Comparative analysis of ANN, Fuzzy MPPT in PV system with Traditional Techniques

This research presents a comparative analysis of Artificial Neural Networks (ANN) and Fuzzy Logic-based MPPT techniques PV systems, evaluating their performance against traditional MPPT algorithms, namely Perturb and Observe (P&O) and Incremental Conductance (INC). Solar PV systems require accurate and efficient MPPT methods to extract the maximum possible energy under varying environmental conditions. Conventional procedures like P&O and INC, while widely used for their simplicity, often struggle with issues like oscillations around the maximum power point, slow response under changing irradiance, and inefficiency under partial shading. ANN and Fuzzy MPPT algorithms, being adaptive and intelligent, offer potential advantages, such as faster convergence, better handling of nonlinear conditions, and superior performance in dynamic environmental situations. In this paper, simulations are conducted to assess the tracking efficiency, convergence time, and overall energy output of each method. The results reveal that ANN and Fuzzy-based MPPT outperform traditional procedures, especially in terms of accuracy and adaptability to rapid environmental changes. The findings provide the most effective MPPT techniques for improving the operation of solar PV schemes in real-world applications.