Study and Analysis of Power System Stabiliser Performance Using Artificial Neural Network

Muawia A. Magzoub; Nordin B. Saad; Rosdiazli B. Ibrahim

doi:10.4028/www.scientific.net/AMM.575.605

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 575Study and Analysis of Power System Stabiliser...

Study and Analysis of Power System Stabiliser Performance Using Artificial Neural Network

Abstract:

This paper represents a study and analysis of designing a PSS (Power system stabilizer) to be used with SMIB (a Single Machine and Infinite Bus) system that was developed using ANN (Artificial Neural Network). Furthermore, the dynamic performance of ANN based stabilizer is established and compared with conventional types of PSS. The proposed scheme’s effectiveness was tested through simulation in order to analyse the stability features of the small signal of the system regarding the operating situation of the steady state when a transmission line is lost. The focus of the primary method was on how the control performed. This was later confirmed to possess the level of a reaching time that was shorter and a spike that was lower.

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Periodical:

Applied Mechanics and Materials (Volume 575)

Pages:

605-609

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.575.605

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Online since:

June 2014

Authors:

Muawia A. Magzoub*, Nordin B. Saad, Rosdiazli B. Ibrahim

Keywords:

Artificial Neural Network (ANN), Infinite Bus, Power System Stabiliser, Single Machine

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* - Corresponding Author

References

[1] L. H. Hassan, M. Moghavvemi, and H. a. F. Mohamed: Power system stabilization based on artificial intelligent techniques; A review (Int. Conf. Tech. Postgraduates, p.1–6, 2009).

DOI: 10.1109/techpos.2009.5412107

Google Scholar

[2] F. Demello and C. Concordia: Concepts of Synchronous Machine Stability as Affected by Excitation Control (IEEE Trans. Power Appar. Syst., vol. PAS-88, no. 4, p.316–329, 1969).

DOI: 10.1109/tpas.1969.292452

Google Scholar

[3] S. I. Safie, S. Majid, A. R. Hasimah, A. Wahab, and M. H. Yusri: Sliding mode control power system stabiliser (PSS) for single machine connected to infinite bus (PECon 08, p.122–126, 2008).

DOI: 10.1109/pecon.2008.4762447

Google Scholar

[4] A. M. Parimi, I. Elamvazuthi, and N. Saad: Improvment of power system stbility with fuzzy logic based IPFC (Int. Rev. Electr. Eng., vol. 5, no. 3, p.1103–1109, Nov. 2010).

Google Scholar

[5] A. M. Parimi, I. Elamvazuthi, and N. Saad: Fuzzy logic control for IPFC for damping low frequency oscillations (2010 Int. Conf. Intell. Adv. Syst., p.1–5, Jun. 2010).

DOI: 10.1109/icias.2010.5716139

Google Scholar

[6] G. K. Venayagamoorthy and S. Member: Optimal Control Parameters for a UPFC in a Multimachine Using PSO (p.488–493, 2005).

Google Scholar

[7] J. Ma, K. L. Lo, and H. J. Wang: Clarification on power system stabiliser design (IET Gener. Transm. Distrib., vol. 7, no. 9, p.973–981, Sep. 2013).

DOI: 10.1049/iet-gtd.2012.0219

Google Scholar

[8] W. Peres, E. J. Oliveira, J. A. P. Filho, D. N. Arcanjo, I. C. S. Junior, and L. W. Oliveira: Power System Stabilizers Tuning using Bio-Inspired Algorithm (PowerTech, p.1–5, 2013).

DOI: 10.1109/ptc.2013.6652355

Google Scholar

[9] D.W. Patterson: Artificial Neural Networks Theory and Applications (Prentice Hall, 1996).

Google Scholar