Mining Partially Labeled Data from Edge Devices to Detect and Locate High Impedance Faults

Qiushi Cui, Yang Weng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The security of active distribution systems is critical to grid modernization along with deep renewable penetration, where the protection plays a vital role. Among various security issues in protection, conventional protection clears only 17.5% of staged high impedance faults (HIFs) due to the limited electrical data utilization. For resolving this problem, a detection and location scheme based on μ-PMUs is presented to enhance data processing capability for HIF detection through machine learning and big data analytics. To detect HIFs with reduced cost on data labeling, we choose expectation-maximization (EM) algorithm for semi-supervised learning (SSL) since it is capable of expressing complex relationships between the observed and target variables by fitting Gaussian models. As one of the generative models, EM algorithm is compared with two discriminative models to highlight its detection performance. To make HIF location robust to HIF impedance variation, we adopt a probabilistic model embedding parameter learning into the physical line modeling. The location accuracy is validated at multiple locations of a distribution line. Numerical results show that the proposed EM algorithm greatly saves labeling cost and outperforms other SSL methods. Hardware-in-the-loop simulation proves a superior HIF location accuracy and detection time to complement the HIF's probabilistic model. With outstanding performance, we develop software for our utility partner to integrate the proposed scheme.

Original languageEnglish (US)
Title of host publicationiSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference
Subtitle of host publicationGrid Modernization for Energy Revolution, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2170-2175
Number of pages6
ISBN (Electronic)9781728149301
DOIs
StatePublished - Nov 2019
Event2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019 - Beijing, China
Duration: Nov 21 2019Nov 23 2019

Publication series

NameiSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference: Grid Modernization for Energy Revolution, Proceedings

Conference

Conference2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019
CountryChina
CityBeijing
Period11/21/1911/23/19

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Development
  • Economics and Econometrics
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Mining Partially Labeled Data from Edge Devices to Detect and Locate High Impedance Faults'. Together they form a unique fingerprint.

  • Cite this

    Cui, Q., & Weng, Y. (2019). Mining Partially Labeled Data from Edge Devices to Detect and Locate High Impedance Faults. In iSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference: Grid Modernization for Energy Revolution, Proceedings (pp. 2170-2175). [8974897] (iSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference: Grid Modernization for Energy Revolution, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iSPEC48194.2019.8974897