This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a 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. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. The Simulated Annealing (SA) algorithm is a probabilistic hill-climbing technique that is based on the annealing/cooling process of metals. In total, most moves may be accepted at initial stages, but at the final stage only improving ones are likely to be allowed. This can help the procedure jump out of a local minimum. However, sometimes it is better to move back to a former solution that was significantly better rather than always moving from the current state. This process is called “restarting” of SA & called as Restarted Simulated Annealing (RSA). In this paper we proposed a hybridized restarted simulated annealing particle swarm optimization (RSAPSO) technique to find global minima more efficiently and robustly. The proposed RSAPSO combines the global search ability of PSO and the local search ability of RSA, and offsets the weaknesses of each other. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other reported algorithms.
The Optimal Control Based on Particle Swarm Intelligence Algorithm
