The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.

A method is proposed to fit parameters of Power System Stabilizer controllers in electromechanical multimachine power systems. The use of the Non-dominated Sorting Genetic Algorithm II heuristic method and Tabu search is considered to be initial search criteria. These methods give an approximation of the values that define the controllers. Then, the stochastic approach was used to evaluate the behavior of the parameters found when considering the system’s response to the presence of random and self-sustained in-time disturbances that affect the response of the system under steady state. The stochastic approach allows the evaluation of the system’s response through the calculation of the cost of energy loss under steady state. The method is applied to two systems: a three-machine nine-busbar system, and the Interconnected System of the Greater North (Sistema Interconectado del Norte Grande) in Chile. For these systems, the proposed methodology effectively optimized the controllers and Tabu search was shown to have a better performance than the Non-dominated Sorting Genetic Algorithm II.