Forecasting the Electric Network Frequency Signals on Power Grid

Woorim Bang; Ji Won Yoon

doi:10.1109/ICTC46691.2019.8939676

Forecasting the Electric Network Frequency Signals on Power Grid

Woorim Bang, Ji Won Yoon

School of Cybersecurity

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

3 Citations (Scopus)

Abstract

The power grid, which is one of the important infrastructures, has a very challenging issue to stably manage electric power. The Electrical Network Frequency (ENF) which is the supply frequency on the power grid, however, has small variations near a constant frequency over time. In this paper, we studied the feasibility of predicting ENF values to operate the power grid reliably. To forecast ENF values, we analyzed the ENF signals by using auto-correlation and correlation coefficient. Based on the analysis results, we employed two approaches to forecast ENF values using a kernel regression model with correlation coefficient and autoregressive moving average model. To evaluate the accuracy of the proposed prediction algorithm, we experimented ENF data for 29 days in three power grids of the United States; the Eastern, the Western, and the Texas power grid. The results of our suggested methods presented the remarkable performance in forecasting ENF signals.

Original language	English
Title of host publication	ICTC 2019 - 10th International Conference on ICT Convergence
Subtitle of host publication	ICT Convergence Leading the Autonomous Future
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1218-1223
Number of pages	6
ISBN (Electronic)	9781728108926
DOIs	https://doi.org/10.1109/ICTC46691.2019.8939676
Publication status	Published - 2019 Oct
Event	10th International Conference on Information and Communication Technology Convergence, ICTC 2019 - Jeju Island, Korea, Republic of Duration: 2019 Oct 16 → 2019 Oct 18

Publication series

Name	ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future

Conference

Conference	10th International Conference on Information and Communication Technology Convergence, ICTC 2019
Country/Territory	Korea, Republic of
City	Jeju Island
Period	19/10/16 → 19/10/18

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported under the framework of international cooperation program managed by National Research Foundation of Korea(No.2017K1A3A1A17092614).

Publisher Copyright:
© 2019 IEEE.

Keywords

Data Mining
Electric Network Frequency
Prediction

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems and Management
Management of Technology and Innovation
Safety, Risk, Reliability and Quality
Media Technology
Control and Optimization

Access to Document

10.1109/ICTC46691.2019.8939676

Cite this

Bang, W., & Yoon, J. W. (2019). Forecasting the Electric Network Frequency Signals on Power Grid. In ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future (pp. 1218-1223). Article 8939676 (ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTC46691.2019.8939676

Forecasting the Electric Network Frequency Signals on Power Grid. / Bang, Woorim; Yoon, Ji Won.
ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1218-1223 8939676 (ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future).

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

Bang, W & Yoon, JW 2019, Forecasting the Electric Network Frequency Signals on Power Grid. in ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future., 8939676, ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future, Institute of Electrical and Electronics Engineers Inc., pp. 1218-1223, 10th International Conference on Information and Communication Technology Convergence, ICTC 2019, Jeju Island, Korea, Republic of, 19/10/16. https://doi.org/10.1109/ICTC46691.2019.8939676

Bang W, Yoon JW. Forecasting the Electric Network Frequency Signals on Power Grid. In ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1218-1223. 8939676. (ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future). doi: 10.1109/ICTC46691.2019.8939676

Bang, Woorim ; Yoon, Ji Won. / Forecasting the Electric Network Frequency Signals on Power Grid. ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1218-1223 (ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future).

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abstract = "The power grid, which is one of the important infrastructures, has a very challenging issue to stably manage electric power. The Electrical Network Frequency (ENF) which is the supply frequency on the power grid, however, has small variations near a constant frequency over time. In this paper, we studied the feasibility of predicting ENF values to operate the power grid reliably. To forecast ENF values, we analyzed the ENF signals by using auto-correlation and correlation coefficient. Based on the analysis results, we employed two approaches to forecast ENF values using a kernel regression model with correlation coefficient and autoregressive moving average model. To evaluate the accuracy of the proposed prediction algorithm, we experimented ENF data for 29 days in three power grids of the United States; the Eastern, the Western, and the Texas power grid. The results of our suggested methods presented the remarkable performance in forecasting ENF signals.",

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AB - The power grid, which is one of the important infrastructures, has a very challenging issue to stably manage electric power. The Electrical Network Frequency (ENF) which is the supply frequency on the power grid, however, has small variations near a constant frequency over time. In this paper, we studied the feasibility of predicting ENF values to operate the power grid reliably. To forecast ENF values, we analyzed the ENF signals by using auto-correlation and correlation coefficient. Based on the analysis results, we employed two approaches to forecast ENF values using a kernel regression model with correlation coefficient and autoregressive moving average model. To evaluate the accuracy of the proposed prediction algorithm, we experimented ENF data for 29 days in three power grids of the United States; the Eastern, the Western, and the Texas power grid. The results of our suggested methods presented the remarkable performance in forecasting ENF signals.

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Forecasting the Electric Network Frequency Signals on Power Grid

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this