Dynamic voltage restorer based on particle swarm optimization algorithm and adaptive neuro-fuzzy inference system

Abstract

This article uses a dynamic voltage restorer to tackle a wide range of power quality issues, such as voltage drooping and swelling, spikes, distortions, and so on. The proportional controller, integrated controller (PI), and adaptive neuro-fuzzy inference system (ANFIS) are proposed dynamic voltage restorer (DVR) controllers. The control strategy's goal is to employ an injection transformer to mitigate for the needed voltage and keep the load voltage fixed. The settings of the PI controller are fine-tuned using two methods: trial and error and intelligent optimum. Particle swarm optimization (PSO) is now the most effective method. In terms of settling time, overshoot, undershoot, and disturbances around the final value, the PSO-tuned PI controller outperforms the trial-and-error PI controller. The ANFIS controller is used to regulate the DVR's responsiveness through the PI-PSO controller. The PI-PSO data is used as training data by the ANFIS controller. The results show that a DVR with an ANFIS controller outperforms a PI-PSO controller in terms of overshoot, undershoot spike voltage, steady state time, and settling time. In the case of a failure voltage, the DVR with an ANFIS controller has a 27% undershoot spike voltage while the PI-PSO controller has a 30% undershoot spike voltage.