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Line outage contingencies in power systems are likely to result in overloads in branches,voltage deviations in buses and excessive power losses. The most common approach for alleviating such consequences is using flexible AC transmission systems (FACTS) devices. Since overloads are the main concern in line outage contingencies and thyristor-controlled series compensator (TCSC) has proved to be effective in power flow control, it is used for mitigating consequences of line outage contingencies. When FACTS devices are utilised in a power system, they should be allocated optimally. From the optimisation perspective, optimal allocation of FACTS devices is a very complex optimisation problem. Heuristic approaches are the most common approaches for solving FACTS allocation problems. Among heuristics, particle swarm optimisation (PSO) has some advantages which make it popular in solving FACTS allocation roblems.Despite all PSO advantages, it suffers from premature convergence.Due to PSO’s premature convergence, in TCSC allocation problem during contingency, it is not able to find near-global solution. Therefore, considerable amounts of overloads, voltage deviations and power losses are obtained. Although existing approaches mitigatePSO’s premature convergence to some extent, they have some shortcomings that should be addressed carefully.Their explorative capability is not decreased during the run.In most cases, the mutated object is transferred to new position either it leads to lower objective value or not. In most of cases, the mutations are applied to positions or velocities of particles, while applying mutation to the leader may enhance the leader and push all particles toward better regions of search space.They also do not provide any mechanism for jumping out particles after stagnation. The aim of this research is to develop a new PSO variant called improved leader PSO (ILPSO) by addressing the mentioned shortcomings of existing premature convergence mitigation strat

FACTS optimisation is one of the most important and difficult problems in power systems. For solving this problem, so many different approaches have been proposed in the literature. Among them, particle swarm optimisation (PSO) has exposed so promising behavior. In this paper, applications of PSO in FACTS optimisation problem are explained and analysed from the viewpoint of the objectives, used basic PSO variant, PSO parameter selection, multi-objective handling, constraint handling and discrete variable handling. Eventually, some hints for future research is provided.

Nowadays, particle swarm optimisation (PSO) is one of the most commonly used optimisation techniques. However, PSO parameters significantly affect its computational behaviour. That is, while it exposes desirable computational behaviour with some settings, it does not behave so by some other settings, so the way for setting them is of high importance. This paper explains and discusses thoroughly about various existent strategies for setting PSO parameters, provides some hints for its parameter setting and presents some proposals for future research on this area. There exists no other paper in literature that discusses the setting process for all PSO parameters. Using the guidelines of this paper can be strongly useful for researchers in optimisation-related fields.

This paper discusses thoroughly about different methods for solution of flexible AC transmission systems (FACTS) optimization problem in power systems. First, it explains the requirements of an ideal solution for FACTS optimization problem, then classifies the methods used by researchers in four main groups as classical methods, technical methods, heuristics and mixed methods, and discusses thoroughly about characteristics, advantage s and disadvantages of each group of methods. Finally, some tips are offered for future research on this area.

In this paper, Evolution Strategies (ES) is used to find optimal placement of FACTS devices in power systems. The goal of optimization is to maximize the system loadability. Optimization is based on finding locations and settings of FACTS devices. Simulations are implemented on IEEE 30-bus test system. From different types of FACTS devices, SVC, TCSC and UPFC are used in this research. The results show that using FACTS devices, the loadability of power system increases significantly. It also shows that there exists a maximum number of devices beyond which, the loadability of power system can not be increased. The implementation results of the method are promising and encouraging, so it is a good method for implementation on the FACTS optimization problem.

This paper presents a comprehensive review on various heuristic methods for solution of flexible AC transmission systems (FACTS) optimization problem in power systems. First, it classifies FACTS optimization methods into four main groups, then subdivides heuristic methods into different subsets and discusses thoroughly about characteristics, advantages and disadvantages of each heuristic subset. Finally, some hints for future researches on this area will be offered.