Forecasting voltage harmonic distortion in residential distribution networks using smart meter data

Pablo  Rodrıguez-Pajaron; araceli Hernandez-Bayo; Jovica V. Milanovic

doi:10.1016/j.ijepes.2021.107653

Forecasting voltage harmonic distortion in residential distribution networks using smart meter data

Pablo Rodrıguez-Pajaron, araceli Hernandez-Bayo, Jovica V. Milanovic

EEE - Academic & Research

Universidad Politecnica de Madrid

Research output: Contribution to journal › Article › peer-review

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Abstract

This paper introduces a methodology to forecast voltage total harmonic distortion (THD) at low voltage busbars
of residential distribution feeders based on the data provided by a limited number of smart meters. The meth-
odology provides relevant power quality indices to system operators using only the existing monitoring infra-
structure required for demand response operation. Different algorithms for voltage THD forecasting are
implemented, including artificial neural networks, and their performance is tested and compared. The necessary
coverage of smart meters for the acceptable accuracy of the estimated THD is also established. The estimation
algorithms are validated considering probabilistic demand load model developed based on typical harmonic
injections of household devices obtained from measurements and using a typical European low voltage test-
feeder with 471 residential consumers.

Original language	English
Article number	107653
Number of pages	12
Journal	International Journal of Electrical Power & Energy Systems
Volume	136
Early online date	7 Oct 2021
DOIs	https://doi.org/10.1016/j.ijepes.2021.107653
Publication status	Published - 1 Mar 2022

Keywords

distribution network
neural network
power quality
smart meter
voltage distortion

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Forecasting voltage harmonic distortion in residential distribution networks using smart meter dataFinal published version, 2.46 MBLicence: CC BY-NC-ND

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title = "Forecasting voltage harmonic distortion in residential distribution networks using smart meter data",

abstract = "This paper introduces a methodology to forecast voltage total harmonic distortion (THD) at low voltage busbarsof residential distribution feeders based on the data provided by a limited number of smart meters. The meth-odology provides relevant power quality indices to system operators using only the existing monitoring infra-structure required for demand response operation. Different algorithms for voltage THD forecasting areimplemented, including artificial neural networks, and their performance is tested and compared. The necessarycoverage of smart meters for the acceptable accuracy of the estimated THD is also established. The estimationalgorithms are validated considering probabilistic demand load model developed based on typical harmonicinjections of household devices obtained from measurements and using a typical European low voltage test-feeder with 471 residential consumers.",

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AU - Rodrıguez-Pajaron, Pablo

AU - Hernandez-Bayo, araceli

AU - Milanovic, Jovica V.

PY - 2022/3/1

Y1 - 2022/3/1

N2 - This paper introduces a methodology to forecast voltage total harmonic distortion (THD) at low voltage busbarsof residential distribution feeders based on the data provided by a limited number of smart meters. The meth-odology provides relevant power quality indices to system operators using only the existing monitoring infra-structure required for demand response operation. Different algorithms for voltage THD forecasting areimplemented, including artificial neural networks, and their performance is tested and compared. The necessarycoverage of smart meters for the acceptable accuracy of the estimated THD is also established. The estimationalgorithms are validated considering probabilistic demand load model developed based on typical harmonicinjections of household devices obtained from measurements and using a typical European low voltage test-feeder with 471 residential consumers.

AB - This paper introduces a methodology to forecast voltage total harmonic distortion (THD) at low voltage busbarsof residential distribution feeders based on the data provided by a limited number of smart meters. The meth-odology provides relevant power quality indices to system operators using only the existing monitoring infra-structure required for demand response operation. Different algorithms for voltage THD forecasting areimplemented, including artificial neural networks, and their performance is tested and compared. The necessarycoverage of smart meters for the acceptable accuracy of the estimated THD is also established. The estimationalgorithms are validated considering probabilistic demand load model developed based on typical harmonicinjections of household devices obtained from measurements and using a typical European low voltage test-feeder with 471 residential consumers.

KW - distribution network

KW - neural network

KW - power quality

KW - smart meter

KW - voltage distortion

U2 - 10.1016/j.ijepes.2021.107653

DO - 10.1016/j.ijepes.2021.107653

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VL - 136

JO - International Journal of Electrical Power & Energy Systems

JF - International Journal of Electrical Power & Energy Systems

M1 - 107653

ER -

Forecasting voltage harmonic distortion in residential distribution networks using smart meter data

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Keywords

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