A Complex Network Based Classification of Covered Conductors Faults Detection

Vantuch, Tomáš; Gaura, Jan; Mišák, Stanislav; Zelinka, Ivan

doi:10.1007/978-3-319-48499-0_33

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Covered conductors have been widely used for medium voltage overhead lines in forested or dissected terrain areas because of their higher operational reliability and reduced land use [1]. Compared with uninsulated overhead lines, the interphase touch of conductors or the contact with tree branches of covered conductors does not lead to an immediate short-circuit fault [2]. Nevertheless, a persistent contact with a tree branch may degrade conductor insulation over time and develop into a fault that hampers the normal operation of a power distribution system.…”

Section: Introductionmentioning

confidence: 99%

“…However, to make it really work, an effective pattern recognition method is required to recognize faulty-related PD activities from its acquired signals. Several studies were brought to analyse the signal characters using fuzzy theory [11], mathematical chaos [12], complex networks [2] or machine learning models [4]. To bring more attention on this problem, in 2018, a dataset which contained a large number of signal measurements was published on Kaggle, the world's largest data science collaboration platform [13].…”

Section: Introductionmentioning

confidence: 99%

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Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features

Chen

Vantuch

Zhang

et al. 2020

IEEE Trans. Smart Grid

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The detection and characterization of partial discharge (PD) are crucial for the insulation diagnosis of overhead lines with covered conductors. With the release of a large dataset containing thousands of naturally obtained high-frequency voltage signals, data-driven analysis of fault-related PD patterns on an unprecedented scale becomes viable. The high diversity of PD patterns and background noise interferences motivates us to design an innovative pulse shape characterization method based on clustering techniques, which can dynamically identify a set of representative PD-related pulses. Capitalizing on those pulses as referential patterns, we construct insightful features and develop a novel machine learning model with a superior detection performance for early-stage covered conductor faults. The presented model outperforms the winning model in a Kaggle competition and provides the state-of-the-art solution to detect real-time disturbances in the field. Index Terms-Covered conductor, partial discharges, clustering methods, gradient boosting trees.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features

Chen

Vantuch

Zhang

et al. 2020

IEEE Trans. Smart Grid

Self Cite

View full text Add to dashboard Cite

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“…The feature extraction approaches described in available literature varied among the application of basic statistical features [16], the wavelet based decomposition [17], the Fourier transform [18], the fractal theory [19], image based processing, chaos theory [20,21] or representation based on graph structure and adjacency matrices [22,23]. The relevance of features extracted by any selected approach depends on the proper adjustment of the process as well as its ability to handle the external background noise (EBN).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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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