Optimal steady-state voltage control using Gaussian process learning
In this paper, an optimal steady-state voltage control framework is developed based on a novel linear Voltage-Power dependence deducted from Gaussian Process (GP) learning. Different from other point-based linearization techniques, this GP-based linear relationship is valid over a subspace of operat...
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sg-ntu-dr.10356-1507222021-08-12T01:23:08Z Optimal steady-state voltage control using Gaussian process learning Pareek, Parikshit Yu, Weng Nguyen, Hung Dinh School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Effective Voltage Control Source Gaussian Process Learning Steady-state Voltage Control In this paper, an optimal steady-state voltage control framework is developed based on a novel linear Voltage-Power dependence deducted from Gaussian Process (GP) learning. Different from other point-based linearization techniques, this GP-based linear relationship is valid over a subspace of operating points and thus suitable for a system with uncertainties such as those in power injections due to renewables. The proposed optimal voltage control algorithms, therefore, perform well over a wide range of operating conditions. Both centralized and distributed optimal control schemes are introduced in this framework. The least-squares estimation is employed to provide analytical forms of the optimal control which offer great computational benefits. Moreover, unlike many existing voltage control approaches deploying fixed voltage references, the proposed control schemes not only minimize the control efforts but also optimize the voltage reference setpoints that lead to the least voltage deviation errors with respect to such setpoints. The control algorithms are also extended to handle uncertain power injections with robust optimal solutions which guarantee compliance with the voltage regulation standards. As for the distributed control scheme, a new network partition problem is cast, based on the concept of Effective Voltage Control Source (EVCS), as an optimization problem which is further solved using convex relaxation. Various simulations on the IEEE 33-bus and 69-bus test feeders are presented to illustrate the performance of the proposed voltage control algorithms and EVCS-based network partition. Energy Market Authority (EMA) Nanyang Technological University National Research Foundation (NRF) Accepted version This work was supported in part by the Nanyang Technological University SUG, in part by the Academic Research Fund TIER 1 2019-T1-001-119 (RG 79/19), and in part by the Energy Market Authority (EMA) and National Research Foundation (NRF) Singapore under Grant EMA-EP004-EKJGC-0003. 2021-08-12T01:23:08Z 2021-08-12T01:23:08Z 2020 Journal Article Pareek, P., Yu, W. & Nguyen, H. D. (2020). Optimal steady-state voltage control using Gaussian process learning. IEEE Transactions On Industrial Informatics, 17(10), 7017-7027. https://dx.doi.org/10.1109/TII.2020.3047844 1551-3203 https://hdl.handle.net/10356/150722 10.1109/TII.2020.3047844 2-s2.0-85099107240 10 17 7017 7027 en 2019-T1-001-119 (RG 79/19) EMA-EP004-EKJGC-0003 IEEE Transactions on Industrial Informatics © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TII.2020.3047844. application/pdf |
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Engineering::Electrical and electronic engineering Effective Voltage Control Source Gaussian Process Learning Steady-state Voltage Control |
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Engineering::Electrical and electronic engineering Effective Voltage Control Source Gaussian Process Learning Steady-state Voltage Control Pareek, Parikshit Yu, Weng Nguyen, Hung Dinh Optimal steady-state voltage control using Gaussian process learning |
description |
In this paper, an optimal steady-state voltage control framework is developed based on a novel linear Voltage-Power dependence deducted from Gaussian Process (GP) learning. Different from other point-based linearization techniques, this GP-based linear relationship is valid over a subspace of operating points and thus suitable for a system with uncertainties such as those in power injections due to renewables. The proposed optimal voltage control algorithms, therefore, perform well over a wide range of operating conditions. Both centralized and distributed optimal control schemes are introduced in this framework. The least-squares estimation is employed to provide analytical forms of the optimal control which offer great computational benefits. Moreover, unlike many existing voltage control approaches deploying fixed voltage references, the proposed control schemes not only minimize the control efforts but also optimize the voltage reference setpoints that lead to the least voltage deviation errors with respect to such setpoints. The control algorithms are also extended to handle uncertain power injections with robust optimal solutions which guarantee compliance with the voltage regulation standards. As for the distributed control scheme, a new network partition problem is cast, based on the concept of Effective Voltage Control Source (EVCS), as an optimization problem which is further solved using convex relaxation. Various simulations on the IEEE 33-bus and 69-bus test feeders are presented to illustrate the performance of the proposed voltage control algorithms and EVCS-based network partition. |
author2 |
School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Pareek, Parikshit Yu, Weng Nguyen, Hung Dinh |
format |
Article |
author |
Pareek, Parikshit Yu, Weng Nguyen, Hung Dinh |
author_sort |
Pareek, Parikshit |
title |
Optimal steady-state voltage control using Gaussian process learning |
title_short |
Optimal steady-state voltage control using Gaussian process learning |
title_full |
Optimal steady-state voltage control using Gaussian process learning |
title_fullStr |
Optimal steady-state voltage control using Gaussian process learning |
title_full_unstemmed |
Optimal steady-state voltage control using Gaussian process learning |
title_sort |
optimal steady-state voltage control using gaussian process learning |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/150722 |
_version_ |
1709685341889232896 |