PSO based location and parameter setting of advance SVC controller with comparison to GA in mitigating small signal oscillations

Abstract

This paper aims to select the optimal location and setting parameters of Static VAR Compensator (SVC) controller using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to mitigate small signal oscillations in a multimachine power system. Though Power System Stabilizers (PSSs) are prime choice in this issue, its performance gets affected by changes in network configurations, load variations etc. Hence installation of FACTS device, SVC has been suggested here in order to achieve appreciable damping of oscillations. However, performance of any FACTS devices highly depends upon its parameters and suitable location in the power network. In this paper PSO as well as GA based techniques are used to investigate this problem. An attempt has also been made to compare the performance of the PSO based SVC controller with its GA based design. The validity of the proposed techniques is simulated in a multimachine system following two common disturbances. It has been revealed that the PSO based SVC controller is more effective than GA based controller even during critical loading condition.

Particle swarm optimisation (PSO) for allocation of FACTS devices in electric transmission systems: A review

“…In most cases, static objectives such as minimising over-power flows and voltage deviations, maximising voltage stability margin, available transfer capacity, total transfer capacity are intended. However, dynamic objectives such as maximising small-signal stability and large-signal stability have been considered in a few cases ( [64,69,79,[83][84][85]). Furthermore, FACTS devices' cost, generation cost and power losses are some other objectives that have been considered in literature.…”

“…Moreover, the problem has also been solved by simulated annealing and PSO's outperformance over SA has been concluded. In [69], optimal controller parameters and location of SVC are determined by PSO in order to maximise damping ratio. There are three tuning parameters of the SVC controller; the controller gain, lead time constant, lag time constant.…”

A Review and Comparison of FACTS Optimal Placement for Solving Transmission System Issues

“…Nevertheless, in [26]- [30] a comparison between these and other methods is done. The fact that there is not a dominant technique makes heuristic and meta-heuristics methods unreliable.…”

Optimal Tuning and Placement of Power System Stabilizers Based Eigenvalue

“…Particle Swarm Optimization (PSO) has some attractive characteristics compared to GA and other similar evolutionary techniques [15]. In [8] the optimal tuning and placement of PSS using Particle Swarm Optimization algorithm is proposed, where two eigenvalue based objective functions to enhance the damping of electromechanical modes are considered.…”