Damping Local and Inter-Area Oscillations with Adaptive Neuro-Fuzzy Power System Stabilizer

In this paper, the authors report on the design, simulation and validation of an adaptive neuro-fuzzy inference system (ANFIS) based power system stabilizer (PSS) for a single-machine-infinite-bus (SMIB) and a multi-machine power system and investigate its performance in damping low frequency local and inter-area oscillations. The design employs a first order Sugeno fuzzy model, whose parameters are tuned off-line through hybrid learning algorithm. This algorithm is a combination of least square estimator and error backpropagation method. The performance of the ANFIS-based PSS is observed through digital simulation for both SMIB and multi-machine systems. Finally the results are compared with conventional fuzzy PSS performances. It is observed that ANFIS-based PSS is playing more satisfactory role in damping local and inter-area oscillations, which proves its effectiveness in small-signal stability.