AI-Based Adaptive MPPT for Hybrid Renewable Systems Integrating PMSG and PV

Abstract

In this study, a single Maximum Power Point Tracking (MPPT) controlling procedure for hybrid energy of wind and solar system is created for stand-alone and grid-connected applications. The hybrid distributed power system with Permanent Magnet Synchronous Generator (PMSG) driven by variable wind turbine speed and a Photovoltaic (PV) array connected to a load and the grid. Because environmental circumstances are unpredictable there exists a high degree of mismatch between power generation and usage. To avoid this unpredictability, a hybrid-based optimization strategy is utilized to employ the full power of both sources. A Multi-Level Genetic Algorithm (MLGA) is utilized iteratively to optimize the elements of the MPPT system. MLGAs are chosen for their capacity to efficiently explore high-dimensional search spaces, adaptively adjust search strategies, incorporate domain knowledge, and handle large-scale optimization problems. Unlike traditional single-level genetic algorithms may struggle with such spaces, whereas MLGAs can effectively explore multiple levels of abstraction simultaneously, enabling them to find optimal solutions more efficiently. In contract to Type-1 Fuzzy Logic Controllers (T1FLCs), which operate with crisp membership functions, T2FLCs utilize fuzzy, sets with varying degrees of uncertainty. Employing type 2 Fuzzy Logic Controller (T2FLC), both the membership functions and the fuzzy rules are defined by fuzzy sets, which are characterized by their own degrees of uncertainty. This enables the controller to make decisions more robust and adaptable to changing conditions. The T2FLCs offer improved performance and reliability in applications, including control systems for industrial processes, robotics, and autonomous vehicles and it also enhances the output accuracy. These economical controllers are recommended for the developed hybrid system for the purpose to track the greatest amount of power from both sources to independently trigger the DC-DC converter (MLGA-based MPPT) and the inverter T2FLC. In the final stage, the load demand, wind energy and PV systems are made to operate alternately, depending on their availability by utilizing MLGA and T2FLC. A dependable grid considering the financial element to determine the load modifying level, T2FLC command rules are devised. Through simulation modeling, the integrated tasks of both optimized controllers under various conditions are assessed.