Modelling and detection of live tree‐related high impedance fault in distribution systems

Bahador, Nooshin; Namdari, Farhad; Matinfar, Hamid Reza

doi:10.1049/iet-gtd.2017.0211

Cited by 27 publications

(13 citation statements)

References 20 publications

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“…However, different contact surfaces can affect the arcing phenomenon of HIFs. Therefore, existing methods have mainly been proposed for detecting one specific type of HIF, such as vegetation HIFs [2], [3], soil-grounding HIFs [4], underwater cable HIFs [5], etc. However, an accurate model representing all types of HIFs is almost impossible.…”

Section: Introductionmentioning

confidence: 99%

A Transfer Learning-Based High Impedance Fault Detection Method Under a Cloud-Edge Collaboration Framework

Zhang

Wang

et al. 2020

IEEE Access

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High impedance faults (HIFs) in distribution networks are hard to describe and be detected precisely because of the complexity and randomness of their features. Therefore, traditional feature analysis methods may lack sufficient reliability and generalization, which makes data-based methods a more appropriate option. However, according to previous statistical analyses, in practical scenarios, only a small quantity of historical HIF data (less than 20%) can be recorded and utilized. In this paper, a transfer learning-based HIF detection method is proposed under a cloud-edge collaboration framework of the Internet of Things, which can solve the problem of insufficient data by integrating historical data from multiple distribution networks. Through the cloud-edge collaboration framework, all features from different distribution networks are first integrated to form a basic cloud convolutional neural network model for HIF detection. The features are extracted and updated by edge computers based on the accurate synchronous measurements provided by distribution-level phasor measurement units. To uniform the data scales of the different distribution networks, principal component analysis is adopted during feature extraction. Specific to each distribution network, the target HIF detection model is transferred from the basic cloud model by fine-tuning. Furthermore, a data augmentation method based on locality sensitive hashing is proposed to improve the performance of the transferred model. The proposed HIF detection method can be operated in both online and offline modes. The performance was verified by seven different distribution networks in numerical simulations and one practical experimental distribution network. INDEX TERMS High impedance faults, cloud-edge collaboration, distribution-level phasor measurement units, transfer learning.

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Section: Introductionmentioning

confidence: 99%

A Transfer Learning-Based High Impedance Fault Detection Method Under a Cloud-Edge Collaboration Framework

Zhang

Wang

et al. 2020

IEEE Access

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“…Another approach of mathematical modeling of HIF and its effect on environment is studied using finite element method (FEM). These methods involve in solving numerical equation which introduces delay and computational complexity of operation …”

Section: Introductionmentioning

confidence: 99%

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

Veerasamy

Wahab

Vinayagam³

et al. 2020

Int Trans Electr Energ Syst

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Summary This paper proposes a discrete wavelet transform (DWT)‐based Graphical Language classifier algorithm for identification of high‐impedance fault (HIF) in medium voltage (MV) distribution network of 13.8 kV. The proposed method of classifier is developed using virtual instrumentation LabVIEW facility, for detection of various faults such as symmetrical, unsymmetrical, and HIF in the system. Initially, the MV distribution feeder network has been modeled in MATLAB/Simulink, and the DWT analysis has been carried out with the introduction of various faults in the network to extract the features. The extracted features such as SD and energy values from the fault current signals have been applied to the proposed classifier algorithm to identify the type of fault. The effectiveness of the presented method has been tested and compared with the similar conventional fuzzy‐based approach. The results indicate that the proposed classifier algorithm outperforms to give 100% accuracy, while the fuzzy‐based approach misclassifies the double line to ground fault (LLG), three‐phase fault (LLLG), and HIF. Furthermore, the proposed algorithm with LabVIEW facility is more flexible and can be implemented in real time using data acquisition unit for obtaining fault current signal from power system.

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“…Relevant recent works [5]- [7] on HIF models usually rely on data from real staged faults to create a variable fault impedance that mimics the effects observed in the electric signals. This, in turn, incentives methods proposing fault detection or location schemes that rely on the models to simulate fault occurrences.…”

Section: Introductionmentioning

confidence: 99%

Vegetation High-Impedance Faults’ High-Frequency Signatures via Sparse Coding

Gomes

Ozansoy

Ulhaq

2020

IEEE Trans. Instrum. Meas.

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The behavior of High-Impedance Faults (HIFs) in power distribution systems depends on multiple factors, making it a challenging disturbance to model. If enough data from real staged faults is provided, signal processing techniques can help reveal patterns from a specific type of fault. Such a task is implemented herein by employing the Shift-Invariant Sparse Coding (SISC) technique on a data set of staged vegetation high-impedance faults. The technique facilitates the uncoupling of shifted and convoluted patterns present in the recorded signals from fault tests. The deconvolution of these patterns was then individually studied to identify the possible repeating fault signatures. The work is primarily focused on the investigation of the under-discussed high-frequency faults signals, especially regarding voltage disturbances created by the fault currents. Therefore, the main contribution from this paper is the resulted evidence of consistent behavior from real vegetation HIFs at higher frequencies. These results can enhance phenomena awareness and support future methodologies dealing with these disturbances.

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Modelling and detection of live tree‐related high impedance fault in distribution systems

Cited by 27 publications

References 20 publications

A Transfer Learning-Based High Impedance Fault Detection Method Under a Cloud-Edge Collaboration Framework

A Transfer Learning-Based High Impedance Fault Detection Method Under a Cloud-Edge Collaboration Framework

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

Vegetation High-Impedance Faults’ High-Frequency Signatures via Sparse Coding

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