A Novel Multiobjective Control of DVR to Enhance Power Quality of Sensitive Load

Abstract

The Dynamic Voltage Restorer (DVR) is one of the fast, flexible, and cost effective solutions available in compensating the voltage-related power quality problems in power distribution systems. In this paper is discussed how power quality enhancement of sensitive load is achieved by applying three versions of Autonomous Group Particle Swarm Optimization like AGPSO1, AGPSO2, and AGPSO3 for tuning the Proportional-Integral DVR controller under balanced and nonlinear load conditions. A novel multiobjective function is formulated to express the control performance of the system, which is quantified using three power quality indices such as Total Harmonic Distortion (THD), voltage sag index, and RMS voltage variation. The obtained results are compared with the Proportional-Integral (PI) controller tuned by Ziegler-Nichols (ZN) method and also by Simple Particle Swarm Optimization based PI controlled DVR. The proposed methodology has improved the performance in terms of the considered power quality indices and the simulation has been carried out in MATLAB/Simulink environment.

Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

“…This device concentrates on the reliability and quality of the power flow of the system applied to the distribution system [6]- [8]. Various types of power electronics devices are active power filters (APF) [9], Battery energy storage systems (BESS) [10], distribution static synchronous compensators (DSTATCOM) [11], dynamic voltage restorer (DVR) [12]- [14]. DVR, one of the power electronics devices, is effective in distribution systems because it requires lower power than other devices, lower maintenance costs, good efficiency, simpler control, and easy implementation [15].…”

Autonomous group particle swarm optimisation tuned dynamic voltage restorers for improved fault-ride-through capability of DFIGs in wind energy conversion system

“…This optimisation method is free from any mathematical model and is able to identify the global optimal region rapidly. PSO algorithms that have been investigated include simple particle swarm optimisation (SPSO) [23 ], improved particle swarm optimisation (IPSO) [24 ], modified particle swarm optimisation (MPSO) [25 ], and time‐varying‐acceleration‐coefficient particle swarm optimisation (TACPSO) [26 ].…”

“…The advantages of the proposed method (AGPSO) is that here, a new approach of autonomous group is included for giving a sort of independence to particles for increasing performance. In [23 ], the DVR is used to improve the power quality of sensitive loads, hence the complexity of fault operating conditions never arise.…”