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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  3/2012 - 15
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 HIGH-IMPACT PAPER 

Nonlinear Adaptive NeuroFuzzy Wavelet Based Damping Control Paradigm for SSSC

BADAR, R. See more information about BADAR, R. on SCOPUS See more information about BADAR, R. on IEEExplore See more information about BADAR, R. on Web of Science, KHAN, L. See more information about KHAN, L. on SCOPUS See more information about KHAN, L. on SCOPUS See more information about KHAN, L. on Web of Science
 
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Download PDF pdficon (810 KB) | Citation | Downloads: 726 | Views: 3,875

Author keywords
SSSC, SMIB power system, power system stability, adaptive neurofuzzy control, wavelet neural network

References keywords
power(15), series(12), fuzzy(11), control(9), wavelet(8), controller(8), panda(7), neural(7), damping(7), compensator(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-08-31
Volume 12, Issue 3, Year 2012, On page(s): 97 - 104
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.03015
Web of Science Accession Number: 000308290500015
SCOPUS ID: 84865851562

Abstract
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Static Synchronous Series Compensator (SSSC) is a series compensating Flexible AC Transmission System (FACTS) controller with primary objective of power flow control on a line by injecting a voltage in series with transmission line. However, it can efficiently be used for improving the system stability by using a supplementary damping control system. In this work, Adaptive Neurofuzzy Wavelet Control (ANFWC) paradigm for SSSC supplementary damping control system has been proposed and successfully applied to a Single Machine Infinite Bus (SMIB) power system. Gradient descent based back propagation algorithm, being simple with sufficient efficiency, has been used to update the controller parameters. The robustness of the proposed control strategy has been validated using nonlinear time domain simulations for different faults and various operating conditions of power system. Finally, the results have been compared with Conventional Adaptive Takagi-Sugino Controller (CATC) on the basis of different performance indices.


References | Cited By  «-- Click to see who has cited this paper

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[2] M. El-Mousri, A. M. Sharaf and K. El-Arroudi, "Optimal control schemes for SSSC for dynamic series compensation," Elect. Power Syst. Research, vol. 78, no. 4, pp. 646-656, April. 2008.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 34]


[3] W. Qiao and R. G. Harley, "Indirect adaptive external neurocontrol for a series capacitive reactance compensator based on a voltage source PWM converter in damping power oscillations," IEEE Trans. Industrial Electronics, vol. 54, no. 1, pp. 77-85, Feb. 2007.
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[CrossRef] [SCOPUS Times Cited 3]


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[CrossRef] [SCOPUS Times Cited 7]


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[14] V. Topalov, G. L. Cascella, V. Giordano, F. Cupertino, and O. Kaynak, "Sliding mode neuro-adaptive control of electrical drives," IEEE Trans. Ind. Electron., vol. 54, no. 1, pp. 671-679, Feb. 2007.
[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 70]


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[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 34]


[16] M. J. Er and Y. Gao, "Robust adaptive control of robot manipulators using generalized fuzzy neural networks," IEEE Trans. Ind. Electron., vol. 50, no. 3, pp. 620-628, Jun. 2003.
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[18] R. H. Abiyev and O. Kaynak, "Fuzzy wavelet neural networks for identification and control of dynamic plants- A novel structure and a comprehensive study," IEEE Trans. Indus. Elect., vol.55, no.8, pp. 3133-3140, 2008.
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[CrossRef]


[22] Q.J. Guo, H.-B. Yu, and A.-D. Xu, "Wavelet fuzzy network for fault diagnosis," in Proc. Int. Conf. Commun. Circuits Syst., 2005, pp. 993-998.
[CrossRef]


[23] Y. Lin and F.-Y. Wang, "Predicting chaotic time-series using adaptive wavelet-fuzzy inference system," in Proc. IEEE Intell. Veh. Symp., 2005, pp. 888-893.
[CrossRef] [SCOPUS Times Cited 13]


[24] D. W. C. Ho, P.-A. Zhang, and J. Xu, "Fuzzy wavelet networks for function learning," IEEE Trans. Fuzzy Syst., vol. 9, no. 1, pp. 200-211, Feb. 2001.
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[25] R. H. Abiyev, "Controller based of fuzzy wavelet neural network for control of technological processes," in Proc. IEEE Int. CISMA, Giardini Naxos, Italy, 2005, pp. 215-219.
[CrossRef] [SCOPUS Times Cited 23]


[26] R. H. Abiyev, "Time series prediction using fuzzy wavelet neural network model," in Lecture Notes in Computer Sciences, vol. 4132. Berlin, Germany: Springer-Verlag, 2006, pp. 191-200.
[CrossRef] [SCOPUS Times Cited 15]


[27] A. Kazemi, A. Badri and S. Jadid, "Investigation of two vector control based methods for static synchronous series compensator," IJEEE, vol. 1, no. 4, pp. 1-6, 2005.

[28] J. W. Park, R. G. Harley and G. K. Venayagamoorthy, "Power system optimization and coordination of damping controls by series FACTS devices," in Inaugural IEEE PES Conference and Exhibition, Durban, South Africa, July 11-15, 2005, pp. 293-298.
[CrossRef]


[29] M. Torii and M. T. Hagan, "Stability of steepest descent with momentum for quadratic functions", IEEE Trans. Neural Nets., vol. 13, no. 3, pp. 752-756, May 2002.
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[30] S. Panda, "Robust coordinated design of excitation and STATCOM-based controller using genetic algorithm", Int. Jr. Innov. Comp. and Appl., vol. 1, no. 4, pp. 244-251, 2008.
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[31] E. G. Romera, M. A. Jaramillo and D. C. Fernandez, "Monthly electric energy demand forecasting with neural networks and Fourier series", Energy Conv. Mang., vol. 49, pp. 3135-3142, 2008.
[CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 92]


References Weight

Web of Science® Citations for all references: 980 TCR
SCOPUS® Citations for all references: 1,324 TCR

Web of Science® Average Citations per reference: 32 ACR
SCOPUS® Average Citations per reference: 43 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-09-17 15:37 in 156 seconds.




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