Title :
A fuzzy logic based power system stabilizer with learning ability
Author :
Hariri, A. ; Malik, O.P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Calgary Univ., Alta., Canada
fDate :
12/1/1996 12:00:00 AM
Abstract :
A fuzzy logic-based power system stabilizer (PSS) with learning ability is proposed in this paper. The proposed PSS employs a multilayer adaptive network. The network is trained directly from the input and the output of the generating unit. The algorithm combines the advantages of artificial neural networks (ANNs) and fuzzy logic control (FLC) schemes. Studies show that the proposed adaptive network-based fuzzy logic PSS (ANF PSS) can provide good damping of power systems over a wide range of operating conditions and improve the dynamic performance of the power system
Keywords :
adaptive control; control system analysis; control system synthesis; damping; fuzzy control; fuzzy neural nets; learning (artificial intelligence); multilayer perceptrons; neurocontrollers; power system control; power system stability; adaptive network-based fuzzy logic PSS; algorithm; artificial neural networks; control design; control simulation; dynamic performance; fuzzy logic control; learning ability; multilayer adaptive network; power system damping; power system stabilizer; training data; Adaptive systems; Artificial neural networks; Control systems; Fuzzy control; Fuzzy logic; Mathematical model; Neural networks; Power system dynamics; Power system modeling; Power systems;
Journal_Title :
Energy Conversion, IEEE Transactions on
