Optimal scheduling of LTC and shunt capacitors in large distorted distribution systems using evolutionary-based algorithms

The optimal scheduling of load tap changers (LTCs) and switched shunt capacitors for simultaneously minimizing energy loss and improving voltage profile while taking harmonics into account is performed using evolutionary-based algorithms (EAs). The proposed algorithm is capable of optimizing large d...

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Main Authors: Ulinuha, A., Masoum, M., Islam, Syed
Format: Journal Article
Published: IEEE Power Engineering Society 2008
Online Access:http://hdl.handle.net/20.500.11937/36390
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author Ulinuha, A.
Masoum, M.
Islam, Syed
author_facet Ulinuha, A.
Masoum, M.
Islam, Syed
author_sort Ulinuha, A.
building Curtin Institutional Repository
collection Online Access
description The optimal scheduling of load tap changers (LTCs) and switched shunt capacitors for simultaneously minimizing energy loss and improving voltage profile while taking harmonics into account is performed using evolutionary-based algorithms (EAs). The proposed algorithm is capable of optimizing large distribution systems with different types of nonlinear loads. The decouple approach is employed for harmonic power-flow calculations, while two EAs are developed to determine the load interval division and near-optimal schedule. The inclusion of harmonics provides the optimization benefits while maintaining the acceptable distortion levels. The scheduling is carried out for the IEEE 123-bus with 14 shunt capacitors and 12 nonlinear loads. The main contributions are inclusion of harmonics in the optimal scheduling problem and its application to large distribution systems with multiple nonlinear loads.
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format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T08:45:28Z
publishDate 2008
publisher IEEE Power Engineering Society
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spelling curtin-20.500.11937-363902017-09-13T15:56:20Z Optimal scheduling of LTC and shunt capacitors in large distorted distribution systems using evolutionary-based algorithms Ulinuha, A. Masoum, M. Islam, Syed The optimal scheduling of load tap changers (LTCs) and switched shunt capacitors for simultaneously minimizing energy loss and improving voltage profile while taking harmonics into account is performed using evolutionary-based algorithms (EAs). The proposed algorithm is capable of optimizing large distribution systems with different types of nonlinear loads. The decouple approach is employed for harmonic power-flow calculations, while two EAs are developed to determine the load interval division and near-optimal schedule. The inclusion of harmonics provides the optimization benefits while maintaining the acceptable distortion levels. The scheduling is carried out for the IEEE 123-bus with 14 shunt capacitors and 12 nonlinear loads. The main contributions are inclusion of harmonics in the optimal scheduling problem and its application to large distribution systems with multiple nonlinear loads. 2008 Journal Article http://hdl.handle.net/20.500.11937/36390 10.1109/TPWRD.2007.911166 IEEE Power Engineering Society fulltext
spellingShingle Ulinuha, A.
Masoum, M.
Islam, Syed
Optimal scheduling of LTC and shunt capacitors in large distorted distribution systems using evolutionary-based algorithms
title Optimal scheduling of LTC and shunt capacitors in large distorted distribution systems using evolutionary-based algorithms
title_full Optimal scheduling of LTC and shunt capacitors in large distorted distribution systems using evolutionary-based algorithms
title_fullStr Optimal scheduling of LTC and shunt capacitors in large distorted distribution systems using evolutionary-based algorithms
title_full_unstemmed Optimal scheduling of LTC and shunt capacitors in large distorted distribution systems using evolutionary-based algorithms
title_short Optimal scheduling of LTC and shunt capacitors in large distorted distribution systems using evolutionary-based algorithms
title_sort optimal scheduling of ltc and shunt capacitors in large distorted distribution systems using evolutionary-based algorithms
url http://hdl.handle.net/20.500.11937/36390