An S-Transform and Support Vector Machine (SVM)-Based Online Method for Diagnosing Broken Strands in Transmission Lines

Xingliang Jiang, Yunfeng Xia, Jianlin Hu, Zhijin Zhang, Lichun Shu and Caxin Sun
Additional contact information
Xingliang Jiang: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China
Yunfeng Xia: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China
Jianlin Hu: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China
Zhijin Zhang: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China
Lichun Shu: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China
Caxin Sun: State Key Laboratory of Power Transmission Equipment & System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China

Energies, 2011, vol. 4, issue 9, 1-23

Abstract: During their long-term outdoor field service, overhead transmission lines will be exposed to strikes by lightning, corrosion by chemical contaminants, ice-shedding, wind vibration of conductors, line galloping, external destructive forces and so on, which will generally cause a series of latent faults such as aluminum strand fracture. This may lead to broken transmission lines which will have a very strong impact on the safe operation of power grids that if the latent faults cannot be recognized and fixed as soon as possible. The detection of broken strands in transmission lines using inspection robots equipped with suitable detectors is a method with good prospects. In this paper, a method for detecting broken strands in transmission lines using an eddy current transducer (ECT) carried by a robot is developed, and an approach for identifying broken strands in transmission lines based on an S-transform is proposed. The proposed approach utilizes the S-transform to extract the module and phase information at each frequency point from detection signals. Through module phase and comparison, the characteristic frequency points are ascertained, and the fault information of the detection signal is constructed. The degree of confidence of broken strand identification is defined by the Shannon fuzzy entropy (SFE-BSICD). The proposed approach combines module information while utilizing phase information, SFE-BSICD, and the energy, so the reliability is greatly improved. These characteristic qualities of broken strands in transmission lines are used as the input of a multi-classification SVM, allowing the number of broken strands to be determined. Through experimental field verification, it can be shown that the proposed approach displays high accuracy and the SFE-BSICD is defined reasonably.

Keywords: broken strands; transmission lines; eddy current transducer; S-transform; support vector machine; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/4/9/1278/pdf (application/pdf)
https://www.mdpi.com/1996-1073/4/9/1278/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:4:y:2011:i:9:p:1278-1300:d:13736

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().