Transformer Active Part Fault Assessment Using Internet of Things

Nauryzbay Mussin; Aidar Suleimen; Temirlan Akhmenov; Nurzhan Amanzholov; Venera Nurmanova; Mehdi Bagheri; Mohammad S. Naderi; Oveis Abedinia

doi:10.1109/CoCoNet.2018.8476903

Transformer Active Part Fault Assessment Using Internet of Things

Nauryzbay Mussin, Aidar Suleimen, Temirlan Akhmenov, Nurzhan Amanzholov, Venera Nurmanova, Mehdi Bagheri, Mohammad S. Naderi, Oveis Abedinia

Nazarbayev University

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

3 Citations (Scopus)

Abstract

Faults in distribution and power transformers jeopardize stability of the power network. Hence, various diagnosis techniques are implemented in order to prevent or at least detect transformer integrity violations. The majority of diagnosis techniques are functioning off-line and requires transformer disconnection from the power line. This is certainly undesirable for utility management and customer. Therefore, on-line or online diagnosis is more preferable and faster than off-line monitoring procedure. The aim of this study is to implement transformer real-time diagnosis technique based on the analysis of the vibrational signal spectrum. It is supposed that vibrational signature of the transformer is transferred and processed over the cloud environment using Internet of Things (IoT), and then the prognosis algorithm is executed over portable device.

Original language	English
Title of host publication	Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	169-174
Number of pages	6
ISBN (Electronic)	9781538659281
DOIs	https://doi.org/10.1109/CoCoNet.2018.8476903
Publication status	Published - Sept 28 2018
Event	2nd International Conference on Computing and Network Communications, CoCoNet 2018 - Astana, Kazakhstan Duration: Aug 15 2018 → Aug 17 2018

Publication series

Name	Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018

Conference

Conference	2nd International Conference on Computing and Network Communications, CoCoNet 2018
Country/Territory	Kazakhstan
City	Astana
Period	8/15/18 → 8/17/18

Keywords

Cloud system
Internet of Things (IoT)
Transformer diagnosis
Vibrational signal analysis

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/CoCoNet.2018.8476903

Cite this

Mussin, N., Suleimen, A., Akhmenov, T., Amanzholov, N., Nurmanova, V., Bagheri, M., Naderi, M. S., & Abedinia, O. (2018). Transformer Active Part Fault Assessment Using Internet of Things. In Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018 (pp. 169-174). Article 8476903 (Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CoCoNet.2018.8476903

Transformer Active Part Fault Assessment Using Internet of Things. / Mussin, Nauryzbay; Suleimen, Aidar; Akhmenov, Temirlan et al.
Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 169-174 8476903 (Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018).

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

Mussin, N, Suleimen, A, Akhmenov, T, Amanzholov, N, Nurmanova, V, Bagheri, M, Naderi, MS & Abedinia, O 2018, Transformer Active Part Fault Assessment Using Internet of Things. in Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018., 8476903, Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018, Institute of Electrical and Electronics Engineers Inc., pp. 169-174, 2nd International Conference on Computing and Network Communications, CoCoNet 2018, Astana, Kazakhstan, 8/15/18. https://doi.org/10.1109/CoCoNet.2018.8476903

Mussin N, Suleimen A, Akhmenov T, Amanzholov N, Nurmanova V, Bagheri M et al. Transformer Active Part Fault Assessment Using Internet of Things. In Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 169-174. 8476903. (Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018). doi: 10.1109/CoCoNet.2018.8476903

Mussin, Nauryzbay ; Suleimen, Aidar ; Akhmenov, Temirlan et al. / Transformer Active Part Fault Assessment Using Internet of Things. Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 169-174 (Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018).

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title = "Transformer Active Part Fault Assessment Using Internet of Things",

abstract = "Faults in distribution and power transformers jeopardize stability of the power network. Hence, various diagnosis techniques are implemented in order to prevent or at least detect transformer integrity violations. The majority of diagnosis techniques are functioning off-line and requires transformer disconnection from the power line. This is certainly undesirable for utility management and customer. Therefore, on-line or online diagnosis is more preferable and faster than off-line monitoring procedure. The aim of this study is to implement transformer real-time diagnosis technique based on the analysis of the vibrational signal spectrum. It is supposed that vibrational signature of the transformer is transferred and processed over the cloud environment using Internet of Things (IoT), and then the prognosis algorithm is executed over portable device.",

keywords = "Cloud system, Internet of Things (IoT), Transformer diagnosis, Vibrational signal analysis",

author = "Nauryzbay Mussin and Aidar Suleimen and Temirlan Akhmenov and Nurzhan Amanzholov and Venera Nurmanova and Mehdi Bagheri and Naderi, {Mohammad S.} and Oveis Abedinia",

note = "Funding Information: This research work was supported by Faculty Development Competitive Research Grants, 090118FD5318, Nazarbayev University. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2nd International Conference on Computing and Network Communications, CoCoNet 2018 ; Conference date: 15-08-2018 Through 17-08-2018",

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AU - Mussin, Nauryzbay

AU - Suleimen, Aidar

AU - Akhmenov, Temirlan

AU - Amanzholov, Nurzhan

AU - Nurmanova, Venera

AU - Bagheri, Mehdi

AU - Naderi, Mohammad S.

AU - Abedinia, Oveis

PY - 2018/9/28

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N2 - Faults in distribution and power transformers jeopardize stability of the power network. Hence, various diagnosis techniques are implemented in order to prevent or at least detect transformer integrity violations. The majority of diagnosis techniques are functioning off-line and requires transformer disconnection from the power line. This is certainly undesirable for utility management and customer. Therefore, on-line or online diagnosis is more preferable and faster than off-line monitoring procedure. The aim of this study is to implement transformer real-time diagnosis technique based on the analysis of the vibrational signal spectrum. It is supposed that vibrational signature of the transformer is transferred and processed over the cloud environment using Internet of Things (IoT), and then the prognosis algorithm is executed over portable device.

AB - Faults in distribution and power transformers jeopardize stability of the power network. Hence, various diagnosis techniques are implemented in order to prevent or at least detect transformer integrity violations. The majority of diagnosis techniques are functioning off-line and requires transformer disconnection from the power line. This is certainly undesirable for utility management and customer. Therefore, on-line or online diagnosis is more preferable and faster than off-line monitoring procedure. The aim of this study is to implement transformer real-time diagnosis technique based on the analysis of the vibrational signal spectrum. It is supposed that vibrational signature of the transformer is transferred and processed over the cloud environment using Internet of Things (IoT), and then the prognosis algorithm is executed over portable device.

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Transformer Active Part Fault Assessment Using Internet of Things

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Keywords

ASJC Scopus subject areas

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