Various control techniques using Advanced Super-conducting Magnetic Energy Storage (ASMES) aimed at improving power system stability have been proposed. As fuzzy controller has proved its value in some applications, the number of investigations employing fuzzy controller with ASMES has been greatly increased over recent period. Nevertheless, it is sometimes difficult to specify the rule base for some plants, or the need can arise for tuning the rule-base parameters if the plant changes. In order to solve such problems the Fuzzy Model Reference Learning Controller (FMRLC) is proposed. This paper investigates multi-inputs/multi-outputs FMRLC for time-variant nonlinear system. This provides the motivation for adaptive fuzzy control, whereby the focus is placed on the automatic on-line synthesis and tuning of fuzzy controller parameters (i.e., the use of on-line data for continuous learning of the fuzzy controller which ensures that the performance objectives are met). The simulation results show that the proposed robust controller is able to work with nonlinear power system (i.e., single machine connected at infinite bus), under various fault conditions and significant disturbances

High performance excitation systems have become very important as limited generation capacity and consumer needs for power continue to increase. This work aims to develop a robust control technique based on Hoo loop-shaping optimization method, applied to a Power System Stabilizer (PSS) to improve transient and dynamic stabilities of a turbo-alternator connected to an infinite bus system. In this paper, a robust controller is designed and simulated under MatIab-Simulink. The robust power system stabilizer (RPSS) is designed using the concept of Hoo loop shaping design which is one of the robust control methods used for designing the controllers for dynamical systems in electrical engineering. Guidance for loop shaping and synthesis of the robust controller are also presented along with the selection of the weighting functions. Comparisons are also made between the Conventional Russian power system stabilizer with a strong action (CPSS) and (RPSS). The simulation results show the effectiveness of the proposed method for a stabilizer using the concept of Roo control, by improving the performances and the robustness.

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This paper present the realization and development of a graphical user interface (GUI) to studied the stability and robustness of power systems (analysis and synthesis), using Conventional Power System Stabilizers (CPSS - realized on PID scheme) or advanced controllers (based on adaptive and robust control), and applied on automatic excitation control of powerful synchronous generators, to improve dynamic performances and robustness. The GUI is a useful average to facilitate stability study of power system with the analysis and synthesis of regulators, and resolution of the compromise: results precision / calculation speed. The obtained Simulation results exploiting our developed GUI realized under MATLAB shown considerable improvements in static and dynamic performances, a great stability and enhancing the robustness of power system, with best precision and minimum operating time. This study was performed for different types of powerful synchronous generators.

This paper proposes the Meta-heuristics approaches using genetic algorithms (GA) and particle swarm optimization (PSO) for tuning power system stabilizer PSS parameters. In this work we have proposed a multi-objective function based on two objectives: first maximize the stability margin by increasing the damping factors and second minimize the eigenvalues real parts. For the effectiveness function proposed check, we compared it with mono-objective function. The simulation results, by comparative study between genetic algorithms and particle swarm optimizations techniques via multi objective and mono objective functions proved the efficiency of the PSS adapted by multi-objective function based genetic algorithms in comparison with particle swarm optimization, it’s enhanced stability of power system works under different operating modes and different network configurations. The simulation results obtained under developed graphical user interface (GUI).