A Data-Driven Approach for Grid Synchronization Based on Deep Learning

Mohammadreza Miranbeigi; Prasad Kandula; Deepak Divan

doi:10.1109/ECCE47101.2021.9595781

A Data-Driven Approach for Grid Synchronization Based on Deep Learning

Mohammadreza Miranbeigi, Prasad Kandula, Deepak Divan

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

3 Scopus citations

Abstract

Synchronization is a complex problem, mainly due to its nonlinearity and stochastic nature of the grid. The Phase-Locked Loop (PLL) has been the standard scheme for the synchronization and P/Q decoupling in grid-following inverters. Nonetheless, during transients, the PLL response is not ideal and causes oscillations or overshoots. Moreover, in adverse grid conditions, the PLL performance degrades significantly and loss of synchronism might occur. This paper introduces a structurally new scheme based on deep neural networks for synchronization, called DeepSynch. The method is capable of extracting the voltage phase fast and in a stable manner, even in a harmonic-polluted environment. The simulation results verify the performance of the proposed scheme.

Original language	English
Title of host publication	2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2985-2991
Number of pages	7
ISBN (Electronic)	9781728151359
DOIs	https://doi.org/10.1109/ECCE47101.2021.9595781
State	Published - 2021
Externally published	Yes
Event	13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Virtual, Online, Canada Duration: Oct 10 2021 → Oct 14 2021

Publication series

Name	2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings

Conference

Conference	13th IEEE Energy Conversion Congress and Exposition, ECCE 2021
Country/Territory	Canada
City	Virtual, Online
Period	10/10/21 → 10/14/21

Funding

This work is supported by ARPA-E under DE-AR0000899 and the Center for Distributed Energy (CDE), Georgia Institute of Technology.

Keywords

Artificial intelligence
DeepSynch
Phase locked-loop
deep learning
smart PLL

Access to Document

10.1109/ECCE47101.2021.9595781

Cite this

Miranbeigi, M., Kandula, P., & Divan, D. (2021). A Data-Driven Approach for Grid Synchronization Based on Deep Learning. In 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings (pp. 2985-2991). (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECCE47101.2021.9595781

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title = "A Data-Driven Approach for Grid Synchronization Based on Deep Learning",

abstract = "Synchronization is a complex problem, mainly due to its nonlinearity and stochastic nature of the grid. The Phase-Locked Loop (PLL) has been the standard scheme for the synchronization and P/Q decoupling in grid-following inverters. Nonetheless, during transients, the PLL response is not ideal and causes oscillations or overshoots. Moreover, in adverse grid conditions, the PLL performance degrades significantly and loss of synchronism might occur. This paper introduces a structurally new scheme based on deep neural networks for synchronization, called DeepSynch. The method is capable of extracting the voltage phase fast and in a stable manner, even in a harmonic-polluted environment. The simulation results verify the performance of the proposed scheme.",

keywords = "Artificial intelligence, DeepSynch, Phase locked-loop, deep learning, smart PLL",

author = "Mohammadreza Miranbeigi and Prasad Kandula and Deepak Divan",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 ; Conference date: 10-10-2021 Through 14-10-2021",

year = "2021",

doi = "10.1109/ECCE47101.2021.9595781",

language = "English",

series = "2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings",

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Miranbeigi, M, Kandula, P & Divan, D 2021, A Data-Driven Approach for Grid Synchronization Based on Deep Learning. in 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 2985-2991, 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021, Virtual, Online, Canada, 10/10/21. https://doi.org/10.1109/ECCE47101.2021.9595781

A Data-Driven Approach for Grid Synchronization Based on Deep Learning. / Miranbeigi, Mohammadreza; Kandula, Prasad; Divan, Deepak.
2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2985-2991 (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings).

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

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N2 - Synchronization is a complex problem, mainly due to its nonlinearity and stochastic nature of the grid. The Phase-Locked Loop (PLL) has been the standard scheme for the synchronization and P/Q decoupling in grid-following inverters. Nonetheless, during transients, the PLL response is not ideal and causes oscillations or overshoots. Moreover, in adverse grid conditions, the PLL performance degrades significantly and loss of synchronism might occur. This paper introduces a structurally new scheme based on deep neural networks for synchronization, called DeepSynch. The method is capable of extracting the voltage phase fast and in a stable manner, even in a harmonic-polluted environment. The simulation results verify the performance of the proposed scheme.

AB - Synchronization is a complex problem, mainly due to its nonlinearity and stochastic nature of the grid. The Phase-Locked Loop (PLL) has been the standard scheme for the synchronization and P/Q decoupling in grid-following inverters. Nonetheless, during transients, the PLL response is not ideal and causes oscillations or overshoots. Moreover, in adverse grid conditions, the PLL performance degrades significantly and loss of synchronism might occur. This paper introduces a structurally new scheme based on deep neural networks for synchronization, called DeepSynch. The method is capable of extracting the voltage phase fast and in a stable manner, even in a harmonic-polluted environment. The simulation results verify the performance of the proposed scheme.

KW - Artificial intelligence

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KW - Phase locked-loop

KW - deep learning

KW - smart PLL

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Miranbeigi M, Kandula P, Divan D. A Data-Driven Approach for Grid Synchronization Based on Deep Learning. In 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2985-2991. (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings). doi: 10.1109/ECCE47101.2021.9595781

A Data-Driven Approach for Grid Synchronization Based on Deep Learning

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