| Summary: | 碩士 === 國立中央大學 === 電機工程學系 === 102 === A new active and reactive power control scheme using two-dimensional recurrent fuzzy cerebellar model articulation neural network (2D-RFCMANN) for a single-stage three-phase grid-connected photovoltaic (PV) system during grid faults is proposed in this study. The presented PV system utilizes a single-stage three-phase current-controlled voltage-source inverter to achieve the maximum power point tracking (MPPT) control of the PV panel with the function of low voltage ride through (LVRT).Thus, a formula based on positive sequence voltage for evaluating the percentage of voltage sag is derived to determine the ratio of the injected reactive current to satisfy the LVRT regulations. Moreover, an incremental conductance (IC) method is adopted for the MPPT control. Furthermore, the constraint of the active and reactive power command of the control scheme is according to the ratio of the reactive current in order to meet the LVRT regulations. To reduce the risk of over-current during LVRT operation, a current limit is predefined for the injection of reactive current. In addition, the recurrent network is embedded in the first layer of the 2D-RFCMANN and a Gaussian basis function is used to model the hypercube structure. The online learning laws of 2D-RFCMANN are derived according to gradient descent method. Additionally, specific learning-rate coefficients for network parameters to assure the convergence of the tracking error are derived using Lyapunov function. Finally, some experimental tests are realized to validate the effectiveness of the proposed control scheme.
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