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Power System Stability Enhancement with Teaching-Learning-Based Optimization

Modern electric power systems are complex, interconnected and susceptible to low frequency oscillations. Power system stabilizers (PSSs) are used in synchronous generators to damp out these oscillations and prevent instability in the case of different possible disturbances. This paper proposes application of an optimal approach for two different controller types to improve PSS performance and enhance power system stability. Three types of controllers including Lead-Lag, fuzzy, and sliding mode are considered for PSS of a single machine infinite bus power system. The parameters of fuzzy and sliding mode controllers are optimized using teaching-learning-based optimization (TLBO) algorithm to minimize oscillations. Simulation results show that TLBO algorithm is capable of proper tuning of different PSS controller strategies to not only maintain stability, but also to reduce oscillations. In addition, performance of different controllers is evaluated for the same specific disturbance. Significant improvement is observed in fuzzy logic controller. In addition, it has been found that SMC optimized by TLBO exhibit best results compared to other controllers.