Adaptive MPPT with Integrated PSO-Tuned ANFIS Method for Optimal Performance in Grid-Connected PV Systems Using Hybrid PSO-ANN Model

In this research, we focus on increasing the power produced by solar panels under various environmental conditions. To optimize efficiency, we employ an Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) technique. This method leverages the strengths of both fuzzy logic and neural networks to enhance tracking accuracy. To further improve the adaptability and performance of the controller, we utilize Particle Swarm Optimization (PSO) to tune its parameters. This tuning process allows the controller to dynamically adjust to changing environmental conditions, making it more effective in real-world applications. By integrating PSO with ANFIS, our goal is to enhance the precision of tracking the maximum power point and enable the system to respond swiftly to variations in solar radiation. We conducted performance evaluations using simulations and compared the results with those obtained from existing MPPT methods. The findings indicate that our proposed technique efficiently generates electricity in grid-connected solar systems. This research makes a significant contribution to the field of renewable energy by presenting a smart and flexible approach to harnessing solar energy. The combination of PSO and ANFIS not only improves the efficiency of solar power generation but also ensures that the system can adapt to diverse and dynamic environmental conditions. As a result, this technique offers a promising solution for enhancing the performance and reliability of solar power systems, thereby supporting the broader adoption and integration of renewable energy sources into the grid.