[Poster Presentation]Latent fault detection method for medium voltage cables based on Kizilcay arc model and KNN

Latent fault detection method for medium voltage cables based on Kizilcay arc model and KNN
ID:188 Submission ID:17 View Protection:ATTENDEE Updated Time:2021-12-03 10:45:55 Hits:454 Poster Presentation

Start Time:2021-12-17 14:35 (Asia/Shanghai)

Duration:5min

Session:[Z] Poster Session » [Z3] Poster Session 3: Power system and automation

Video No Permission Presentation File

Tips: The file permissions under this presentation are only for participants. You have not logged in yet and cannot view it temporarily.

Abstract
Cable plays an important role in urban medium voltage distribution networks. Detecting and locating latent cable faults is of great significance for preventing permanent cable faults, reducing power outages caused by faults, and ensuring the safe operation of power systems. In this paper, based on Kizilcay arc model, an equivalent model suitable for cable latent fault in small resistance grounded distribution system is established, and a typical 10kV small resistance cable distribution network model is built in PSCAD / EMTDC platform to compare and analyze the effects of different fault parameters on the characteristics of latent fault arc. Secondly, based on the phase space reconstruction theory and fractal theory, the latent fault characteristics of medium voltage distribution cables are extracted. Based on latent fault feature vector and KNN algorithm classifier, a new latent fault detection method for medium voltage distribution cable is proposed. The effectiveness of the proposed latent fault detection method is verified by PSCAD / EMTDC simulation.
Keywords
Latent cable fault; Kizilcay arc model; Phase space reconstruction; Fractal theory; KNN algorithm classifier
Speaker
Xue Chang
Shandong University

Submission Author
Baodong Zhang State Grid Dezhou Power Supply Company
Chaozhang Liu State Grid Dezhou Power Supply Company
Jian Liu State Grid Dezhou Power Supply Company
Xue Chang Shandong University
Comment submit
Verification code Change another
All comments
Contact Us:
Southwest Jiaotong University(SWJTU)
Add: No.999, Xi'an Road, Pidu District, Chengdu City, Sichuan Province,611756 China
Email: ciycee2021@163.com

 

WeChat public account:

IEEE IAS SWJTU学生分会

WeChatgroup: 

CIYCEE2021 

CIYCEE官方微信群:CIYCEE2021