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This paper proposes a highly sophisticated controller to track the maximum power point (MPP) of Photovoltaic (PV) systems. This method is based on the Artificial Neural Network (ANN) algorithm, which uses Radial Basis Neural Network (RBNN) to estimate the optimum voltage for the considered PV system, which helps to extract Global MPP. The critical methodology lies in the RBNN block generation, which considers one-year real-time data of the Panaji, Goa (India) region for the training process to drive this extensive PV system with resistive load. Nearly 1500 samples of one-year real-time Irradiation (G) and Temperature (T) are given as input to RBNN. The proposed intelligent technique only consists of single-stage ANN, thereby reducing the processing time and memory allocations for generating the corresponding Vmpp value for each G and T. A comparative study has been done using conventional techniques like Perturb and Observe (P &amp; O) and Incremental Conductance (InC) methodologies. It was found that RBNN MPPT is best to use with PV modules affected by partial shading; on average, the tracking accuracy ranging from 94.6% to 97.4%. The response time of the RBNN method to reach MPP is 0.007 s which is much faster than the P &amp; O method (0.15 s) and InC method (0.268 s). Also, very few oscillations are observed with the RBNN method than the other two in the transient tracking period. Obtained results indicate that the proposed inherent learning-based controller, with its enhanced efficiency conversion and faster tracking speeds, ensures better reliability for the complete PV system.

Application of Radial Basis Neural Network in MPPT Technique for Stand-Alone PV System Under Partial Shading Conditions