A Peer Review on MPPT Extraction in Structured Hybrid Power Systems with Optimization Topologies

This chapter delves into the critical evaluation of Maximum Power Point Tracking (MPPT) extraction methods in structured hybrid power systems, emphasizing various optimization topologies. It begins with an introduction to the challenges of power supply in remote areas and the limitations of conventional energy sources, advocating for the integration of renewable energy sources like wind and solar power. The literature review highlights the importance of MPPT in hybrid systems, supported by case studies that showcase different optimization techniques such as Elephant Herding Optimization (EHO), Artificial Bee Colony (ABC) algorithm, and fuzzy logic controllers. The analysis of MPPT techniques compares methods like Perturb and Observe (P&O), Incremental Conductance (IC), Hill Climbing (HC), and others, discussing their strengths, limitations, and potential benefits. The chapter concludes by underscoring the necessity of advanced optimization topologies, such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Fuzzy Logic Controllers (FLC), for enhancing MPPT performance in hybrid power systems. These methods provide adaptive and robust solutions capable of dealing with the non-linearities and uncertainties inherent in renewable energy sources, making them essential for maximizing the efficiency of hybrid power systems.