Title :
Hybrid multi-swarm particle swarm optimisation based multi-objective reactive power dispatch
Author :
Srivastava, Laxmi ; Singh, Himmat
Author_Institution :
Dept. of Electr. Eng., Madhav Inst. of Technol. & Sci., Gwalior, India
Abstract :
Most of the real-world optimisation problems are subject to different types of constraints and are known as constrained optimisation problems. Reactive power dispatch (RPD) in electrical power system is also a non-linear, multi-objective or a single objective constrained optimisation problem. In this study, hybrid multi-swarm particle swarm optimisation (HMPSO) algorithm has been proposed to solve the RPD problem. HMPSO is one of the recently proposed population based search algorithm, in which the existing swarm is partitioned into several sub-swarms. Particle swarm optimisation is applied as the search engine for each sub-swarm. In addition, to explore more promising regions of the search space, differential evolution (DE) algorithm is implemented to improve the personal best of each particle. The RPD problem is formulated as non-linear, constrained multi-objective optimisation problem with equality and inequality constraints for minimisation of power losses and improvement of voltage profile simultaneously. To find the Pareto optimal set for RPD problem, weighted sum method has been applied. Afterwards, for finding the preferred solution out of the Pareto-optimal set, fuzzy membership function has been used. Effectiveness of the HMPSO algorithm has been verified on the standard IEEE 30-bus and a practical 75-bus Indian systems.
Keywords :
Pareto optimisation; evolutionary computation; fuzzy set theory; load dispatching; particle swarm optimisation; search problems; HMPSO algorithm; Pareto optimal set; RPD; constrained optimisation problems; differential evolution algorithm; fuzzy membership function; hybrid multiswarm particle swarm optimisation; multiobjective optimisation problem; multiobjective reactive power dispatch; population based search algorithm; power losses minimisation; search engine;
Journal_Title :
Generation, Transmission & Distribution, IET
DOI :
10.1049/iet-gtd.2014.0469