Power System Stabilizer Design Using Particle Swarm Optimization

Abstract

Dynamic stability has challenged power system engineers since over three decades now. In the generator, the electromechanical coupling between the rotor and rest of the system causes ossillatory behaviour around the equilibrium state, following any disturbance. The use of fast acting high gain AVRs and evolution of large interconnected power systems with transfer of bulk power across weak transmission links have further aggravated the problem of low frequency oscillations. The oscillations, which are typically in the frequency range of 0.2 to 0.3 hertz, might be excited by the disturbances in the system or, in some cases, might even build up spontaneously. These oscillations limit the power transmission capability of a network and, sometimes, even cause a loss of synchronism and an eventual breakdown of the entire system. The application of power system stabilizer can help in damping out these oscillations and improve the system stability. The traditional and till date the most popular solution to this problem is application of conventional power system stabilizer (CPSS). However, continual changes in the operating condition and network parameters result in corresponding change in system dynamics. This constantly changing nature of power system makes the design of CPSS a difficult task. In this thesis work Particle Swarm Optimization algorithm has been used for tuning the parameters of a fixed gain power system stabilizer. The stabilizer places the troublesome system modes in an acceptable region in the complex plane and guarantees a robust performance over a wide range of operating conditions. Conventional lead/lag structure is retained but its parameters are retuned using Particle swarm optimization algorithm to obtain enhanced performance. Unlike GA and other heuristic algorithms, PSO has the flexibility to control the balance between the global and local exploration of the search space. This unique feature of PSO overcomes the premature convergence problem and enhances the search capability.