Robust PSS tuning through multiobjective optimization

Abstract

This paper presents a technique for PSS design which is based on multiobjective optimization. The technique has the ability to tune multiple PSS controllers by simultaneously enhancing the damping based performance index and robustness index specifi ed via the IIHlloo norm of the system. To illustrate the proposed design technique two case studies are presented, including a two-area benchmark system and a fifty-machine test system. To allow the proposed tools to be applied for PSS control design, the authors developed a MATLAB interface to the Micro GA optimization solver.

Decentralized Synergetic Control of Power Systems

“…The method is tested successfully on a SMIB-FACT system and on a two area four machines system with the SVC included in the middle of the lines connecting the two areas together. The results, when compared with the previous works [47,16]…”

“…The results obtained are simulated and compared to each other. Comparison is also made with previous study when PSSs are installed at generators 2 and 3 [16]. In summary, simultaneous tuning of decentralized synergetic controllers and SVC supplementary controller using PSO algorithm is proposed in this chapter.…”

“…The advantages have the merit of simultaneously tuning the parameters through the framework of multi-objective optimization problem. Genetic Algorithms (GA) have been employed in [15,16], Tabu Search (TS) optimization technique in [17], and Particle Swarm Optimization (PSO) in [18]. Also in recent years, Reinforcement Learning (RL) technique is utilized for on-line tuning of gains of conventional PSS [19,20].…”

“…By considering the stability of these two conditions, the capability of the proposed controller to withstand a loss of a line after three phase fault at bus 3 is appraised. The results obtained are compared with fixed parameter synergetic controller tuned by particle swarm optimization and fixed gained linear power system stabilizer (PSS) tuned by genetic algorithm [47,16].…”

Power system damping controllers design using a backstepping control technique

“…Second technique, Genetic Algorithm (GA), the gains are obtained from survey on tuning PSS gains [36]. The parameters for the excitation system are given in Appendix D. In Figure 4.9 extended-backstepping control with damping factor γ = 150 (solid line) damp the rotor…”