Partial Discharge Identification of Power Transformers Based on Chaotic Characteristics of the Cumulative Energy Function

Shao Rui Qin; Yu Hang Fang; Guo Cheng Ding; Tai Yun Zhu; Jian Lin Li; Chen Chen Zhang; Guan Jun Zhang

doi:10.1109/CMD.2018.8535653

Partial Discharge Identification of Power Transformers Based on Chaotic Characteristics of the Cumulative Energy Function

Shao Rui Qin
, Yu Hang Fang
, Guo Cheng Ding
, Tai Yun Zhu
, Jian Lin Li
, Chen Chen Zhang
, Guan Jun Zhang

School of Electrical Engineering

State Grid Anhui Electric Power Research Institute
Xi'an Jiaotong University
State Grid Anhui Electric Power Co., Ltd.

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

Abstract

The diagnosis of partial discharge(PD) in power transformer is of great significance to the evaluation of transformer operation state. The traditional method which is based on pattern recognition of Phase Resolved Partial Discharge(PRPD) Pattern is limited to the morphological characteristics of partial discharge spectra analysis. The lack of deeper research of the characteristic of partial discharge leads to low recognition rate and inaccuracy. This paper presents a method of partial discharge pattern recognition based on the chaos theory and the cumulative energy function. A chaotic analysis of the cumulative energy function of partial discharge signals is carried out to calculate the maximum Lyapunov exponent and obtain the maximum Lyapunov exponent distribution characteristics of different types of partial discharge defects. In this paper, different types of partial discharge defects, such as protrusion discharge, void discharge and surface discharge, were placed on the 35kV experimental transformer. The experimental results show that the overall recognition of partial discharge type of transformer is better than traditional method by using chaos characteristics.

Original language	English
Title of host publication	2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings
Editors	Ahmed Abu-Siada
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538641262
DOIs	https://doi.org/10.1109/CMD.2018.8535653
State	Published - 14 Nov 2018
Event	7th International Conference on Condition Monitoring and Diagnosis, CMD 2018 - Perth, Australia Duration: 23 Sep 2018 → 26 Sep 2018

Publication series

Name	2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings

Conference

Conference	7th International Conference on Condition Monitoring and Diagnosis, CMD 2018
Country/Territory	Australia
City	Perth
Period	23/09/18 → 26/09/18

Keywords

Chaos theory
Cumulative energy function
Partial discharges
Power transformers

Access to Document

10.1109/CMD.2018.8535653

Cite this

Qin, S. R., Fang, Y. H., Ding, G. C., Zhu, T. Y., Li, J. L., Zhang, C. C., & Zhang, G. J. (2018). Partial Discharge Identification of Power Transformers Based on Chaotic Characteristics of the Cumulative Energy Function. In A. Abu-Siada (Ed.), 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings Article 8535653 (2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CMD.2018.8535653

Qin, Shao Rui ; Fang, Yu Hang ; Ding, Guo Cheng et al. / Partial Discharge Identification of Power Transformers Based on Chaotic Characteristics of the Cumulative Energy Function. 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings. editor / Ahmed Abu-Siada. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings).

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title = "Partial Discharge Identification of Power Transformers Based on Chaotic Characteristics of the Cumulative Energy Function",

abstract = "The diagnosis of partial discharge(PD) in power transformer is of great significance to the evaluation of transformer operation state. The traditional method which is based on pattern recognition of Phase Resolved Partial Discharge(PRPD) Pattern is limited to the morphological characteristics of partial discharge spectra analysis. The lack of deeper research of the characteristic of partial discharge leads to low recognition rate and inaccuracy. This paper presents a method of partial discharge pattern recognition based on the chaos theory and the cumulative energy function. A chaotic analysis of the cumulative energy function of partial discharge signals is carried out to calculate the maximum Lyapunov exponent and obtain the maximum Lyapunov exponent distribution characteristics of different types of partial discharge defects. In this paper, different types of partial discharge defects, such as protrusion discharge, void discharge and surface discharge, were placed on the 35kV experimental transformer. The experimental results show that the overall recognition of partial discharge type of transformer is better than traditional method by using chaos characteristics.",

keywords = "Chaos theory, Cumulative energy function, Partial discharges, Power transformers",

author = "Qin, \{Shao Rui\} and Fang, \{Yu Hang\} and Ding, \{Guo Cheng\} and Zhu, \{Tai Yun\} and Li, \{Jian Lin\} and Zhang, \{Chen Chen\} and Zhang, \{Guan Jun\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 7th International Conference on Condition Monitoring and Diagnosis, CMD 2018 ; Conference date: 23-09-2018 Through 26-09-2018",

