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A. Y. Hatata
M. Eladawy
K. Shebl



Author(s) and WSEAS

A. Y. Hatata
M. Eladawy
K. Shebl


WSEAS Transactions on Power Systems


Print ISSN: 1790-5060
E-ISSN: 2224-350X

Volume 13, 2018

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.



Parameter Control Scheme for Active Power Filter Based on NARX Neural Network

AUTHORS: A. Y. Hatata, M. Eladawy, K. Shebl

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ABSTRACT: This paper presents a shunt active power filter control reference signal using Nonlinear Autoregressive with eXogenous neural network (NARX) with back propagation training algorithm. The instantaneous reactive power algorithm is integrated within the neural network to extract the dominant harmonics. The proposed method is demonstrated on three phase thyristor controlled drive which is one of widely used loads in petroleum industry field

KEYWORDS: NARX Neural Network; Nonlinear Loads; Shunt Active Power Filter; Instantaneous Reactive Power Algorithm

REFERENCES:

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[3] B. Singh, K. Al Haddad and A. Chandra, A review of active filters for power quality improvement, Industrial Electronics, IEEE Transactions on, Vol. 46, pp. 960-971, 1999.

[4] A. Emadi, A. Nasiri, and S. B. Bekiarov, Uninterruptible Power Supplies and Active Filters, Boca Raton, FL: CRC Press, ISBN: 0 8493-3035-1, Oct. 2004.

[5] H. Akagi, Trends in active power line conditioners, IEEE Industrial Electronics, Control, Instrumentation, and Automation, Vol. 1, 1992, pp. 1924.

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[8] H. Akagi, Active and hybrid filters for power conditioning, IEEE Conference on Industrial Electronics, Vol. 1, 2000, pp. TU26TU36.

[9] P. Salmeron and J. R. Vazquez, Practical design of a three phase active power line conditioner controlled by artificial neural networks, IEEE Transactions on Power Delivery, Vol. 20, 2, pp.1037-1044, April 2005.

[10] P. Cheng, S. Bhattacharya and D. Divan, Experimental verification of dominant harmonic active filter for high power applications, IEEE Transactions on Industry Applications, Vol. 36, pp.567-577, March/April 2000.

[11] J. Mazumdar, System and method for determining harmonic contributions from nonlinear loads in power systems, Georgia Institute of Technology Dec. 2006.

[12] M. U. Hashmi, V. Arora, J. G. Priolkar, Hourly electric load forecasting using Nonlinear AutoRegressive with eXogenous (NARX) based neural network for the state of Goa, India, Industrial Instrumentation and Control (ICIC), 2015 International Conference on Year: 2015, Pp. 1418 - 1423, IEEE Conference Publications.

[13] Y. Liu, X. Wang, Y. Liu, Asynchronous harmonic analysis based on out-of-sequence measurement for large scale residential power network, Instrumentation and Measurement Technology Conference (I2MTC), 2015, pp. 1693 1698, IEEE Conference Publications

[14] H. T. Siegelmann, and E. D. Sontag, Turing Computability with Neural Nets, Applied Mathematics Letters, Vol. 4, 1991, pp. 77-80.

[15] M. T. Hagan, H. B. Demuth, M. H. Beale, Neural Network Design, Jan 2002

[16] M. H. Beale, M. T. Hagan, H. B. Demuth, Neural Network Toolbox 7 User s Guide.

WSEAS Transactions on Power Systems, ISSN / E-ISSN: 1790-5060 / 2224-350X, Volume 13, 2018, Art. #12, pp. 118-124


Copyright © 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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