Estimation of Power System Stabilizer Parameters Using Swarm Intelligence Techniques to Improve Small Signal Stability of Power System

Abstract

Interconnection of the power system utilities and grids presents a formidable challenge in front of design engineers. With the interconnections, power system has emerged as a more complex and nonlinear system. In recent years, small signal stability issues have gained much importance along with the conventional transient stability issues. Transient stability of the power system can be achieved with high gain and fast acting automatic voltage regulators (AVRs). However, AVRs introduce negative damping in the system. Propagation of small signals is hazardous for system’s health and presents a potential threat to system’s oscillatory stability. These small signals have magnitude of 0.2 to 2 Hz. The efficient control methodology to enhance system damping is power system stabilizer (PSS). This paper presents application of swarm intelligence for PSS parameter estimation problem on standard IEEE 10 Generator 39 Bus power network (New England). Realization of the objective function is carried out with the help of interpolation analysis using MATLAB. The system performance is compared with the conventional optimization algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) based PSS controller. The robustness of proposed controller is tested by considering different operating conditions. An Eigen property analysis is carried out on this system i.e. before installing PSS, and after the employment of GA and PSO tuned PSSs. A meaningful comparison is carried out with GA and PSO on the basis of convergence characteristics and dynamic response of speed deviation curves of various generators.

Keywords: Power system stabilizer (PSS), low frequency oscillations, genetic algorithm (GA), particle swarm optimization (PSO), automatic voltage regulators (AVRs)