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Adaptive unified neural network for dynamic power quality compensation


Citation

Ghazanfarpour, Behzad and Mohd Radzi, Mohd Amran and Mariun, Norman and Shoorangiz, Reza (2013) Adaptive unified neural network for dynamic power quality compensation. In: 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO 2013), 3-4 June 2013, Langkawi, Kedah. (pp. 114-118).

Abstract

Voltage sag is a temporary voltage drop at the fundamental component of utility voltage line. Because of its nature, fast detecting and compensating of sag is very critical. In this work, adaptive neural network is proposed for detection and compensating of sag conditions. The neural network part uses Adaline structure to model the fundamental component of line voltage. Moreover, an adaptive learning rule is applied on the neural network algorithm to enhance the system speed in detecting voltage sag magnitude and phase. For compensating the fault, another controller plant is implemented that uses Levenberg-Marquardt backpropagation algorithm. This plant is trained during normal condition of voltage line and memorizes its peak magnitude. While voltage sag happens, it compares difference between the magnitudes of the normal condition to the sag situation and generates proper switching signal for the compensator. The proposed compensator in this work is series active power filter which has ability to compensate power system harmonics at the same time.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1109/PEOCO.2013.6564526
Publisher: IEEE
Keywords: Adaptive neural network; Voltage sag; Active power filter
Depositing User: Nabilah Mustapa
Date Deposited: 10 Jun 2019 02:44
Last Modified: 10 Jun 2019 02:44
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/PEOCO.2013.6564526
URI: http://psasir.upm.edu.my/id/eprint/68637
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