Technical gazette, Vol. 28 No. 2, 2021.
Preliminary communication
https://doi.org/10.17559/TV-20191011133311
Improvement of Power System Small-Signal Stability by Artificial Neural Network Based on Feedback Error Learning
Sedat Nazlibilek*
; School of Electrical and Electronics Engineering, Baskent University, 06810, Ankara
Issa Ali
; Department of Electric and Electronic Engineering, Higher Institute of Science and Technology, 16328, Jadu, Libya
Alyaseh Askir
; Department of Mechatronics Engineering, AL- Zawia University, 16418, Zawia, Libya
Abstract
Electrical power systems usually suffer from instabilities because of some disturbances occurring due to environmental conditions, system failures, and loading conditions. The most frequently encountered problem is the loss of synchronization between the rotor angle and the stator magnetic angle for synchronous generators. The contribution of this study is that a nonlinear adaptive control approach called feedback error learning (FEL) is utilized to improve the small-signal stabilities of an electric power system. The power system under study is composed of a synchronous machine connected to infinite bus. Many advantages of FEL control approach makes it capable to robustly adapting with all possible operating conditions rather than using optimization algorithms for tuning the conventional power system stabilizer (CPSS) that is still unsatisfactory especially at some critical operating points. The performances of two controllers, namely the proposed FEL scheme and the conventional controller CPSS, are tested by Matlab simulations. It is found that the FEL controller can be effectively used as an alternative stabilizer for improving small-signal stabilities of the power
Keywords
artificial neural network; conventional PSS; conventional power system stabilizer; feedback error learning; SMIB power system
Hrčak ID:
255883
URI
Publication date:
17.4.2021.
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