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.

Highlights

  • As the penetration level of inverter-based distributed generators (DGs) including renewable energy resources (RERs) increases, the stability margin of the distribution system may be detrimentally affected

  • Lin et al.: Improved low-voltage ride through (LVRT) Performance of PV Power Plant Using recurrent wavelet fuzzy neural network (RWFNN) Control was proposed to improve the performance of LVRT and guarantee the power flow balancing between inverter and MPPT during grid faults

  • An emulated weak grid is connected between the point of common coupling (PCC) of the PV power plant and the grid, and the resulted SCR is only 3

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Summary

INTRODUCTION

As the penetration level of inverter-based distributed generators (DGs) including renewable energy resources (RERs) increases, the stability margin of the distribution system may be detrimentally affected. In [6], several reference current generation methods, which were developed based on the positive–negative sequence control strategy, were reported to provide the LVRT requirements for the grid-connected inverter based DGs. A Karush–Kuhn–Tucker condition for finding optimal solutions to calculate the inverter’s active and reactive current references is proposed in [7]. Since current grid-connected PV power plant lacking the capability of stabilizing output voltage and power under grid faults for the weak grid conditions, an intelligent control. Advanced controller is required to take the place of the conventional PI controller for the control of active and reactive powers to improve the stability of the grid-connected PV power plants especially for the LVRT under grid faults.

ONLINE LEARNING ALGORITHM OF RWFNN
CONVERGENCE ANALYSIS OF RWFNN
DESIGN AND SIMULATION
Findings
CONCLUSION
Full Text
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