Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario

Alejandro  Verduzco; Paula  Páramo Balsa; Francisco Gonzalez-Longatt; Manuel A. Andrade; Martha Nohemi Acosta Montalvo; José Luis Rueda; Peter Palensky

doi:10.1109/ISIE51582.2022.9831751

Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario

Alejandro Verduzco, Paula Páramo Balsa, Francisco Gonzalez-Longatt, Manuel A. Andrade, Martha Nohemi Acosta Montalvo, José Luis Rueda, Peter Palensky

Intelligent Electrical Power Grids

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

This research paper presents a method that uses measurements of voltages angles, as provided by phasor measurement units (PMU), to accurately detect the sudden disconnection of a generation unit from a power grid. Results in this research paper have demonstrated, in a practical fashion, that a multi-layer perceptron (MLP) neural network (NN) can be appropriately trained to detect and identify the sudden disconnection of a generation unit in a multi-synchronous generation unit power system. Synthetic time-series bus voltage angles considering low inertia scenarios in the IEEE 39 bus system were used to train the MLP NN. The training process is speeded up by using four GPUs hardware. The simulations results have confirmed the successful detection and identification of the generator outage.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)
Publisher	IEEE
Pages	424-429
Number of pages	6
ISBN (Electronic)	978-1-6654-8240-0
ISBN (Print)	978-1-6654-8240-0
DOIs	https://doi.org/10.1109/ISIE51582.2022.9831751
Publication status	Published - 2022
Event	2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) - Anchorage, United States Duration: 1 Jun 2022 → 3 Jun 2022 Conference number: 31st

Publication series

Name	IEEE International Symposium on Industrial Electronics
Volume	2022-June

Conference

Conference	2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)
Country/Territory	United States
City	Anchorage
Period	1/06/22 → 3/06/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Artificial neural network
deep learning
machine learning
outage detection and identification
power system dynamics

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Detection_and_Identification_of_Generator_Disconnection_Using_Multi-layer_Perceptron_Neural_Network_Considering_Low_Inertia_ScenarioFinal published version, 1.11 MB

Cite this

Verduzco, A., Páramo Balsa, P., Gonzalez-Longatt, F., Andrade, M. A., Acosta Montalvo, M. N., Rueda, J. L., & Palensky, P. (2022). Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario. In Proceedings of the 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) (pp. 424-429). Article 9831751 (IEEE International Symposium on Industrial Electronics; Vol. 2022-June). IEEE. https://doi.org/10.1109/ISIE51582.2022.9831751

Verduzco, Alejandro ; Páramo Balsa, Paula ; Gonzalez-Longatt, Francisco et al. / Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario. Proceedings of the 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE). IEEE, 2022. pp. 424-429 (IEEE International Symposium on Industrial Electronics).

@inproceedings{d06f79f0dcb54bd7b652b82f376a09fd,

title = "Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario",

abstract = "This research paper presents a method that uses measurements of voltages angles, as provided by phasor measurement units (PMU), to accurately detect the sudden disconnection of a generation unit from a power grid. Results in this research paper have demonstrated, in a practical fashion, that a multi-layer perceptron (MLP) neural network (NN) can be appropriately trained to detect and identify the sudden disconnection of a generation unit in a multi-synchronous generation unit power system. Synthetic time-series bus voltage angles considering low inertia scenarios in the IEEE 39 bus system were used to train the MLP NN. The training process is speeded up by using four GPUs hardware. The simulations results have confirmed the successful detection and identification of the generator outage.",

keywords = "Artificial neural network, deep learning, machine learning, outage detection and identification, power system dynamics",

author = "Alejandro Verduzco and {P{\'a}ramo Balsa}, Paula and Francisco Gonzalez-Longatt and Andrade, {Manuel A.} and {Acosta Montalvo}, {Martha Nohemi} and Rueda, {Jos{\'e} Luis} and Peter Palensky",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) ; Conference date: 01-06-2022 Through 03-06-2022",

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language = "English",

isbn = "978-1-6654-8240-0",

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Verduzco, A, Páramo Balsa, P, Gonzalez-Longatt, F, Andrade, MA, Acosta Montalvo, MN, Rueda, JL & Palensky, P 2022, Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario. in Proceedings of the 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)., 9831751, IEEE International Symposium on Industrial Electronics, vol. 2022-June, IEEE, pp. 424-429, 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE), Anchorage, United States, 1/06/22. https://doi.org/10.1109/ISIE51582.2022.9831751

Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario. / Verduzco, Alejandro ; Páramo Balsa, Paula ; Gonzalez-Longatt, Francisco et al.
Proceedings of the 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE). IEEE, 2022. p. 424-429 9831751 (IEEE International Symposium on Industrial Electronics; Vol. 2022-June).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario

AU - Verduzco, Alejandro

AU - Páramo Balsa, Paula

AU - Gonzalez-Longatt, Francisco

AU - Andrade, Manuel A.

AU - Acosta Montalvo, Martha Nohemi

AU - Rueda, José Luis

AU - Palensky, Peter

N1 - Conference code: 31st

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N2 - This research paper presents a method that uses measurements of voltages angles, as provided by phasor measurement units (PMU), to accurately detect the sudden disconnection of a generation unit from a power grid. Results in this research paper have demonstrated, in a practical fashion, that a multi-layer perceptron (MLP) neural network (NN) can be appropriately trained to detect and identify the sudden disconnection of a generation unit in a multi-synchronous generation unit power system. Synthetic time-series bus voltage angles considering low inertia scenarios in the IEEE 39 bus system were used to train the MLP NN. The training process is speeded up by using four GPUs hardware. The simulations results have confirmed the successful detection and identification of the generator outage.

AB - This research paper presents a method that uses measurements of voltages angles, as provided by phasor measurement units (PMU), to accurately detect the sudden disconnection of a generation unit from a power grid. Results in this research paper have demonstrated, in a practical fashion, that a multi-layer perceptron (MLP) neural network (NN) can be appropriately trained to detect and identify the sudden disconnection of a generation unit in a multi-synchronous generation unit power system. Synthetic time-series bus voltage angles considering low inertia scenarios in the IEEE 39 bus system were used to train the MLP NN. The training process is speeded up by using four GPUs hardware. The simulations results have confirmed the successful detection and identification of the generator outage.

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KW - deep learning

KW - machine learning

KW - outage detection and identification

KW - power system dynamics

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BT - Proceedings of the 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)

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Verduzco A, Páramo Balsa P, Gonzalez-Longatt F, Andrade MA, Acosta Montalvo MN, Rueda JL et al. Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario. In Proceedings of the 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE). IEEE. 2022. p. 424-429. 9831751. (IEEE International Symposium on Industrial Electronics). doi: 10.1109/ISIE51582.2022.9831751

Detection and Identification of Generator Disconnection Using Multi-layer Perceptron Neural Network Considering Low Inertia Scenario

Abstract

Publication series

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Bibliographical note

Keywords

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