Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components

Haresh Kumar; Muhammad Shafiq; Ghulam Amjad Hussain; Kimmo Kauhaniemi

doi:10.1109/ISGTEurope52324.2021.9639923

Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components

Haresh Kumar
, Muhammad Shafiq
, Ghulam Amjad Hussain
, Kimmo Kauhaniemi

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Partial discharge (PD) diagnosis is an effective tool to track the condition of electrical insulation in the medium voltage (MV) power components. Machine Learning Algorithms (MLAs) promote automated diagnosis solutions for large scale and reliable maintenance strategy. This paper aims to investigate the performance of two MLAs: Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for the classification of different types of PD sources. Suitable features are extracted by applying statistical parameters on the coefficients of discrete wavelet transform (DWT) for observing the performance of both MLAs. The performance of the algorithms is evaluated using key performance indicators (KPIs); accuracy, prediction speed and training time. Besides KPIs, a confusion matrix is presented to highlight the accurately classified and misclassified PD signals for the SVM algorithm. Comparative study of both algorithms demonstrates that SVM provides better results as compared to the KNN algorithm. The proposed solution can be valuable for the development of automated classification.

Original language	English
Title of host publication	Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe
Subtitle of host publication	Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665448758
DOIs	https://doi.org/10.1109/ISGTEurope52324.2021.9639923
State	Published - 2021
Event	11th IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe 2021 - Espoo, Finland Duration: 18 Oct 2021 → 21 Oct 2021

Publication series

Name	Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021

Conference

Conference	11th IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe 2021
Country/Territory	Finland
City	Espoo
Period	18/10/21 → 21/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

classification
electrical insulation
features extraction
key performance indicators
machine learning algorithms
partial discharge

Access to Document

10.1109/ISGTEurope52324.2021.9639923

https://ieeexplore.ieee.org/abstract/document/9639923

Cite this

Kumar, H., Shafiq, M., Hussain, G. A., & Kauhaniemi, K. (2021). Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components. In Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021 (Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISGTEurope52324.2021.9639923

Kumar, Haresh ; Shafiq, Muhammad ; Hussain, Ghulam Amjad et al. / Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components. Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021).

@inproceedings{ce37a4a898a4479b9fe3dc3e6cddffd4,

title = "Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components",

abstract = "Partial discharge (PD) diagnosis is an effective tool to track the condition of electrical insulation in the medium voltage (MV) power components. Machine Learning Algorithms (MLAs) promote automated diagnosis solutions for large scale and reliable maintenance strategy. This paper aims to investigate the performance of two MLAs: Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for the classification of different types of PD sources. Suitable features are extracted by applying statistical parameters on the coefficients of discrete wavelet transform (DWT) for observing the performance of both MLAs. The performance of the algorithms is evaluated using key performance indicators (KPIs); accuracy, prediction speed and training time. Besides KPIs, a confusion matrix is presented to highlight the accurately classified and misclassified PD signals for the SVM algorithm. Comparative study of both algorithms demonstrates that SVM provides better results as compared to the KNN algorithm. The proposed solution can be valuable for the development of automated classification.",

keywords = "classification, electrical insulation, features extraction, key performance indicators, machine learning algorithms, partial discharge",

author = "Haresh Kumar and Muhammad Shafiq and Hussain, \{Ghulam Amjad\} and Kimmo Kauhaniemi",

note = "Funding Information: This work was carried out first with the support of the Nissi Foundation scholarship, and later it was supported by the Fortum grant. The second author acknowledges the Estonian Research Council's grant under project No. PSG632. Publisher Copyright: {\textcopyright} 2021 IEEE.; 11th IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe 2021 ; Conference date: 18-10-2021 Through 21-10-2021",

year = "2021",

doi = "10.1109/ISGTEurope52324.2021.9639923",

language = "English",

series = "Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe",

address = "United States",

}

Kumar, H, Shafiq, M, Hussain, GA & Kauhaniemi, K 2021, Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components. in Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021. Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021, Institute of Electrical and Electronics Engineers Inc., 11th IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe 2021, Espoo, Finland, 18/10/21. https://doi.org/10.1109/ISGTEurope52324.2021.9639923

Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components. / Kumar, Haresh; Shafiq, Muhammad; Hussain, Ghulam Amjad et al.
Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components

AU - Kumar, Haresh

AU - Shafiq, Muhammad

AU - Hussain, Ghulam Amjad

AU - Kauhaniemi, Kimmo

N1 - Funding Information: This work was carried out first with the support of the Nissi Foundation scholarship, and later it was supported by the Fortum grant. The second author acknowledges the Estonian Research Council's grant under project No. PSG632. Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - Partial discharge (PD) diagnosis is an effective tool to track the condition of electrical insulation in the medium voltage (MV) power components. Machine Learning Algorithms (MLAs) promote automated diagnosis solutions for large scale and reliable maintenance strategy. This paper aims to investigate the performance of two MLAs: Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for the classification of different types of PD sources. Suitable features are extracted by applying statistical parameters on the coefficients of discrete wavelet transform (DWT) for observing the performance of both MLAs. The performance of the algorithms is evaluated using key performance indicators (KPIs); accuracy, prediction speed and training time. Besides KPIs, a confusion matrix is presented to highlight the accurately classified and misclassified PD signals for the SVM algorithm. Comparative study of both algorithms demonstrates that SVM provides better results as compared to the KNN algorithm. The proposed solution can be valuable for the development of automated classification.

AB - Partial discharge (PD) diagnosis is an effective tool to track the condition of electrical insulation in the medium voltage (MV) power components. Machine Learning Algorithms (MLAs) promote automated diagnosis solutions for large scale and reliable maintenance strategy. This paper aims to investigate the performance of two MLAs: Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for the classification of different types of PD sources. Suitable features are extracted by applying statistical parameters on the coefficients of discrete wavelet transform (DWT) for observing the performance of both MLAs. The performance of the algorithms is evaluated using key performance indicators (KPIs); accuracy, prediction speed and training time. Besides KPIs, a confusion matrix is presented to highlight the accurately classified and misclassified PD signals for the SVM algorithm. Comparative study of both algorithms demonstrates that SVM provides better results as compared to the KNN algorithm. The proposed solution can be valuable for the development of automated classification.

KW - classification

KW - electrical insulation

KW - features extraction

KW - key performance indicators

KW - machine learning algorithms

KW - partial discharge

UR - https://www.scopus.com/pages/publications/85123908491

U2 - 10.1109/ISGTEurope52324.2021.9639923

DO - 10.1109/ISGTEurope52324.2021.9639923

M3 - Conference contribution

T3 - Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021

BT - Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe 2021

Y2 - 18 October 2021 through 21 October 2021

ER -

Kumar H, Shafiq M, Hussain GA, Kauhaniemi K. Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components. In Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of 2021 IEEE PES Innovative Smart Grid Technologies Europe: Smart Grids: Toward a Carbon-Free Future, ISGT Europe 2021). doi: 10.1109/ISGTEurope52324.2021.9639923

Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components

Abstract

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UN SDGs

Keywords

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