Optimal placement and parameter settings of unified power flow controller device using a perturbed particle swarm optimization

Optimal siting and sizing of UPFC using evolutionary algorithms

“…8.The objectives considered are minimization of total cost including installation cost and maximization of loadability as an added constraint. From the figure, it is clear that Line 10 (6-8) followed by lines (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22) and (29-30) are ideal locations for the placement of UPFC.…”

“…From this figure, it is clearly seen that NSGA-II algorithm is able to generate Pareto-front in a single simulation run. The ideal locations of UPFC placement are lines (6-9), (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and (30-31).The cost and loadability corresponding to the extreme points of NSGA-II and reference Pareto-front are given in Table 10. The cost corresponding to the loadability extreme point obtained in NSGA-II method is 75.734e 6 dollars.…”

“…The optimal location of UPFC for maximizing loadability using genetic algorithm with decoupled model of UPFC is discussed [4]. Perturbed particle swarm optimization is employed to identify the optimal location and control parameter settings of UPFC using decoupled model for enhancing system security under deregulated environment [19]. Sensitivity analysis is used to screen the location of UPFC for minimum power generation cost using transformer model of UPFC [23].…”

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

“…In [48], a modified PSO called perturbed PSO is utilised to determine optimal location and settings of UPFC in order to minimise power flow violations and voltage deviations under single line contingencies. A contingency analysis is first conducted to identify the most severe line outage contingencies.…”

“…However, in some cases, other PSO variants are adopted for tackling FACTS allocation problem. Constricted PSO [63,86], fuzzy adaptive inertia weight PSO [84], hybrid of PSO and GA [65,85], inertia-less PSO [47], PSO with perturbed g best [48], hybrid of evolutionary PSO and Tabu search [53][54][55], probabilistic PSO [58], PSO with time-varying acceleration coefficients [61], fixed inertia weight PSO [77] and random inertia weight PSO [62] are other PSO variants that have been applied to FACTS allocation problem.…”