High-Impedance Fault Identification on Distribution Networks

Santos, W. C.; Lopes, Felipe V.; Brito, Núbia Silva Dantas; Souza, Benemar de

doi:10.1109/tpwrd.2016.2548942

Cited by 186 publications

(99 citation statements)

References 21 publications

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“…A sift into the past ten years of research can shed light on common correlational features used in modern detection approaches. Among the most popular, wavelet features have been gaining presence and popularity in much of the works [4]- [7]. The detailed coefficients, outputs of the wavelet transformation, are usually used in two main detection approaches.…”

Section: Introductionmentioning

confidence: 99%

High-Sensitivity Vegetation High-Impedance Fault Detection Based on Signal's High-Frequency Contents

Gomes

Ozansoy

Ulhaq

2018

IEEE Trans. Power Delivery

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High Impedance Faults (HIFs) are linked to enduring unaddressed knowledge gaps due to their diverse and complex behavior, despite being extensively researched disturbances. Vegetation HIFs, for instance, are a particular type of fault that can lead to great fire hazards and life risks. They have unique fault signatures and should receive special attention if fire risk mitigation is desired. This paper focuses on the detection of these distinct, very small current faults. As the main correlational features, the proposed methodology uses the vegetation fault signatures' high-frequency content. Different from many previous works that rely on HIF models, the approach validation is performed using a real dataset comprising a large number of experiments, sampled in a functioning network in the presence of noise. The classification is performed by boosted decision trees, which showed high dependability and security in the classification of small phase-to-earth and phase-to-phase HIFs.

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Section: Introductionmentioning

confidence: 99%

High-Sensitivity Vegetation High-Impedance Fault Detection Based on Signal's High-Frequency Contents

Gomes

Ozansoy

Ulhaq

2018

IEEE Trans. Power Delivery

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“…Nevertheless, all these works, presents the identification and location of conventional fault without considering the HIF. On the other hand, the authors in and present the identification and location of HIF in power system but fail to test the robustness of the classifier for all other conventional faults and switching transients. In this case, the accuracy of identification of HIF in the system lies in the range of 70% to 100% by methods like DWT, Morphological fault detector and combination of WT with other various classifiers namely Fuzzy‐ARTMAP, Boosted decision tree, Finite element method and Extreme learning machine.…”

Section: Comparison With Literature Workmentioning

confidence: 99%

“…In linking with previously published work, though the various intelligence‐based classifier techniques are accurate in detecting the fault but suffers from few limitation such as: increased burden of its computational complexity, required large size training data with proper tuning of weight/membership function, long run time and non‐identical solution in repeated runs that can increases the difficultly level to implement in real time . With the advancement of digital technology and communication networks, a classical digital system‐based deterministic approach is used for monitoring the condition of the system is proposed in this paper . The proffered classifier is a simple Graphical Language (GL) algorithm, developed using LabVIEW interfacing facility.…”

Section: Introductionmentioning

confidence: 97%

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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“…If a discrete signal x ( k ) is transformed by wavelet function, it has high‐frequency component coefficients d j ( k ) and low‐frequency coefficient a j ( k ) at instant k and scale j. The frequency band ranges contained in the signal components D j ( k ) and A j ( k ) obtained by reconstruction are :

\{\begin{matrix} center \\ D_{j} (k) : [2^{- (j + 1)} f_{s}, 2^{- j} f_{s}] \\ A_{j} (k) : [0, 2^{- j} f_{s}] \end{matrix}

where f s is the sampling frequency, j = 1, 2,…, m . The original signal sequence x ( k ) can be represented by the sum of all components, namely:

\begin{matrix} x ((), k) & = D_{1} ((), k) + A_{1} ((), k) = D_{1} ((), k) + D_{2} ((), k) + A_{2} ((), k) \\ = \sum_{j = 1}^{m} D_{j} ((), k) + A_{2} ((), k) \end{matrix}

…”

Section: The Theory Of Wavelet Transforms and Wavelet Entropymentioning

confidence: 99%

Detection of high‐impedance fault in distribution network based on time–frequency entropy of wavelet transform

Shu

Xiao

2020

IEEJ Transactions Elec Engng

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Due to the characteristics of small, nonlinear, random, and unstable current, the high impedance faults (HIF) are not always effectively cleared by conventional over current relays. A new simple and effective algorithm of HIF detection in distribution network based on the wavelet time-frequency entropy is presented in this article. On the basis of analysis, the time-frequency distribution characteristics in the concerned frequency band of HIFs by wavelet transform. The feature sequence developed by the wavelet time-frequency entropy at the time sequence is employed to detect HIF at substation. This criterion is calculated from the sum of wavelet time-frequency entropy at the time sequence every half cycle. If the value of the criterion is more than the threshold during four cycles, the HIF event can be identified. The developed detection method has been tested with real-world recorded signals from different HIF medium and PSCAD/EMTDC-generated signals. The result shows that the proposed method is robust to transients generated during normal events such as capacitor bank switching, load switching, and harmonic load. It has good performance for antinoise ability by noise reduction coefficient improved appropriately. And the proposed method has better reliability than the traditional third-harmonic-based method.

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High-Impedance Fault Identification on Distribution Networks

Cited by 186 publications

References 21 publications

High-Sensitivity Vegetation High-Impedance Fault Detection Based on Signal's High-Frequency Contents

High-Sensitivity Vegetation High-Impedance Fault Detection Based on Signal's High-Frequency Contents

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

Detection of high‐impedance fault in distribution network based on time–frequency entropy of wavelet transform

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