Enhancing Data Transmission and Storage Efficiency in Power Grids Through Compression of Point-on-Wave Data

Yuru Wu; He Yin; Wei Qiu; Yilu Liu

doi:10.1109/IAS54024.2023.10406478

Enhancing Data Transmission and Storage Efficiency in Power Grids Through Compression of Point-on-Wave Data

Yuru Wu
, He Yin
, Wei Qiu
, Yilu Liu

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

1 Scopus citations

Abstract

This paper proposes an event-driven hybrid compression technique to effectively compress the Point-on-wave data of Grid-Edge current and voltage signals. An event identification function is utilized to detect the dynamic event, triggering the lossless compression for critical event signals without any information loss. For non-event signals, the lossy compression with higher compression ratio is activated to ensure efficient data transmission while preserving predefined critical information. Multiple lossless and lossy compression techniques are evaluated and the optimal selections are subsequently integrated into the proposed method. The performance of the proposed method is verified through an experiment using a generation trip signal, showcasing its ability to accurately detect events and preserve information during those events. Additionally, the proposed method reduces data size with an equivalent compression ratio of 16.7, enhancing the efficiency of data transmission and storage.

Original language	English
Title of host publication	2023 IEEE Industry Applications Society Annual Meeting, IAS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350320169
DOIs	https://doi.org/10.1109/IAS54024.2023.10406478
State	Published - 2023
Externally published	Yes
Event	2023 IEEE Industry Applications Society Annual Meeting, IAS 2023 - Nashville, United States Duration: Oct 29 2023 → Nov 2 2023

Publication series

Name	2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

Conference

Conference	2023 IEEE Industry Applications Society Annual Meeting, IAS 2023
Country/Territory	United States
City	Nashville
Period	10/29/23 → 11/2/23

Funding

This work is supported by the Technology Commercialization Fund under awarding number DE-EE 00038398, and in part by the DOE project: Development of a Low-Cost and Hardware-Friendly Instantaneous Waveform Measurement Technology for Distribution System.

Keywords

Waveform measurement
data compression
event detection
hybrid solution methods
points-on-wave

Access to Document

10.1109/IAS54024.2023.10406478

Cite this

Wu, Y., Yin, H., Qiu, W., & Liu, Y. (2023). Enhancing Data Transmission and Storage Efficiency in Power Grids Through Compression of Point-on-Wave Data. In 2023 IEEE Industry Applications Society Annual Meeting, IAS 2023 (2023 IEEE Industry Applications Society Annual Meeting, IAS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAS54024.2023.10406478

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title = "Enhancing Data Transmission and Storage Efficiency in Power Grids Through Compression of Point-on-Wave Data",

abstract = "This paper proposes an event-driven hybrid compression technique to effectively compress the Point-on-wave data of Grid-Edge current and voltage signals. An event identification function is utilized to detect the dynamic event, triggering the lossless compression for critical event signals without any information loss. For non-event signals, the lossy compression with higher compression ratio is activated to ensure efficient data transmission while preserving predefined critical information. Multiple lossless and lossy compression techniques are evaluated and the optimal selections are subsequently integrated into the proposed method. The performance of the proposed method is verified through an experiment using a generation trip signal, showcasing its ability to accurately detect events and preserve information during those events. Additionally, the proposed method reduces data size with an equivalent compression ratio of 16.7, enhancing the efficiency of data transmission and storage.",

keywords = "Waveform measurement, data compression, event detection, hybrid solution methods, points-on-wave",

author = "Yuru Wu and He Yin and Wei Qiu and Yilu Liu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Industry Applications Society Annual Meeting, IAS 2023 ; Conference date: 29-10-2023 Through 02-11-2023",

year = "2023",

doi = "10.1109/IAS54024.2023.10406478",

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series = "2023 IEEE Industry Applications Society Annual Meeting, IAS 2023",

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Wu, Y, Yin, H, Qiu, W & Liu, Y 2023, Enhancing Data Transmission and Storage Efficiency in Power Grids Through Compression of Point-on-Wave Data. in 2023 IEEE Industry Applications Society Annual Meeting, IAS 2023. 2023 IEEE Industry Applications Society Annual Meeting, IAS 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE Industry Applications Society Annual Meeting, IAS 2023, Nashville, United States, 10/29/23. https://doi.org/10.1109/IAS54024.2023.10406478

Enhancing Data Transmission and Storage Efficiency in Power Grids Through Compression of Point-on-Wave Data. / Wu, Yuru; Yin, He; Qiu, Wei et al.
2023 IEEE Industry Applications Society Annual Meeting, IAS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE Industry Applications Society Annual Meeting, IAS 2023).

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

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T1 - Enhancing Data Transmission and Storage Efficiency in Power Grids Through Compression of Point-on-Wave Data

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AU - Qiu, Wei

AU - Liu, Yilu

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Y1 - 2023

N2 - This paper proposes an event-driven hybrid compression technique to effectively compress the Point-on-wave data of Grid-Edge current and voltage signals. An event identification function is utilized to detect the dynamic event, triggering the lossless compression for critical event signals without any information loss. For non-event signals, the lossy compression with higher compression ratio is activated to ensure efficient data transmission while preserving predefined critical information. Multiple lossless and lossy compression techniques are evaluated and the optimal selections are subsequently integrated into the proposed method. The performance of the proposed method is verified through an experiment using a generation trip signal, showcasing its ability to accurately detect events and preserve information during those events. Additionally, the proposed method reduces data size with an equivalent compression ratio of 16.7, enhancing the efficiency of data transmission and storage.

AB - This paper proposes an event-driven hybrid compression technique to effectively compress the Point-on-wave data of Grid-Edge current and voltage signals. An event identification function is utilized to detect the dynamic event, triggering the lossless compression for critical event signals without any information loss. For non-event signals, the lossy compression with higher compression ratio is activated to ensure efficient data transmission while preserving predefined critical information. Multiple lossless and lossy compression techniques are evaluated and the optimal selections are subsequently integrated into the proposed method. The performance of the proposed method is verified through an experiment using a generation trip signal, showcasing its ability to accurately detect events and preserve information during those events. Additionally, the proposed method reduces data size with an equivalent compression ratio of 16.7, enhancing the efficiency of data transmission and storage.

KW - Waveform measurement

KW - data compression

KW - event detection

KW - hybrid solution methods

KW - points-on-wave

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DO - 10.1109/IAS54024.2023.10406478

M3 - Conference contribution

AN - SCOPUS:85186122571

T3 - 2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

BT - 2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

Y2 - 29 October 2023 through 2 November 2023

ER -

Enhancing Data Transmission and Storage Efficiency in Power Grids Through Compression of Point-on-Wave Data

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