{'fraction_non_alphanumeric': 0.02, 'fraction_numerical': 0.00875, 'mean_word_length': 5.459677419354839, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Comparison of Maximum Power Tracking using Artificial Intelligence based optimization controller in Photovoltaic Systems

This paper analyzes performance of Artificial Intelligence based optimization controller for the comparative study of maximum power point tracking (MPPT) in PV Systems. Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) control methods are the two such techniques used, and are simulated in MATLAB-Simulink using Trina Solar TSM-250PD05.08. The simulation results suitably depict the performance of these methods on the basis of some parameters like their rise time, settling time, time taken to reach maximum power point and their efficiency. It is found that maximum power point is tracked in PV systems with greater efficiency using PSO as compared to ANN.