ACEEE Int. J. on Control System and Instrumentation, Vol. 02, No. 02, June 2011 © 2011 ACEEE DOI: 01.IJCSI.02.02. 42 Hybrid Particle Swarm Optimization for Multi-objective Reactive Power Optimization with Voltage Stability Enhancement P.Aruna Jeyanthy1, and Dr.D.Devaraj 2 1 N.I.C.E ,Kumarakoil/EEE Department,Kanyakumari,India Email: arunadarwin@yahoo.com 2 Kalasingam University/EEE Department, Srivillipithur,India Email: deva230@yahoo.com Abstract —This paper presents a new hybrid particle swarm optimization (HPSO) method for solving multi-objective real power optimization problem. The objectives of the optimization problem are to minimize the losses and to maximize the voltage stability margin. The proposed method expands the original GA and PSO to tackle the mixed –integer non- linear optimization problem and achieves the voltage stability enhancement with continuous and discrete control variables such as generator terminal voltages, tap position of transformers and reactive power sources. A comparison is made with conventional, GA and PSO methods for the real power losses and this method is found to be effective than other methods. It is evaluated on the IEEE 30 and 57 bus test system, and the simulation results show the effectiveness of this approach for improving voltage stability of the system. Keywords: Hybrid Particle Swarm Optimization (HPSO), real power loss, reactive power dispatch (RPD), Voltage stability constrained reactive power dispatch (VSCRPD). 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 optimal values of generator bus voltage magnitudes, transformer tap setting and the output of reactive power sources so as to minimize the transmission loss. 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 [1]. 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 [2] is used as the indicator of voltage stability enhancement. The modal analysis technique provides voltage stability critical areas and gives information about the best corrective/preventive actions for improving system stability margins. It is done by evaluating the Jacobian matrix, the critical eigen values/vector [3].The least singular value of converged power flow jacobian is used an objective for the voltage stability enhancement. It is a non- linear optimization problem and various mathematical techniques have been adopted to solve this optimal reactive power dispatch problem. These include the gradient method [4, 5], Newton method [6] and linear programming [7].The 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 optimization problem [8-15]. Genetic algorithm is a stochastic search technique based on the mechanics of natural selection [16].In GA-based RPD problem it starts with the randomly generated population of points, improves the fitness as generation proceeds through the application of the three operators-selection, crossover and mutation. But in the recent research some deficiencies are identified in the GA performance. This degradation in efficiency is apparent in applications with highly epistatic objective functions i.e. where the parameters being optimized are highly correlated. In addition, the premature convergence of GA degrades its performance and reduces its search capability. In addition to this, these algorithms are found to take more time to reach the optimal solution. Particle swarm optimization (PSO) is one of the stochastic search techniques developed by Kennedy and Eberhart [17]. This technique can generate high quality solutions within shorter calculation time and stable convergence characteristics than other stochastic methods. But the main problem of PSO is poor local searching ability and cannot effectively solve the complex non-linear equations needed to be accurate. Several methods to improve the performance of PSO algorithm have been proposed and some of them have been applied to the reactive power and voltage control problem in recent years [18-20]. Here a few modifications are made in the original PSO by including the mutation operator from the real coded GA. Thus the proposed algorithm identifies the optimal values of generation bus voltage magnitudes, transformer tap setting and the output of the reactive power sources so as to minimize the transmission loss and to improve the voltage stability. The effectiveness of the proposed approach is demonstrated through IEEE-30and IEEE-57 bus system. 12