Abstract
The parameter variations in the microgrid models including internal and external faults, modelling uncertainties, unexpected changes in load or disturbances and harmonic currents are unavoidable in a microgrid. Here, a hybrid energy generation system consisting of Proton-Exchange Membrane Fuel Cells)PEMFC(/PhotoVoltiaic)PV (/Battery Energy Storage System)BESS(is considered. A new Backstepping Fractional order Adaptive Terminal Sliding Mode Control (BFATSMC) based on Reinforcement Learning (RL) is designed to soar robustness and disturbance rejection for an autonomous microgrid. To verify the performance of the BFATSMC a comparative study is investigated and the proposed method is applied to an islanded microgrid. In the presence of nonlinear and unbalanced loads with harmonic, unmodeled dynamics, and parametric uncertainties, the proposed method has successfully controlled the voltage.
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