year = "2018",

month = nov,

day = "14",

doi = "10.1109/CMD.2018.8535653",

language = "英语",

series = "2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Ahmed Abu-Siada",

booktitle = "2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings",

}

Qin, SR, Fang, YH, Ding, GC, Zhu, TY, Li, JL, Zhang, CC & Zhang, GJ 2018, Partial Discharge Identification of Power Transformers Based on Chaotic Characteristics of the Cumulative Energy Function. in A Abu-Siada (ed.), 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings., 8535653, 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 7th International Conference on Condition Monitoring and Diagnosis, CMD 2018, Perth, Australia, 23/09/18. https://doi.org/10.1109/CMD.2018.8535653

Partial Discharge Identification of Power Transformers Based on Chaotic Characteristics of the Cumulative Energy Function. / Qin, Shao Rui; Fang, Yu Hang; Ding, Guo Cheng et al.
2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings. ed. / Ahmed Abu-Siada. Institute of Electrical and Electronics Engineers Inc., 2018. 8535653 (2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings).

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

TY - GEN

T1 - Partial Discharge Identification of Power Transformers Based on Chaotic Characteristics of the Cumulative Energy Function

AU - Qin, Shao Rui

AU - Fang, Yu Hang

AU - Ding, Guo Cheng

AU - Zhu, Tai Yun

AU - Li, Jian Lin

AU - Zhang, Chen Chen

AU - Zhang, Guan Jun

PY - 2018/11/14

Y1 - 2018/11/14

N2 - The diagnosis of partial discharge(PD) in power transformer is of great significance to the evaluation of transformer operation state. The traditional method which is based on pattern recognition of Phase Resolved Partial Discharge(PRPD) Pattern is limited to the morphological characteristics of partial discharge spectra analysis. The lack of deeper research of the characteristic of partial discharge leads to low recognition rate and inaccuracy. This paper presents a method of partial discharge pattern recognition based on the chaos theory and the cumulative energy function. A chaotic analysis of the cumulative energy function of partial discharge signals is carried out to calculate the maximum Lyapunov exponent and obtain the maximum Lyapunov exponent distribution characteristics of different types of partial discharge defects. In this paper, different types of partial discharge defects, such as protrusion discharge, void discharge and surface discharge, were placed on the 35kV experimental transformer. The experimental results show that the overall recognition of partial discharge type of transformer is better than traditional method by using chaos characteristics.

AB - The diagnosis of partial discharge(PD) in power transformer is of great significance to the evaluation of transformer operation state. The traditional method which is based on pattern recognition of Phase Resolved Partial Discharge(PRPD) Pattern is limited to the morphological characteristics of partial discharge spectra analysis. The lack of deeper research of the characteristic of partial discharge leads to low recognition rate and inaccuracy. This paper presents a method of partial discharge pattern recognition based on the chaos theory and the cumulative energy function. A chaotic analysis of the cumulative energy function of partial discharge signals is carried out to calculate the maximum Lyapunov exponent and obtain the maximum Lyapunov exponent distribution characteristics of different types of partial discharge defects. In this paper, different types of partial discharge defects, such as protrusion discharge, void discharge and surface discharge, were placed on the 35kV experimental transformer. The experimental results show that the overall recognition of partial discharge type of transformer is better than traditional method by using chaos characteristics.

KW - Chaos theory

KW - Cumulative energy function

KW - Partial discharges

KW - Power transformers

UR - https://www.scopus.com/pages/publications/85059104702

U2 - 10.1109/CMD.2018.8535653

DO - 10.1109/CMD.2018.8535653

M3 - 会议稿件

AN - SCOPUS:85059104702

T3 - 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings

BT - 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings

A2 - Abu-Siada, Ahmed

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th International Conference on Condition Monitoring and Diagnosis, CMD 2018

Y2 - 23 September 2018 through 26 September 2018

ER -

Qin SR, Fang YH, Ding GC, Zhu TY, Li JL, Zhang CC et al. Partial Discharge Identification of Power Transformers Based on Chaotic Characteristics of the Cumulative Energy Function. In Abu-Siada A, editor, 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8535653. (2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings). doi: 10.1109/CMD.2018.8535653

Partial Discharge Identification of Power Transformers Based on Chaotic Characteristics of the Cumulative Energy Function

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