ANFIS Based Energy Management and Harmonic Reduction in an Islanded Hybrid Renewable Energy Microgrid System

Engineering notes

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Chinese explanation / 中文解读

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Original abstract

The proposed islanded hybrid renewable energy system integrates a photovoltaic array, a wind turbine, and a Battery at the Point of Common Coupling. Initially, the PV and wind inverters are controlled using a Fuzzy Logic Controller, which utilizes the DC-link voltage, DC power, and AC power as input variables to generate PWM gating signals for the inverters. These signals controls the inverter output voltage, frequency, and power delivered to the PCC. However, the performance of the conventional FLC depends heavily on predefined membership functions and expert-designed fuzzy rules, limiting its adaptability under varying operating conditions. To overcome these limitations, an Adaptive Neuro-Fuzzy Inference System is proposed. The ANFIS is trained with the backpropagation algorithm with FLC generated data, enabling automatic tuning of the controller parameters. Consequently, the proposed ANFIS based controller improves control accuracy, enhances active power transfer from the PV array, wind turbine, and BESS, and significantly reduces total harmonic distortion, resulting in superior power distribution and under varying operating conditions of the environment.

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