Tree-related high impedance fault location using phase shift measurement of high frequency magnetic field

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

doi:10.1016/j.ijepes.2018.03.008

Cited by 17 publications

(15 citation statements)

References 20 publications

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“…In [27], the magnetic field sensors with a 3 km distance from each other were used for HI fault location in an overhead line distribution network. This method is not suitable for wide networks, especially with long-distance underground cables.…”

Section: Discussionmentioning

confidence: 99%

“…To use the continuous wavelet transform for analyzing fault signals in [8], high frequency sampling equipment (e.g., 1 GHz) is required. In addition, to have the same reference time for measurement units in [8,27], synchronization by a global positioning system (GPS) is required. The method that was presented in this paper improved the state-of-the-art as it did not have the weaknesses mentioned in [1,8,27].…”

Section: Discussionmentioning

confidence: 99%

“…The works in [1,8,27] were only tested in OHL distribution power network (DPN) using voltage and current and voltage and magnetic field measurement (see Table 7) with a 1.5-2.8% error. However, in this paper, in addition to checking the validity of the proposed method in a combined overhead line and underground cable distribution network, the maximum error was −0.49%.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, the HI faults may not be detected by the relay protection. Therefore, different methods have been proposed [1,7,9,[25][26][27]. Fault location and parameters can be estimated within the first cycles after fault occurrence [9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers

et al. 2020

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Power distribution networks are vulnerable to different faults, which compromise the grid performance and need to be managed effectively. Automatic and accurate fault detection and location are key components of effective fault management. This paper proposes a new framework for fault detection and location for smart distribution networks that are equipped with data loggers. The framework supports networks with mixed overhead lines and underground cables. The proposed framework consists of area detection, faulty section identification, and high impedance fault location. Firstly, the faulty zone and section are detected based on the operation of over-current relays and digital fault recorders. Then, by comparing the recorded traveling times at both ends of lines, which are related to the protection zone, the faulty line is identified. In the last step, the location of the fault is estimated based on discrete wavelet transform. The proposed method is tested on a 20 kV 13 node network, which is composed of overhead lines and underground cables. The method is tested in both balanced and unbalanced configurations. The obtained results confirm the advantages of the proposed method compared with the current state-of-the art.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers

et al. 2020

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“…In [27], arcing‐HIF indicators are installed in the vicinity of the sending feeders to measure the magnetic‐field strength. Bahador et al [28] propose a novel arcing‐HIF detection and location method based on non‐contact magnetic‐field strength measurement, and the proposed technique relies on the total phase shift of the high‐frequency component of the magnetic‐field strength in the plane perpendicular to the conductor axis.…”

Section: Data Acquisitionmentioning

confidence: 99%

AI in arcing‐HIF detection: a brief review

Bai

2020

IET Smart Grid

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In the past few decades, the arcing-high-impedance fault (arcing-HIF) detection problems have become an important issue in the effectively grounded distribution network. Many solutions have been proposed to address this problem. The most attractive way is artificial intelligence (AI) method. The paper gives a comprehensive review of arcing-HIF detection in distribution network-based AI. First, characteristics and models of arcing-HIF are analysed, the arcing-HIF database construction method is also explained; this part is a foundation work for arcing-HIF detection. Next, arcing-HIF detection methods based AI are summarised in details including data acquisition, feature extraction and classifier selection. Then, a set of criteria are proposed to evaluate the reliability of arcing-HIF detection algorithm. Finally, the future trends and challenges to arcing-HIF detection are also fully accounted. This review can be a valuable guide for researchers who are interested in arcing-HIF detection-based AI.

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The effectiveness of different sampling rates in vegetation high-impedance fault classification

Gomes

Ozansoy

Ulhaq³

et al. 2019

Electric Power Systems Research

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This paper investigates the alarming fire igniting scenario of High-Impedance Faults (HIF) resulting from the contact of vegetation with the power lines. Our findings are based on a set of experiments performed on a data set of real staged fault-signals sampled by two distinct channels with different band-pass filters. Representations from these two sampling methods are extracted by different signal processing methods and ranked by their discriminative potential. Experimental results obtained by our proposed methodology show the effectiveness of wide band signals sampled at higher frequencies. Their features result in higher separability potential and are more effective at discriminating fault occurrences than ones from the lowfrequency channel. As the approach of employing high-frequency signals in such task may be faced with skepticism, the paper also discusses the possible concerns and feasibility of using higher sampling rate technologies for HIF fault classification.

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Tree-related high impedance fault location using phase shift measurement of high frequency magnetic field

Cited by 17 publications

References 20 publications

High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers

High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers

AI in arcing‐HIF detection: a brief review

The effectiveness of different sampling rates in vegetation high-impedance fault classification

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