Control algorithm of a smart grid device for optimal radial feeder load reconfiguration
- Authors: Nicolae, Dan-Valentin , Jordaan, J. A.
- Date: 2013
- Subjects: Voltage stabilizers , Electronic power system stability , Voltage regulators
- Language: English
- Type: Conference proceedings
- Identifier: http://ujcontent.uj.ac.za8080/10210/376485 , http://hdl.handle.net/10210/18155 , uj:15966 , ISBN: 978-1-4673-5769-2 , Citation: Nicolae, D.V. & Jordaan, J.A. 2013. Control algorithm of a smart grid device for optimal radial feeder load reconfiguration. Proceedings of the 9th Asian Control Conference, 23-26 June, 2013, Istanbul, Turkey. DOI: 10.1109/ASCC.2013.6606240
- Description: Abstract: Secondary distribution network, generally speaking, performs as well as the performance of its LV feeders. The main problem a feeder is experiencing is the load unbalancing due to the stochastic nature of its individual single-phase loads: bigger losses in certain phase accompanied with bed voltage regulation and voltage unbalance. The aim of this paper is to address the issue of automatic balancing as progressing from the end of the feeder towards the front using smart device based on three-ways switch selector and artificial intelligence algorithm to minimize the neutral current.
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Novel heuristic and SVM based optimization algorithm for improving distribution feeder performance
- Authors: Jordaan, J. A. , Nicolae, Dan-Valentin , Jimoh, A. A.
- Date: 2012
- Subjects: Transmission , Electric transformer
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/17526 , uj:15895 , Citation: Jordaan, J.A., Nicolae, D.V. & Jimoh, A.A. 2012. Novel heuristic and SVM based optimization algorithm for improving distribution feeder performance. In: The 2012 International Joint Conference on Neural Networks. DOI;10.1109/IJCNN.2012.6252616-5. , DOI;10.1109/IJCNN.2012.6252616
- Description: Abstract: Secondary distribution networks generally perform as well as its LV feeders are performing. The main problem that a feeder is experiencing would be the load unbalancing due to the stochastic nature of its individual single-phase loads: larger losses in certain phases accompanied by bad voltage regulation and voltage unbalance. In order to address this problem, it may be economical to install apparatus to automatically balance or partially balance the loads progressing from the end of the feeder towards the front using smart devices based on a three-ways switch selector and an artificial intelligence algorithm to minimize the neutral current. The main idea behind this paper is therefore to keep the three phases progressively balanced along the whole length of the line. A Support Vector Machines (SVM) implementation and a heuristic method are presented as the numerical algorithms
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The use of support vector machine for phase balancing in the distribution feeder
- Authors: Siti, M. W. , Jimoh, A. A. , Jordaan, J. A. , Nicolae, Dan-Valentin
- Date: 2007
- Subjects: Electric power transmission , Distribution channels
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/21374 , uj:16146 , ISSN: 0302-9743 , Citation: Siti, M.W. et al. 2007. The use of support vector machine for phase balancing in the distribution feeder. ICONIP 2007, Neural Information Processing Lecture Notes in Computer Science, 4985:721-729 , DOI:10.1007/978354069162475
- Description: Abstract: Phase voltage and current unbalances in power system distribution networks are major factors leading to extra losses, communication interference, equipment overloading, and malfunctioning of the protective relay, which consequently results in service quality and operation efficiency being reduced. As a better alternative to the traditional practices of manual trial and error, and the contemporary solution technique of network reconfiguration or load rearrangement, this paper investigates and proposes a novel method that is based on the use of the historical data and artificial intelligence for eliminating or minimizing phase unbalance problems. The proposed method is based on support vector machine
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