International Journal of Electrical Energy, Vol. 2, No. 2, June 2014 ©2014 Engineering and Technology Publishing 172 doi: 10.12720/ijoee.2.2.172-177 Particle Sharing Based Particle Swarm Frog Leaping Hybrid Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem K. Lenin, B. Ravindranath Reddy, and M. Surya Kalavathi Jawaharlal Nehru Technological University Kukatpally, Hyderabad 500 085, India Email: gklenin@gmail.com, {bumanapalli-brreddy, munagala12}@yahoo.co.in Abstract—This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. A particle sharing based particle swarm frog leaping hybrid optimization algorithm (PSFLH) is used to solve the reactive power dispatch problem. The algorithm uses the good global search capability of particle swarm and the strong local search ability of shuffled frog leaping algorithm, and overcomes the shortcomings of swarm intelligence algorithms to fall into local optimum at later stage and “premature” convergence. Simulation results show that this algorithm has better coverage optimization results. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms and simulation results show that (PSFLH) is more efficient than other algorithms for solution of single-objective ORPD problem. Index Terms—shuffled frog leaping algorithm, particle swarm optimization, optimal reactive power, transmission loss gradient and Newton methods suffer from the difficulty in handling inequality constraints. To apply linear programming, the input-output function is to be expressed as a set of linear functions which may lead to loss of accuracy. Recently, global optimization techniques such as genetic algorithms have been proposed to solve the reactive power flow problem [8], [9]. In recent years, the problem of voltage stability and voltage collapse has become a major concern in power system planning and operation. To enhance the voltage stability, voltage magnitudes alone will not be a reliable indicator of how far an operating point is from the collapse point [10]. The reactive power support and voltage problems are intrinsically related. Hence, this paper formulates the reactive power dispatch as a multi- objective optimization problem with loss minimization and maximization of static voltage stability margin (SVSM) as the objectives. Voltage stability evaluation using modal analysis [10] is used as the indicator of voltage stability. Particle Swarm Optimization (PSO) algorithm was originally an evolutionary computation technique proposed by Kennedy and Eberhart [11] in 1995, from observation and study of the predatory behaviour of birds. Later Shi and Eberhart [12] I. INTRODUCTION Optimal reactive power dispatch problem is one of the difficult optimization problems in power systems. The sources of the reactive power are the generators, synchronous condensers, capacitors, static compensators and tap changing transformers. The problem that has to be solved in a reactive power optimization is to determine the required reactive generation at various locations so as to optimize the objective function. Here the reactive power dispatch problem involves best utilization of the existing generator bus voltage magnitudes, transformer tap setting and the output of reactive power sources so as to minimize the loss and to enhance the voltage stability of the system. It involves a non linear optimization problem. Various mathematical techniques have been adopted to solve this optimal reactive power dispatch problem. These include the gradient method [1], [2], Newton method [3] and linear programming [4]-[7]. The Manuscript received December 2, 2013; revised March 21, 2014. introduced the inertia weight to balance global search and convergence rate, forming the current standard PSO. Shuffled Frog Leaping Algorithm (SFLA) is swarm intelligence based sub-heuristic computation optimization algorithm proposed in 2003 by Muzaffar Eusuff and Kevin Lansey [13], to solve discrete combinatorial optimization problem. The two algorithms are simple in concept, have less parameter, fast calculation speed, global search capability, and are easy to implement. In just more than a decade, they have gained great development, made good applications in some areas, and become a research hotspot in the field of intelligent computing [14]. Using the good global search capability of particle swarm and the strong local search ability of shuffled frog leaping algorithm, we combine particle swarm and shuffled frog leaping algorithm, proposes a particle sharing based particle swarm frog leaping hybrid optimization algorithm, and applies it to reactive power optimization problem. The performance of (PSFLH) has been evaluated in standard IEEE 30 bus test system and the results analysis shows that our proposed approach outperforms all approaches investigated in this paper.