{'fraction_non_alphanumeric': 0.01896551724137931, 'fraction_numerical': 0.0, 'mean_word_length': 5.414364640883978, '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}}
Artificial Neural Network Controlled Electric Spring for SEIG based Isolated Microgrid

Due to the expansion of distributed power generation technology, gradually renewable energy sources (RESs) are evolved as new generation plug-ins to the modern-day power system. But the heavy penetration of RESs unfolds the stability threats to voltage and frequency. Electric Spring (ES) is a new idea to the solution of voltage fluctuation due to intermittency nature of RESs in distributed networks. In this proposed work, the proficiency of minimizing voltage fluctuation due to wind speed variation is analyzed for self-excited induction generator (SEIG) oriented islanded microgrid. An artificial neural network (ANN) based ES is proposed for voltage control across critical load that is capable of restricting the voltage hike and dip to a limited range during wind speed change. The suggested control approach is tested and validated in MATLAB/Simulink environment. The simulation result show that the ANN achieve better voltage stabilization effect than traditional PI control, which further resolves the adverse effect of voltage fluctuation on system equipment.