Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network

This paper introduces a framework for optimal placement (OP) of phasor measurement units (PMUs) using metaheuristic algorithms in a distribution network. The voltage magnitude and phase angle obtained from PMUs were selected as the input variables for supervised learning-based pseudo-measurement mod...

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Autores principales:	Kyung-Yong Lee, Jung-Sung Park, Yun-Su Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	metaheuristic algorithms optimal placement phasor measurement units (PMU) pseudo-measurement state estimation Technology T
Acceso en línea:	https://doaj.org/article/d503995f602b46328dc7598c4a9b1731
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spelling	oai:doaj.org-article:d503995f602b46328dc7598c4a9b17312021-11-25T17:28:36ZOptimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network10.3390/en142277671996-1073https://doaj.org/article/d503995f602b46328dc7598c4a9b17312021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7767https://doaj.org/toc/1996-1073This paper introduces a framework for optimal placement (OP) of phasor measurement units (PMUs) using metaheuristic algorithms in a distribution network. The voltage magnitude and phase angle obtained from PMUs were selected as the input variables for supervised learning-based pseudo-measurement modeling that outputs the voltage magnitude and phase angle of the unmeasured buses. For three, four, and five PMU installations, the metaheuristic algorithms explored 2000 combinations, corresponding to 40.32%, 5.56%, and 0.99% of all placement combinations in the 33-bus system and 3.99%, 0.25%, and 0.02% in the 69-bus system, respectively. Two metaheuristic algorithms, a genetic algorithm and particle swarm optimization, were applied; the results of the techniques were compared to random search and brute-force algorithms. Subsequently, the effects of pseudo-measurements based on optimal PMU placement were verified by state estimation. The state estimation results were compared among the pseudo-measurements generated by the optimal PMU placement, worst PMU placement, and load profile (LP). State estimation results based on OP were superior to those of LP-based pseudo-measurements. However, when pseudo-measurements based on the worst placement were used as state variables, the results were inferior to those obtained using the LP.Kyung-Yong LeeJung-Sung ParkYun-Su KimMDPI AGarticlemetaheuristic algorithmsoptimal placementphasor measurement units (PMU)pseudo-measurementstate estimationTechnologyTENEnergies, Vol 14, Iss 7767, p 7767 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	metaheuristic algorithms optimal placement phasor measurement units (PMU) pseudo-measurement state estimation Technology T
spellingShingle	metaheuristic algorithms optimal placement phasor measurement units (PMU) pseudo-measurement state estimation Technology T Kyung-Yong Lee Jung-Sung Park Yun-Su Kim Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network
description	This paper introduces a framework for optimal placement (OP) of phasor measurement units (PMUs) using metaheuristic algorithms in a distribution network. The voltage magnitude and phase angle obtained from PMUs were selected as the input variables for supervised learning-based pseudo-measurement modeling that outputs the voltage magnitude and phase angle of the unmeasured buses. For three, four, and five PMU installations, the metaheuristic algorithms explored 2000 combinations, corresponding to 40.32%, 5.56%, and 0.99% of all placement combinations in the 33-bus system and 3.99%, 0.25%, and 0.02% in the 69-bus system, respectively. Two metaheuristic algorithms, a genetic algorithm and particle swarm optimization, were applied; the results of the techniques were compared to random search and brute-force algorithms. Subsequently, the effects of pseudo-measurements based on optimal PMU placement were verified by state estimation. The state estimation results were compared among the pseudo-measurements generated by the optimal PMU placement, worst PMU placement, and load profile (LP). State estimation results based on OP were superior to those of LP-based pseudo-measurements. However, when pseudo-measurements based on the worst placement were used as state variables, the results were inferior to those obtained using the LP.
format	article
author	Kyung-Yong Lee Jung-Sung Park Yun-Su Kim
author_facet	Kyung-Yong Lee Jung-Sung Park Yun-Su Kim
author_sort	Kyung-Yong Lee
title	Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network
title_short	Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network
title_full	Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network
title_fullStr	Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network
title_full_unstemmed	Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network
title_sort	optimal placement of pmu to enhance supervised learning-based pseudo-measurement modelling accuracy in distribution network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d503995f602b46328dc7598c4a9b1731
work_keys_str_mv	AT kyungyonglee optimalplacementofpmutoenhancesupervisedlearningbasedpseudomeasurementmodellingaccuracyindistributionnetwork AT jungsungpark optimalplacementofpmutoenhancesupervisedlearningbasedpseudomeasurementmodellingaccuracyindistributionnetwork AT yunsukim optimalplacementofpmutoenhancesupervisedlearningbasedpseudomeasurementmodellingaccuracyindistributionnetwork
_version_	1718412314588217344

Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network

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