Optimum control for dynamic voltage restorer based on particle swarm optimization algorithm

Abstract

This article <span>addresses a variety of power quality concerns, including voltage sag and swell, surges, harmonics, and so on, utilizing a dynamic voltage restorer (DVR). The proposed controller for DVR is proportional plus integral (PI) controller. Two methods are used for tuning the parameters of PI controller, trial and error and intelligent optimal method. The utilized optimal method is particle swarm optimization (PSO) method. Results depicted that DVR using PI controller tuned by PSO has improved performance than PI controller tuned by trial and error in term of rise time, maximum overshoot and settling time, as well as total harmonic distortion (THD). These improvements are applicable for voltage sag and swell conditions.</span>

ANFIS control of four-quadrant DC/DC chopper for wireless EV charging

“…Proportional gain (kp) and integral gain (ki) are the two parameters that must be chosen (and occasionally optimized) to the specified procedure for the desired responses output from the four quadrants DC-DC chopper Particle swarm optimization can be used to adapt this controller (PSO) quickly. Hence, the advantage of the best position in its memory and knowledge of the better location is that each bird "particle" uses this strategy to update its location inside the swarm to find the ideal place [17,18]. Figure 3 illustrates the Algorithm flowchart for PSO.…”

Dynamic Voltage Restorer as a Solution to Voltage Problems in Power Systems: Focus on Sags, Swells and Steady Fluctuations

“…In some instances, trial-and-error approaches have been used as techniques for optimisation. To control a DVR aimed at mitigating voltage sags and swells, a PI controller was proposed by Salman et al [84], with the parameters of this PI controller tuned using the trial-and-error method as well as the PSO method, and a comparison of the results was shown that indicated the better performance of the DVR using the PI controller tuned using PSO in terms of rise time, maximum overshoot and settling time, as well as total harmonic distortion (THD). For a DVR employed to tackle power quality problems, such as voltage sags and swells, spikes, distortions, etc., Salman et al [85] proposed the use of PI and ANFIS for a DVR, with the settings of the PI controller firstly fine-tuned using the trial-and-error method and, secondly, the PSO, with latter being more effective based on settling time, overshoot, undershoot and disturbances around the final value.…”

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

“…where Vij(d) is the particle ith speed in a j dimension at iteration d, xij(d) is the particle ith location in a j dimension at iteration d, Pbest is the perfect previous location of ith particle, Gbest is the perfect particle among all the population, W is the inertia weight factor, (C1 and C2) are the acceleration constants, (r1 and r2) are the random integers between [0-1], and n is the swarm size [32], [33].…”