Contribution to the Control of a Hybrid Renewable Energy Generation System Supplying an Active Power Filter with Intelligent Energy Management

Abstract

The increasing global demand for clean and sustainable energy has driven the widespread integration of renewable energy sources (RES), particularly photovoltaic (PV) and wind systems, into modern power grids. However, the inherent intermittency of these sources, coupled with the presence of nonlinear loads and the variability of power demand, poses significant challenges to power quality (PQ) and system stability. This thesis presents a comprehensive framework for the design, control, and management of a hybrid PV–wind microgrid that ensures efficient energy utilization and enhanced PQ under dynamic operating conditions. An intelligent energy management system (EMS) is developed to coordinate the energy flow between renewable sources, battery energy storage, and the grid, considering load requirements and system constraints. Advanced artificial intelligence-based Maximum Power Point Tracking (MPPT) techniques are proposed to optimize energy harvesting from RESs in real time. Additionally, series, shunt, and hybrid active power filters are integrated to mitigate harmonic distortions, voltage fluctuations, and waveform unbalance. The proposed system is modeled and validated through simulation studies under various scenarios, including nonlinear loads and grid disturbances. The results demonstrate significant improvements in total harmonic distortion (THD), voltage regulation, energy efficiency, and system reliability, making the framework a robust and scalable solution for next-generation smart and resilient power networks.