A hybrid neuro-fuzzy power system stabilizer for multimachine power systems

A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper to improve power system dynamic stability. The proposed FBFN based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is train...

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Bibliographic Details
Published in:IEEE transactions on power systems 1998-11, Vol.13 (4), p.1323-1330
Main Authors: Abido, M.A., Abdel-Magid, Y.L.
Format: Article
Language:English
Subjects:
Adaptive control
Applied sciences
Automata. Logic controller
Computer science
control theory
systems
Control theory. Systems
Disturbances. Regulation. Protection
Electric, optical and optoelectronic circuits
Electrical engineering. Electrical power engineering
Electrical power engineering
Electronics
Exact sciences and technology
Fuzzy logic
Fuzzy systems
Hybrid power systems
Minerals
Neural networks
Petroleum
Power networks and lines
Power system dynamics
Power system modeling
Power system stability
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Summary:A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper to improve power system dynamic stability. The proposed FBFN based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is trained over a wide range of operating conditions in order to re-tune the PSS parameters in real-time based on machine loading conditions. The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious FBFN model. Time domain simulations of a single machine infinite bus system and a multimachine power system subject to major disturbances are investigated. The performance of the proposed FBFN PSS is compared with that of conventional (CPSS). The results show the capability of the proposed FBFN PSS to enhance the system damping of local modes of oscillations over a wide range of operating conditions. The decentralized nature of the proposed FBFN PSS makes it easy to install and tune.
ISSN:0885-8950
1558-0679
DOI:10.1109/59.736272