Performance analysis of an ant lion optimizer in tuning generators' excitation controls in multi machine power system

In this paper an ant lion optimizer algorithm is proposed for tuning excitation controls in multi machine power system. Tuning of excitation controls includes change of parameters in automatic voltage regulator (AVR) and power system stabilizer (PSS). A proposed algorithm is used as a swarm intelligence optimization method for finding a best solution for small signal stability problems. A two area - four machine model (TAFM) with two interconnected transmission lines is considered in this paper. Small signal stability disturbances observed in this paper include automatic re-closure of an interconnection line between two areas, short circuit on an interconnection line, short term load outage in first area, and a load change in first area. Performance of a proposed algorithm is compared with other swarm intelligence algorithms such as particle swarm optimization (PSO), velocity relaxed particle swarm optimization and a salp swarm algorithm (SSA). Objective functions used in performance analysis and comparison include integral of time-weighted absolute error (ITAE) and mean value of time domain transitional process quality indicators.