Probabilistic Wavelet Fuzzy Neural Network based reactive power control for grid-connected three-phase PV system during grid faults. (July 2016) - Lin, Faa-Jeng; Lu, Kuang-Chin; Ke, Ting-Han
Abstract: This study presents a reactive power controller using Probabilistic Wavelet Fuzzy Neural Network (PWFNN) for grid-connected three-phase PhotoVoltaic (PV) system during grid faults. The controller also considers the ratio of the injected reactive current to meet the Low Voltage Ride Through (LVRT) regulation. Moreover, the balance of the active power between the PV panel and the grid-connected inverter during grid faults is controlled by the dc-link bus voltage. Furthermore, to reduce the risk of over-current during LVRT operation, a current limit is predefined for the injection of reactive current. The main contribution of this study is the introduction of the PWFNN controller for reactive and active power control that provides LVRT operation with power balance under various grid fault conditions. Finally, some experimental tests are realized to validate the effectiveness of the proposed controller. Highlights: A controller using Probabilistic Wavelet Fuzzy Neural Network (PWFNN) for grid-connected PhotoVoltaic (PV) system. Dc-link bus voltage and Maximum Power Point Tracking (MPPT) control of PV system are considered to ensure the power balance. The network structure, online training algorithms and convergence analysis of the proposed PWFNN controller are presented. The dual mode operation control strategy of the PV side converter during grid faults is developed. The control performance of the PWFNN controller is examined by some experimental results.