rapid response and robust mitigation of common power quality issues such as harmonics, voltage unbalance, and disturbances.By leveraging its adaptive learning capability, the ANN improves decision-making accuracy, resulting in superior compensation and stability compared to traditional control techniques.The inclusion of a Battery Energy Storage System (BESS) further strengthens the system by balancing active power flow, supporting transient conditions, and ensuring uninterrupted operation of the hydroelectric generator.Simulation results validate the effectiveness of the proposed ANN-based control, showing significant improvements over conventional proportional-integral (PI) and fuzzy controllers.Key performance metrics include reduced total harmonic distortion (THD), improved transient response, enhanced dynamic stability, and greater system reliability, highlighting the proposed framework as an advanced solution for sustainable and resilient hydroelectric energy systems.