Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data

Widespread deployments of optimally placed real-time power quality (PQ) monitoring tools such as distribution level micro-phasor measurement units (D-PMUs or <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>PMU), digital fault recorders...

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Main Authors: Manoj Prabhakar Anguswamy, Manoj Datta, Lasantha Meegahapola, Arash Vahidnia
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10292636/
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author Manoj Prabhakar Anguswamy
Manoj Datta
Lasantha Meegahapola
Arash Vahidnia
author_facet Manoj Prabhakar Anguswamy
Manoj Datta
Lasantha Meegahapola
Arash Vahidnia
author_sort Manoj Prabhakar Anguswamy
collection DOAJ
description Widespread deployments of optimally placed real-time power quality (PQ) monitoring tools such as distribution level micro-phasor measurement units (D-PMUs or <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>PMU), digital fault recorders, and PQ analyzers are expected to play a critical role in improving the stability and reliability of the smart grid. In this paper, an improved PQ disturbance (PQD) classification method using discrete wavelet transform (DWT) with a cubic multi-class support vector machine (CMSVM) classifier is proposed, which incorporates a decade&#x2019;s worth of high-quality continuous waveform PQ data from the Australian power network. This research also introduces misclassification cost (MC) and cost-sensitive classification theory into the area of PQD classifiers to build improved and more robust network models for the future. The method is evaluated using four case studies of synthetic and real-world PQD field data combinations and five application case studies using optimally placed <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>PMUs. The results indicate similar classification performance for standard PQDs than previous literature, alongside improved MC for complex PQD classes. Comparative analysis with previous literature highlights the importance of using high-quality real PQD field data to improve the fidelity of classifiers to provide better PQ insights as more complex components are added to the distribution network.
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spelling doaj.art-d98c44a2bd304a97bbaeb3710f9db77e2023-10-31T23:00:32ZengIEEEIEEE Access2169-35362023-01-011111873711876110.1109/ACCESS.2023.332695010292636Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field DataManoj Prabhakar Anguswamy0https://orcid.org/0000-0003-0697-6795Manoj Datta1https://orcid.org/0000-0001-7515-7166Lasantha Meegahapola2https://orcid.org/0000-0003-4471-8471Arash Vahidnia3https://orcid.org/0000-0002-2443-2321School of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC, AustraliaSchool of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC, AustraliaSchool of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC, AustraliaSchool of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC, AustraliaWidespread deployments of optimally placed real-time power quality (PQ) monitoring tools such as distribution level micro-phasor measurement units (D-PMUs or <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>PMU), digital fault recorders, and PQ analyzers are expected to play a critical role in improving the stability and reliability of the smart grid. In this paper, an improved PQ disturbance (PQD) classification method using discrete wavelet transform (DWT) with a cubic multi-class support vector machine (CMSVM) classifier is proposed, which incorporates a decade&#x2019;s worth of high-quality continuous waveform PQ data from the Australian power network. This research also introduces misclassification cost (MC) and cost-sensitive classification theory into the area of PQD classifiers to build improved and more robust network models for the future. The method is evaluated using four case studies of synthetic and real-world PQD field data combinations and five application case studies using optimally placed <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>PMUs. The results indicate similar classification performance for standard PQDs than previous literature, alongside improved MC for complex PQD classes. Comparative analysis with previous literature highlights the importance of using high-quality real PQD field data to improve the fidelity of classifiers to provide better PQ insights as more complex components are added to the distribution network.https://ieeexplore.ieee.org/document/10292636/Distribution networkmicro-PMUoptimal placementpower quality insights
spellingShingle Manoj Prabhakar Anguswamy
Manoj Datta
Lasantha Meegahapola
Arash Vahidnia
Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data
IEEE Access
Distribution network
micro-PMU
optimal placement
power quality insights
title Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data
title_full Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data
title_fullStr Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data
title_full_unstemmed Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data
title_short Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data
title_sort distribution network power quality insights with optimally placed micro pmus incorporating synthetic and real field data
topic Distribution network
micro-PMU
optimal placement
power quality insights
url https://ieeexplore.ieee.org/document/10292636/
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AT manojdatta distributionnetworkpowerqualityinsightswithoptimallyplacedmicropmusincorporatingsyntheticandrealfielddata
AT lasanthameegahapola distributionnetworkpowerqualityinsightswithoptimallyplacedmicropmusincorporatingsyntheticandrealfielddata
AT arashvahidnia distributionnetworkpowerqualityinsightswithoptimallyplacedmicropmusincorporatingsyntheticandrealfielddata