NDLI: A hybrid PSO technique for damping electro-mechanical oscillations in large power system

Content Provider	IEEE Xplore Digital Library
Author	Eslami, M. Shareef, H. Mohamed, A. Khajehzadeh, M.
Copyright Year	2010
Description	Author affiliation: Dept. of Electrical, Electronic & Systems Engineering, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia (Eslami, M.; Shareef, H.; Mohamed, A.) \|\| Dept. of Civil & Structural Engineering, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia (Khajehzadeh, M.)
Abstract	In this paper, a hybrid optimization technique is presented to solve the well-known problem of tuning power system stabilizers' (PSSs) parameters. The hybrid technique is derived from particle swarm optimization (PSO) by adding the passive congregation model. The tuning of the PSS parameters is formulated as the multi-objective function with constraints including the damping ratio and damping factor. Maximizations of the damping factor and the damping ratio of power system modes are taken as the goals or two objective functions, when designing the PSS parameters. The New England 16-unit 68-bus standard power system, under various system configurations and operation conditions, is employed to illustrate the performance of the proposed method. The results are very encouraging and suggest that the proposed PSO with passive Congregation (PSOPC) algorithm is very efficient in damping low frequency oscillations and improving the stability of power system.
Starting Page	442
Ending Page	447
File Size	933751
Page Count	6
File Format	PDF
ISBN	9781424486472
e-ISBN	9781424486489
DOI	10.1109/SCORED.2010.5704050
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-13
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Damping Passive Congregation PSS Design Power system dynamics Power system stability Generators Multi-objective optimization Oscillators Optimization Particle Swarm Optimization
Content Type	Text
Resource Type	Article

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