Simulation and Implementation of PI, Fuzzy and ANFIS Controller for PV and WIND Based EV Charging Station

Abstract The study utilizes a fuzzy logic-controller, as referenced in the selection of a FLC is based on its remarkable capacity to handle systems that exhibit nonlinearities and uncertainties. This capability arises from its use of operator experiences, which enhances the system with an additional layer of intelligence. FLC's primary benefit is from its autonomy from a specific mathematical model of the system, making it more resilient in comparison to traditional controllers. The unique strength of FLC is its capacity to express linguistic rules based on fuzzy sets and fuzzy algorithms. This characteristic allows it to handle the imprecision and uncertainty inherent in SPV systems, which conventional controllers may struggle to accommodate. Fuzzy logic operates on the principle of linguistic variables and rule-based reasoning, making it well-suited for systems with complex and non-linear behavior, such as SPV modules. The preceding sections have highlighted the intricate non-linear attributes found in SPV modules, which pose challenges for standard methods used in MPP Tracking (MPPT) to identify and extract the MPP. AN techniques, such as FL-based MPPT, offer a highly promising alternative. It utilizes human-like reasoning, integrating qualitative information to make judgments without requiring a precise mathematical description of the system. The use of FLC in MPPT for SPV modules reflects a strategic approach to address the challenges posed by the non-linear nature of these modules. By capitalizing on the inherent adaptability and intelligence of fuzzy logic, this technique enhances the robustness and efficiency of the SPV system, showcasing the broader applicability and advantages of AI in optimizing renewable energy systems