Title :
Self-learning adaptive-network-based fuzzy logic power system stabilizer
Author :
Hariri, A. ; Malik, O.P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Calgary Univ., Alta., Canada
Abstract :
An adaptive network-based fuzzy logic power system stabilizer (ANF PSS) with self-learning capability is presented in this paper. This method combines the advantages of artificial neural networks (ANNs) and fuzzy logic control schemes to design a new PSS, without resorting to another existing controller. In this approach, two ANFs are employed, one functions as power plant model, the other as controller. The error signal at the output of the plant is backpropagated through different stages in time to train the controller. The improvement of the dynamic performance of the power system is demonstrated by simulation studies for different operating conditions and disturbances
Keywords :
adaptive control; backpropagation; control system analysis; control system synthesis; fuzzy control; fuzzy neural nets; power system control; power system stability; self-adjusting systems; artificial neural networks; backpropagation; control design; control simulation; disturbances; dynamic performance; error signal; fuzzy logic control; fuzzy neural net; operating conditions; power plant model; power system stabilizer; self-learning system; Artificial neural networks; Automatic control; Control systems; Error correction; Fuzzy logic; Power system dynamics; Power system modeling; Power system simulation; Power system stability; Power systems;
Conference_Titel :
Intelligent Systems Applications to Power Systems, 1996. Proceedings, ISAP '96., International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-3115-X
DOI :
10.1109/ISAP.1996.501088