Integrated energy management and harmonic mitigation in a microgrid using sea gull-ANN MPPT and advanced multi-level inverter

Abstract

In the relentless quest for sustainable energy solutions on a global scale, a dynamic surge of research and innovation has been ignited within the domain of micro-grid systems. This research introduces a self-contained micro-grid system that seamlessly integrates a Solar Photovoltaic (PV) source with an emphasis on achieving effective management. The proposed approach exploits a novel Hybridized Seagull Optimization Algorithm (SOA) in conjunction with an Artificial Neural Network (ANN) for Maximum Power Point Tracking (MPPT). This hybridized approach ensures more accurate and faster tracking thereby achieving a highly adaptive and efficient mechanism. The SOA-ANN strategy is implemented through a high-gain Single-Ended Primary Inductor Converter (SEPIC)-Cuk converter, enabling efficient extraction with improved power transfer. A surplus energy management mechanism directs excess energy from hybrid sources towards battery charging when it exceeds load requirements. Harmonics mitigation is addressed by channeling DC power from the converter into an advanced Reduced Switch 11-Level Multi-Level Inverter (MLI) incorporating a switched capacitor topology for boosting and self-voltage balancing. Particle Swarm Optimized Proportional Integral (PSO-PI) controller with Sinusoidal Pulse Width Modulation (SPWM) meticulously controls the inverter, ensuring elimination of undesirable harmonics. The resulting controlled AC power, passed through an LC filter, facilitates seamless grid synchronization with a substantial reduction in harmonics. MATLAB validation demonstrate the enhanced performance of strategies and components in optimizing energy extraction, regulation, and overall system efficiency. Overall, the combination of the hybrid MPPT with advanced converter and inverter topologies offer efficient energy management with improved adaptability and power quality.