Analysis of Various Maximum Power Point Tracking Algorithms Using Hybrid Energy Systems

Nowadays, the need for electricity is progressively rising as a result of population expansion and industrial growth. However, traditional power plants are inadequate in satisfying consumer demand, primarily due to environmental considerations. The performance of various Maximum Power Point Tracking (MPPT) algorithms is assessed in this study, including Perturb and Observe (P&O), Incremental Conductance (IncCond), Short-Circuit Current (SCC), as well as Deep Learning approaches like Neural Networks (NN) and Genetic Algorithms (GA), etc., A comparative analysis is conducted, considering important performance metrics such as tracking efficiency, response time, convergence rate, and accuracy across different environmental and load conditions. Additionally, this research explores the integration of MPPT algorithms with advanced control strategies, such as adaptive control and predictive control to enhance the overall performance of microgrid systems. These control strategies utilize real-time data, weather forecasts, and load predictions to intelligently allocate power generation and storage resources, ensuring reliable and efficient operation of microgrids. The findings shed light on the strengths and limitations of each algorithm, enabling system designers and operators to make informed decisions to enhance the performance, stability, and energy management of microgrids.