Three-Phase Feeder Load Balancing Based Optimized Neural Network Using Smart Meters

The electricity distribution system is the coupling point between the utility and the end-user. Typically, these systems have unbalanced feeders due to the variety of customers’ behaviors. Some significant problems occur; the unbalanced loads increase the operational cost and system investment. In r...

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Autores principales:	Lina Alhmoud, Qosai Nawafleh, Waled Merrji
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence feed-forward back-propagation load balancing radial basis function Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/234f92765c4a4fa3b939e8bcfc852598
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spelling	oai:doaj.org-article:234f92765c4a4fa3b939e8bcfc8525982021-11-25T19:07:30ZThree-Phase Feeder Load Balancing Based Optimized Neural Network Using Smart Meters10.3390/sym131121952073-8994https://doaj.org/article/234f92765c4a4fa3b939e8bcfc8525982021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2195https://doaj.org/toc/2073-8994The electricity distribution system is the coupling point between the utility and the end-user. Typically, these systems have unbalanced feeders due to the variety of customers’ behaviors. Some significant problems occur; the unbalanced loads increase the operational cost and system investment. In radial distribution systems, swapping loads between the three phases is the most effective method for phase balancing. It is performed manually and subjected to load flow equations, capacity, and voltage constraints. Recently, due to smart grids and automated networks, dynamic phase balancing received more attention, thus swapping the loads between the three phases automatically when unbalance exceeds permissible limits by using a remote-controlled phase switch selector/controller. Automatic feeder reconfiguration and phase balancing eliminates the service interruption, enhances energy restoration, and minimize losses. In this paper, a case study from the Irbid district electricity company (IDECO) is presented. Optimal reconfiguration of phase balancing using three techniques: feed-forward back-propagation neural network (FFBPNN), radial basis function neural network (RBFNN), and a hybrid are proposed to control the switching sequence for each connected load. The comparison shows that the hybrid technique yields the best performance. This work is simulated using MATLAB and C programming language.Lina AlhmoudQosai NawaflehWaled MerrjiMDPI AGarticleartificial intelligencefeed-forward back-propagationload balancingradial basis functionMathematicsQA1-939ENSymmetry, Vol 13, Iss 2195, p 2195 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence feed-forward back-propagation load balancing radial basis function Mathematics QA1-939
spellingShingle	artificial intelligence feed-forward back-propagation load balancing radial basis function Mathematics QA1-939 Lina Alhmoud Qosai Nawafleh Waled Merrji Three-Phase Feeder Load Balancing Based Optimized Neural Network Using Smart Meters
description	The electricity distribution system is the coupling point between the utility and the end-user. Typically, these systems have unbalanced feeders due to the variety of customers’ behaviors. Some significant problems occur; the unbalanced loads increase the operational cost and system investment. In radial distribution systems, swapping loads between the three phases is the most effective method for phase balancing. It is performed manually and subjected to load flow equations, capacity, and voltage constraints. Recently, due to smart grids and automated networks, dynamic phase balancing received more attention, thus swapping the loads between the three phases automatically when unbalance exceeds permissible limits by using a remote-controlled phase switch selector/controller. Automatic feeder reconfiguration and phase balancing eliminates the service interruption, enhances energy restoration, and minimize losses. In this paper, a case study from the Irbid district electricity company (IDECO) is presented. Optimal reconfiguration of phase balancing using three techniques: feed-forward back-propagation neural network (FFBPNN), radial basis function neural network (RBFNN), and a hybrid are proposed to control the switching sequence for each connected load. The comparison shows that the hybrid technique yields the best performance. This work is simulated using MATLAB and C programming language.
format	article
author	Lina Alhmoud Qosai Nawafleh Waled Merrji
author_facet	Lina Alhmoud Qosai Nawafleh Waled Merrji
author_sort	Lina Alhmoud
title	Three-Phase Feeder Load Balancing Based Optimized Neural Network Using Smart Meters
title_short	Three-Phase Feeder Load Balancing Based Optimized Neural Network Using Smart Meters
title_full	Three-Phase Feeder Load Balancing Based Optimized Neural Network Using Smart Meters
title_fullStr	Three-Phase Feeder Load Balancing Based Optimized Neural Network Using Smart Meters
title_full_unstemmed	Three-Phase Feeder Load Balancing Based Optimized Neural Network Using Smart Meters
title_sort	three-phase feeder load balancing based optimized neural network using smart meters
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/234f92765c4a4fa3b939e8bcfc852598
work_keys_str_mv	AT linaalhmoud threephasefeederloadbalancingbasedoptimizedneuralnetworkusingsmartmeters AT qosainawafleh threephasefeederloadbalancingbasedoptimizedneuralnetworkusingsmartmeters AT waledmerrji threephasefeederloadbalancingbasedoptimizedneuralnetworkusingsmartmeters
_version_	1718410265707413504

Three-Phase Feeder Load Balancing Based Optimized Neural Network Using Smart Meters

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