Particle swarm intelligence tuned fuzzy controller for damping modal oscillations of power system

In this paper a particle swarm intelligent optimization based optimal fuzzy scheme has been developed to design intelligent adaptive controllers for improving the dynamic and transient stability performance of power systems. This concept is applied to power system stabilizer (PSS) connected to a nine bus three machine power system. The rules of the Takagi-Sugeno (TS) fuzzy scheme are derived from the speed error and their derivatives. To implement this proposed scheme the coefficients in the TS-fuzzy rules needs to be optimized. The optimization of this coefficients as well as the coefficient for auxiliary signal generation is performed through particle swarm intelligent algorithm. The performance of the proposed controller is analysed in multimachine power systems subjected to various dynamic and transient disturbances. The proposed particle swarm intelligent -neuro-fuzzy control scheme exhibits a superior damping performance in comparison to the existing controllers. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation.

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