Abstract
The frequency of power distribution networks in a power grid is called electrical network frequency (ENF). Because it provides the spatio-temporal changes of the power grid in a particular location, ENF is used in many application domains including the prediction of grid instability and blackouts, detection of system breakup, and even digital forensics. In order to build high performing applications and systems, it is necessary to capture a large-scale nationwide or worldwide ENF map. Consequently, many studies have been conducted on the distribution of specialized physical devices that capture the ENF signals. However, this approach is not practical because it requires significant effort from design to setup, moreover, it has a limitation in its efficiency to monitor and stably retain the collection equipment distributed throughout the world. Furthermore, this approach requires a significant budget. In this paper, we proposed a novel approach to constructing the worldwide ENF map by analyzing streaming data obtained by online multimedia services, such as "Youtube", "Earthcam", and "Ustream" instead of expensive specialized hardware. However, extracting accurate ENF from the streaming data is not a straightforward process because multimedia has its own noise and uncertainty. By applying several signal processing techniques, we can reduce noise and uncertainty, and improve the quality of the restored ENF. For the evaluation of this process, we compared the performance between the ENF signals restored by our proposed approach and collected by the frequency disturbance recorder (FDR) from FNET/GridEye. The experimental results show that our proposed approach outperforms in stable acquisition and management of the ENF signals compared to the conventional approach.
| Original language | English |
|---|---|
| Title of host publication | CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management |
| Publisher | Association for Computing Machinery |
| Pages | 19-28 |
| Number of pages | 10 |
| Volume | Part F131841 |
| ISBN (Electronic) | 9781450349185 |
| DOIs | |
| Publication status | Published - 2017 Nov 6 |
| Event | 26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore Duration: 2017 Nov 6 → 2017 Nov 10 |
Other
| Other | 26th ACM International Conference on Information and Knowledge Management, CIKM 2017 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 17/11/6 → 17/11/10 |
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Keywords
- Electrical network frequency
- Frequency domain
- Multimedia data
- Power grid
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Decision Sciences(all)
Cite this
Construction of a national scale ENF map using online multimedia data. / Kim, Hyunsoo; Jeon, Youngbae; Yoon, Ji Won.
CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Vol. Part F131841 Association for Computing Machinery, 2017. p. 19-28.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Construction of a national scale ENF map using online multimedia data
AU - Kim, Hyunsoo
AU - Jeon, Youngbae
AU - Yoon, Ji Won
PY - 2017/11/6
Y1 - 2017/11/6
N2 - The frequency of power distribution networks in a power grid is called electrical network frequency (ENF). Because it provides the spatio-temporal changes of the power grid in a particular location, ENF is used in many application domains including the prediction of grid instability and blackouts, detection of system breakup, and even digital forensics. In order to build high performing applications and systems, it is necessary to capture a large-scale nationwide or worldwide ENF map. Consequently, many studies have been conducted on the distribution of specialized physical devices that capture the ENF signals. However, this approach is not practical because it requires significant effort from design to setup, moreover, it has a limitation in its efficiency to monitor and stably retain the collection equipment distributed throughout the world. Furthermore, this approach requires a significant budget. In this paper, we proposed a novel approach to constructing the worldwide ENF map by analyzing streaming data obtained by online multimedia services, such as "Youtube", "Earthcam", and "Ustream" instead of expensive specialized hardware. However, extracting accurate ENF from the streaming data is not a straightforward process because multimedia has its own noise and uncertainty. By applying several signal processing techniques, we can reduce noise and uncertainty, and improve the quality of the restored ENF. For the evaluation of this process, we compared the performance between the ENF signals restored by our proposed approach and collected by the frequency disturbance recorder (FDR) from FNET/GridEye. The experimental results show that our proposed approach outperforms in stable acquisition and management of the ENF signals compared to the conventional approach.
AB - The frequency of power distribution networks in a power grid is called electrical network frequency (ENF). Because it provides the spatio-temporal changes of the power grid in a particular location, ENF is used in many application domains including the prediction of grid instability and blackouts, detection of system breakup, and even digital forensics. In order to build high performing applications and systems, it is necessary to capture a large-scale nationwide or worldwide ENF map. Consequently, many studies have been conducted on the distribution of specialized physical devices that capture the ENF signals. However, this approach is not practical because it requires significant effort from design to setup, moreover, it has a limitation in its efficiency to monitor and stably retain the collection equipment distributed throughout the world. Furthermore, this approach requires a significant budget. In this paper, we proposed a novel approach to constructing the worldwide ENF map by analyzing streaming data obtained by online multimedia services, such as "Youtube", "Earthcam", and "Ustream" instead of expensive specialized hardware. However, extracting accurate ENF from the streaming data is not a straightforward process because multimedia has its own noise and uncertainty. By applying several signal processing techniques, we can reduce noise and uncertainty, and improve the quality of the restored ENF. For the evaluation of this process, we compared the performance between the ENF signals restored by our proposed approach and collected by the frequency disturbance recorder (FDR) from FNET/GridEye. The experimental results show that our proposed approach outperforms in stable acquisition and management of the ENF signals compared to the conventional approach.
KW - Electrical network frequency
KW - Frequency domain
KW - Multimedia data
KW - Power grid
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U2 - 10.1145/3132847.3132982
DO - 10.1145/3132847.3132982
M3 - Conference contribution
AN - SCOPUS:85037335975
VL - Part F131841
SP - 19
EP - 28
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
ER -