Paper Info
Title
Forecasting The Electric Network Frequency Signals On Power Grid
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.
Year
DOI
Venue
2019
10.1109/ictc46691.2019.8939676
2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC): ICT CONVERGENCE LEADING THE AUTONOMOUS FUTURE
Keywords
Field
DocType
Prediction, Electric Network Frequency, Data Mining
Autoregressive–moving-average model,Correlation coefficient,Electric power,Computer science,Electrical network frequency,Power grid,Electronic engineering,Kernel regression,Electric network
Conference
ISSN
Citations 
PageRank 
2162-1233
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Woorim Bang100.68
Ji Won Yoon211223.94