In order to improve the functionality and efficiency of a unified power quality conditioner (UPQC), a DC-link bus integrated photovoltaic charging module is proposed in a UPQC. It can generate power for essential loads apart from providing energy to a DC-link bus. A conventional proportional integral (PI) controller fails to run smoothly in dynamic conditions of the micro-grid, since it has poor capabilities in determining suitable values of proportional gain and integral gain. So, the optimization algorithm for a PI controller based on chaos particle swarm optimization based on a multi-agent system (CPSO-MAS) algorithm was developed in this paper to overcome properties such as intermittent instability in the micro-grid. Through verification by simulation and experiment of UPQC harmonic compensation, it showed that the proposed DC link bus voltage control strategy can be effectively applied to UPQC towards various conditions related to voltage and current distortion. In addition, it proved that the proposed strategy has faster convergence than other algorithms, which enhances the stability of DC-link bus voltage. Hence, the contribution presented in this paper is to provide a novel approach for the power quality improvement of UPQC.
Aiming at the nonlinear relationship between the evaluation indexes and the complex grade of power quality, a
power quality evaluation method is proposed based on model of pulse coupled neural network (PCNN). The operating
schematic is analyzed according to PCNN modeling. The input power quality grade is controlled by threshold value, and
the evaluation grade is output by neuron ignition. The evaluation methods between PCNN and fuzzy neural network are
compared through the measured data, the results indicate that PCNN method is more scientific and accurate, and provides
a new method to solve the problem on power quality evaluation of distribution network containing microgrid.
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