Real-time Abnormal Data Filtering Framework for Battery Energy Storage System Real-world Application

Cong Sheng Huang; Lelin Cai; Bharat Balagopal; Mo Yuen Chow

doi:10.1109/ISIE.2019.8781150

Real-time Abnormal Data Filtering Framework for Battery Energy Storage System Real-world Application

Cong Sheng Huang
, Lelin Cai
, Bharat Balagopal
, Mo Yuen Chow

Department of Electrical Engineering

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

5 Scopus citations

Abstract

Battery Energy Storage Systems (BESS) are key components in microgrids for reliable operations. To ensure the safety and reliability of the BESS, accurate State-of-Charge (SOC) estimation is crucial. Most battery SOC estimation algorithms are developed assuming the measured load current and terminal voltage data are trust-worthy. However, in the real-world BESS applications, the measured battery data contains abnormal data which result in poor SOC estimation accuracy and will be harmful to the safety of the BESS and the stability of the microgrid. In this paper, a practical abnormal data filtering framework for real-world BESS applications is proposed. This framework real-time detects and filters abnormal data in real-world applications. Three typical abnormal data filters are investigated and demonstrated in this paper to illustrate the effectiveness of using data filtering for accurate SOC estimation in real-world BESS applications.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2452-2457
Number of pages	6
ISBN (Electronic)	9781728136660
DOIs	https://doi.org/10.1109/ISIE.2019.8781150
State	Published - Jun 2019
Event	28th IEEE International Symposium on Industrial Electronics, ISIE 2019 - Vancouver, Canada Duration: 12 Jun 2019 → 14 Jun 2019

Publication series

Name	IEEE International Symposium on Industrial Electronics
Volume	2019-June

Conference

Conference	28th IEEE International Symposium on Industrial Electronics, ISIE 2019
Country/Territory	Canada
City	Vancouver
Period	12/06/19 → 14/06/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

State-of-Charge (SOC)
abnormal data
battery energy storage system (BESS)
data filtering
real-world micro-grid applications

Access to Document

10.1109/ISIE.2019.8781150

Cite this

Huang, C. S., Cai, L., Balagopal, B., & Chow, M. Y. (2019). Real-time Abnormal Data Filtering Framework for Battery Energy Storage System Real-world Application. In Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019 (pp. 2452-2457). Article 8781150 (IEEE International Symposium on Industrial Electronics; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE.2019.8781150

Huang, Cong Sheng ; Cai, Lelin ; Balagopal, Bharat et al. / Real-time Abnormal Data Filtering Framework for Battery Energy Storage System Real-world Application. Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2452-2457 (IEEE International Symposium on Industrial Electronics).

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title = "Real-time Abnormal Data Filtering Framework for Battery Energy Storage System Real-world Application",

abstract = "Battery Energy Storage Systems (BESS) are key components in microgrids for reliable operations. To ensure the safety and reliability of the BESS, accurate State-of-Charge (SOC) estimation is crucial. Most battery SOC estimation algorithms are developed assuming the measured load current and terminal voltage data are trust-worthy. However, in the real-world BESS applications, the measured battery data contains abnormal data which result in poor SOC estimation accuracy and will be harmful to the safety of the BESS and the stability of the microgrid. In this paper, a practical abnormal data filtering framework for real-world BESS applications is proposed. This framework real-time detects and filters abnormal data in real-world applications. Three typical abnormal data filters are investigated and demonstrated in this paper to illustrate the effectiveness of using data filtering for accurate SOC estimation in real-world BESS applications.",

keywords = "State-of-Charge (SOC), abnormal data, battery energy storage system (BESS), data filtering, real-world micro-grid applications",

author = "Huang, \{Cong Sheng\} and Lelin Cai and Bharat Balagopal and Chow, \{Mo Yuen\}",

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Huang, CS, Cai, L, Balagopal, B & Chow, MY 2019, Real-time Abnormal Data Filtering Framework for Battery Energy Storage System Real-world Application. in Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019., 8781150, IEEE International Symposium on Industrial Electronics, vol. 2019-June, Institute of Electrical and Electronics Engineers Inc., pp. 2452-2457, 28th IEEE International Symposium on Industrial Electronics, ISIE 2019, Vancouver, Canada, 12/06/19. https://doi.org/10.1109/ISIE.2019.8781150

Real-time Abnormal Data Filtering Framework for Battery Energy Storage System Real-world Application. / Huang, Cong Sheng; Cai, Lelin; Balagopal, Bharat et al.
Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2452-2457 8781150 (IEEE International Symposium on Industrial Electronics; Vol. 2019-June).

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

TY - GEN

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AU - Balagopal, Bharat

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Y1 - 2019/6

N2 - Battery Energy Storage Systems (BESS) are key components in microgrids for reliable operations. To ensure the safety and reliability of the BESS, accurate State-of-Charge (SOC) estimation is crucial. Most battery SOC estimation algorithms are developed assuming the measured load current and terminal voltage data are trust-worthy. However, in the real-world BESS applications, the measured battery data contains abnormal data which result in poor SOC estimation accuracy and will be harmful to the safety of the BESS and the stability of the microgrid. In this paper, a practical abnormal data filtering framework for real-world BESS applications is proposed. This framework real-time detects and filters abnormal data in real-world applications. Three typical abnormal data filters are investigated and demonstrated in this paper to illustrate the effectiveness of using data filtering for accurate SOC estimation in real-world BESS applications.

AB - Battery Energy Storage Systems (BESS) are key components in microgrids for reliable operations. To ensure the safety and reliability of the BESS, accurate State-of-Charge (SOC) estimation is crucial. Most battery SOC estimation algorithms are developed assuming the measured load current and terminal voltage data are trust-worthy. However, in the real-world BESS applications, the measured battery data contains abnormal data which result in poor SOC estimation accuracy and will be harmful to the safety of the BESS and the stability of the microgrid. In this paper, a practical abnormal data filtering framework for real-world BESS applications is proposed. This framework real-time detects and filters abnormal data in real-world applications. Three typical abnormal data filters are investigated and demonstrated in this paper to illustrate the effectiveness of using data filtering for accurate SOC estimation in real-world BESS applications.

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Huang CS, Cai L, Balagopal B, Chow MY. Real-time Abnormal Data Filtering Framework for Battery Energy Storage System Real-world Application. In Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2452-2457. 8781150. (IEEE International Symposium on Industrial Electronics). doi: 10.1109/ISIE.2019.8781150

Real-time Abnormal Data Filtering Framework for Battery Energy Storage System Real-world Application

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