Robust Supplementary Controllers for AVR and PSS

Abstract

The paper presents a step-by-step design methodology of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¿</sub> optimization methods based Automatic Voltage Regulator (AVR) and Power System Stabilizer (PSS).This paper demonstrates their performance in a single-machine-infinite-bus power system through digital simulation. The ANFIS design employs a zero and 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 and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¿</sub> optimization methods based AVR and PSS in damping oscillation is then compared with conventional AVR and PSS performance. It is found that the damping characteristics of both ANFIS and optimization based H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¿</sub> AVR and PSS are better than the conventional AVR and PSS. The effectiveness of the proposed ANFIS-based AVR and PSS in small-signal stability is thus established. Index Temts- AVR PSS, Fuzzy Logic, Adaptive NeuroFuzzy Inference System, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¿</sub> optimization, SugenoFuzzy Model, Hybrid Learning Algorithm.