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Abstract. The paper considers simultaneous placement and tuning of power system stabilizers for stabilization of power systems over a wide range of operating conditions using genetic algorithm. The power system operating at various conditions is considered as a finite set of plants. The problem of setting parameters of power system stabilizers is converted as a simple optimization problem that is solved by a genetic algorithm and an eigenvalue-based objective function. A single machine -infinite bus system and a multi-machine system are considered to test the suggested technique. The optimum placement and tuning of parameters of PSSs are done simultaneously. A PSS tuned using this procedure is robust at different operating conditions and structure changes of the system.

Abstract. Tuning of power system stabilizers (PSS) over a wide range of operating conditions and load models is investigated using an artificial neural network (ANN). The neural nettwork is specially trained by an input-output set prepared by a novel approach based on genetic algorithms (GA). To enhance power system damping, it is desirable to adapt the PSS parameters in real-time based on generator operating conditions and load models. To do this, on-line measurements of generator loading conditions are chosen as the input signals to the neural network. The output of the neural network is the desired gain of the PSS that ensures the stabilization of the system for a wide range of load models connected to the power system. For training the neural network a set of operating conditions is chosen as the input. The desired output for any input is computed by simultaneous stabilization of the system over a wide range of load models using genetic algorithm. In this regard, the power system operating at a specified operating condition and various load models is treated as a finite set of plants. The problem of selecting the output parameters for every operating point which simultaneously stabilize this set of plants is converted to a simple optimization problem which is solved by a genetic algorithm and an eigenvalue-based objective function. The proposed method is applied to a test system and the validity is demonstrated through digital simulation.