Improved LVRT Performance of PV Power Plant Using Recurrent Wavelet Fuzzy Neural Network Control for Weak Grid Conditions
Faa‐Jeng Lin, Kuang‐Hsiung Tan, Wen-Chou Luo, Guo-Deng Xiao
Abstract
An intelligent control method using recurrent wavelet fuzzy neural network (RWFNN) is proposed to improve the low-voltage ride through (LVRT) performance of a two-stage photovoltaic (PV) power plant under grid faults for the weak grid conditions. The PV power plant comprises an interleaved DC/DC converter and a three-level neutral-point clamped (NPC) smart inverter, in which the output active and reactive powers of the inverter can be predetermined in accordance with grid codes of the utilities. Moreover, for the purpose of improving the control performance of the PV power plant to handle the grid faults for the weak grid conditions, a new RWFNN with online training is proposed to replace the traditional proportional-integral (PI) controller for the active and reactive powers control of the smart inverter. Furthermore, the proposed controllers are implemented by two floating-point digital signal processors (DSPs). From the simulation and experimental results, excellent control performance for the tracking of active and reactive powers under grid faults for the weak grid conditions can be achieved by using the proposed intelligent control method.