Adaptive power system stabilizer using ANFIS and genetic algorithms

Authors:
Jesús Fraile-Ardanuy;Pedro J. Zufiria
Affiliations:
Grupo de Redes Neuronales, ETSI Telecomunicación, UPM, Spain;Grupo de Redes Neuronales, ETSI Telecomunicación, UPM, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

This paper presents an adaptive Power System Stabilizer (PSS) using an Adaptive Network Based Fuzzy Inference System (ANFIS) and Genetic Algorithms (GAs). Firstly, genetic algorithms are used to tune a conventional PSS on a wide range of operating conditions and then, the relationship between these operating points and the PSS parameters is learned by the ANFIS. The ANFIS optimally selectes the classical PSS parameters based on machine loading conditions. The proposed stabilizer has been tested by performing nonlinear simulations using a synchronous machine-infinite bus model. The results show the robustness and the capability of the stabilizer to enhance system damping over a wide range of operating conditions and system parameter variations.