An Auto-Tuning Fuzzy Logic PSS Design under Multi-operating Conditions Using Real-Coded Genetic Algorithm

The power system dynamic instability is occurred by loosing balance relation between electrical generation and a varying load demand that justifies the necessity of using Power System Stabilizer (PSS). Moreover, the PSS must have the capability of producing appropriate stabilizing signals with limited practical amplitude and it should be robust against a wide range of operating conditions and disturbances. For this purpose, a robust PSS design based on an auto-tuning fuzzy logic control under multi-operating conditions by using Real Coded Genetic Algorithm (RCGA) is proposed. This method includes two fuzzy controllers; internal fuzzy controller and supervisor fuzzy controller. The supervisor controller tunes the internal one by on-line applying of nonlinear scaling factors to inputs and outputs using extra signals. The RCGA-based method is used for off-line training of this supervisor controller by considering that the operating conditions for training be different from those are used for test simulations. Then, the proposed PSS is tested in three operational conditions; nominal load, heavy load, and in the case of fault occurrence in transmission line. The simulation results are provided to compare the proposed PSS with conventional fuzzy PSS and conventional PSS. It is shown that the performance and robustness of proposed PSS in different operating conditions is more acceptable.

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