Title: Integrating FACTS controllers in unbalanced distribution systems to restore the balanced operation using ANN

Authors: Mohamed I. Mosaad; Fawzan Salem; Mohamed Alsumiri

Addresses: Higher Technological Institute (HTI), 10th of Ramadan City – Industrial area 2, near the small industries complex, Egypt; Yanbu Industrial College, P.O. Box: 30436, Yanbu Al-Sinaiya 21477, Saudi Arabia ' Power Electronics and Energy Conversion Department, Electronics Research Institute (ERI), Elbehous Street, Dokki, Cairo, Egypt; Yanbu Industrial College, P.O. Box: 30436, Yanbu Al-Sinaiya 21477, Saudi Arabia ' Yanbu Industrial College, P.O. Box: 30436, Yanbu Al-Sinaiya 21477, Saudi Arabia

Abstract: Artificial neural network (ANN) is applied in this work for integrating flexible AC transmission systems (FACTS) in the distribution system to overcome the unbalanced operation problems. Fixed capacitor-thyristor controlled reactor (FC-TCR) as one of FACTS devices is introduced in this paper to cancel the negative and zero sequence line currents caused by the unbalanced load to restore balanced operation. Three-phase unbalanced load is utilised and FACTS controller in the form of FC-TCR type is integrated to the load in the form of three-phase variable susceptances whose values are adjusted by the thyristor firing angles connected in parallel with the unbalanced load. Many test cases are applied to the system by changing the values of the load admittances, then the corresponding firing angles for each phase of the FC-TCR are obtained to restore the system balanced operation. The three-phase load admittances and the corresponding thyristors firing angles of FC-TCR device are set to be the input and output signals to the ANN respectively, in order to train the network. In order to test the capability of the proposed scheme, two test cases are used in order to restore the balanced operation in each case.

Keywords: flexible AC transmission systems; FACTS; fixed capacitor-thyristor controlled reactor; FC-TCR; unbalanced loads; artificial neural network; ANN.

DOI: 10.1504/IJIED.2017.087612

International Journal of Industrial Electronics and Drives, 2017 Vol.3 No.4, pp.219 - 228

Received: 22 Mar 2017
Accepted: 30 Apr 2017

Published online: 23 Oct 2017 *

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