Towards the Text Compression Based Feature Extraction in High Impedance Fault Detection

High impedance faults of medium voltage overhead lines with covered conductors can be identified by the presence of partial discharges. Despite it is a subject of research for more than 60 years, online partial discharges detection is always a challenge, especially in environment with heavy backgrou...

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Published inEnergies (Basel) Vol. 12; no. 11; p. 2148
Main Authors Vantuch, Tomáš, Prílepok, Michal, Fulneček, Jan, Hrbáč, Roman, Mišák, Stanislav
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 05.06.2019
Subjects
Alzheimer's disease
Artificial intelligence
Classification
covered conductor
Experiments
Fourier transforms
high impedance fault detection
International conferences
Lempel-Ziv complexity
Machine learning
Noise
overhead lines
partial discharges
text compression
Time series
Wavelet transforms
United States > US
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Summary:High impedance faults of medium voltage overhead lines with covered conductors can be identified by the presence of partial discharges. Despite it is a subject of research for more than 60 years, online partial discharges detection is always a challenge, especially in environment with heavy background noise. In this paper, a new approach for partial discharge pattern recognition is presented. All results were obtained on data, acquired from real 22 kV medium voltage overhead power line with covered conductors. The proposed method is based on a text compression algorithm and it serves as a signal similarity estimation, applied for the first time on partial discharge pattern. Its relevancy is examined by three different variations of classification model. The improvement gained on an already deployed model proves its quality.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en12112148