Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System

When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although incorporation of bypass diodes at output alleviates effect PS, such modification results in multiple peaks power. Conventional algorithms-such as perturb and observe (P&O;) hill-climbing (HC)-are not suitable be employed track optimal peak due their convergence local maxima. To address this issue, various artificial intelligence (AI) based an neural network (ANN) fuzzy logic control (FLC)-have been maximum power point (MPP). these algorithms provide satisfactory under PS conditions, a very large amount data is required for training process, thereby imposing excessive burden on processor memory. Consequently, paper proposes novel optimization algorithm stochastic search (random exploration space), known adaptive jaya (Ajaya) which two coefficients are incorporated tracking (MPPT) with rapid rate, fewer fluctuations high stability. The successfully eliminates issues associated existing conventional AI-based algorithms. Moreover, proposed outperforms other state-of-the-art search-based techniques terms fluctuations, robustness, simplicity, faster optima. Extensive analysis obtained MATLAB ® done prove above performance parameters static insolation conditions (using three, four five-module series-connected PV system), dynamically varying four-module series connected by changing module rating system) using IEC standard.