Hybrid multi-swarm particle swarm optimisation based multi-objective reactive power dispatch

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.

[1]  K. C. Mamandur,et al.  Optimal Control of Reactive Power flow for Improvements in Voltage Profiles and for Real Power Loss Minimization , 1981, IEEE Transactions on Power Apparatus and Systems.

[2]  T. M. Abdel-Rahman,et al.  Non-Linear Var Optimization Using Decomposition And Coordination , 1984 .

[3]  Q. H. Wu,et al.  Power system optimal reactive power dispatch using evolutionary programming , 1995 .

[4]  Yuancheng Li,et al.  A hybrid artificial bee colony assisted differential evolution algorithm for optimal reactive power flow , 2013 .

[5]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[6]  S. C. Srivastava,et al.  Corrective action planning to achieve a feasible optimal power flow solution , 1995 .

[7]  Yoshikazu Fukuyama,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 2000 .

[8]  Bhimrao S. Umre,et al.  Reactive power control using dynamic Particle Swarm Optimization for real power loss minimization , 2012 .

[9]  D. Bhagwan Das,et al.  A new hybrid evolutionary strategy for reactive power dispatch , 2003 .

[10]  Alireza Mousavi,et al.  Hybrid Mutation Particle Swarm Optimisation method for Available Transfer Capability enhancement , 2012 .

[11]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[12]  K. Lee,et al.  A United Approach to Optimal Real and Reactive Power Dispatch , 1985, IEEE Transactions on Power Apparatus and Systems.

[13]  Wadaed Uturbey,et al.  Performance assessment of PSO, DE and hybrid PSO–DE algorithms when applied to the dispatch of generation and demand , 2013 .

[14]  Muhammad Khurram Khan,et al.  A hybrid particle swarm optimization algorithm for high-dimensional problems , 2011, Comput. Ind. Eng..

[15]  Laxmi Srivastava,et al.  Modified Differential Evolution algorithm for multi-objective VAR management , 2014 .

[16]  William F. Tinney,et al.  Optimal Power Flow Solutions , 1968 .

[17]  Kenji Iba.  Reactive power optimization by genetic algorithm , 1993 .

[18]  S. Granville.  Optimal reactive dispatch through interior point methods , 1994 .

[19]  M. A. Abido,et al.  Optimal power flow using particle swarm optimization , 2002 .

[20]  Sakti Prasad Ghoshal,et al.  Optimal VAR control for improvements in voltage profiles and for real power loss minimization using Biogeography Based Optimization , 2012 .

[21]  M. A. Abido,et al.  Optimal VAR dispatch using a multiobjective evolutionary algorithm , 2005 .

[22]  Mohammad Ali Abido,et al.  Differential evolution algorithm for optimal reactive power dispatch , 2011 .

[23]  Yuren Zhou,et al.  Multiobjective Optimization and Hybrid Evolutionary Algorithm to Solve Constrained Optimization Problems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[24]  Leandro dos Santos Coelho,et al.  Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[25]  Yong Wang,et al.  A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems , 2009, Frontiers of Computer Science in China.

[26]  Loi Lei Lai,et al.  Application of evolutionary programming to reactive power planning-comparison with nonlinear programming approach , 1997 .

[27]  G. Lambert-Torres,et al.  A hybrid particle swarm optimization applied to loss power minimization , 2005, IEEE Transactions on Power Systems.

[28]  Jing J. Liang,et al.  Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[29]  K. S. Swarup,et al.  Differential evolutionary algorithm for optimal reactive power dispatch , 2008 .

[30]  Ahmed Yousuf Saber,et al.  Economic dispatch using particle swarm optimization with bacterial foraging effect , 2012 .