Insights into applications of IEEE Standards for Ground Fault Protection in Industrial and Commercial Power Systems

Mardegan, Claudio S.; Rifaat, Rasheek

doi:10.1109/icps.2014.6839181

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…In a three-phase transmission line, faults can be classified as: single Line to Ground (LG), Double Line (LL), Double Line to Ground (LLG) and three-phase to ground [28]- [31]. Statistics such as those compiled by authors in [31], [32] have shown that LG faults are the most common type in power systems. According to statistics collected by authors in [33], more than 90% of overhead transmission line fault are LG faults.…”

Section: A Current Methods Of Inspection Faults By Dnos and Tnosmentioning

confidence: 99%

An Advanced Unmanned Aerial Vehicle (UAV) Approach via Learning-Based Control for Overhead Power Line Monitoring: A Comprehensive Review

Foudeh

Luk²,

Whidborne

2021

IEEE Access

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Detection and prevention of faults in overhead electric lines is critical for the reliability and availability of electricity supply. The disadvantages of conventional methods range from cumbersome installations to costly maintenance and from lack of adaptability to hazards for human operators. Thus, transmission inspections based on unmanned aerial vehicles (UAV) have been attracting the attention of researchers since their inception. This article provide a comprehensive review for the development of UAV technologies in the overhead electric power lines patrol process for monitoring and identifying faults, explores its advantages, and realizes the potential of the aforementioned method and how it can be exploited to avoid obstacles, especially when compared with the state-of-the-art mechanical methods. The review focuses on the development of advanced Learning Control strategies for higher manoeuvrability of the quadrotor. It also explores a suitable recharging strategies and motor control for improved mission autonomy.

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Section: A Current Methods Of Inspection Faults By Dnos and Tnosmentioning

confidence: 99%

An Advanced Unmanned Aerial Vehicle (UAV) Approach via Learning-Based Control for Overhead Power Line Monitoring: A Comprehensive Review

Foudeh

Luk²,

Whidborne

2021

IEEE Access

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“…Operation with fault easily leads to over voltage, resulting in multiple accidents, such as insulation breakdown and TV high voltage fuse breaking. Statistics show that single-phase ground fault is the most common fault type in neutral non-effectively grounded systems [2], accounting for more than 80% of distribution network faults [3]. Therefore, the faulty feeder must be quickly determined and cut off.…”

Section: Introductionmentioning

confidence: 99%

Deep Belief Network Based Faulty Feeder Detection of Single-Phase Ground Fault

et al. 2021

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“…Compared with the transmission system, the structure of the distribution system is more complicated and the operation situation is more variable. As a result, the distribution system is more prone to failure, and the single-phase to ground (SPG) fault occurs more often [1]. An SPG fault may result in rise of the voltage of the other two normal phase and even cause insulation breakdown, which presents a serious threat to personal and property safety.…”

Section: Introductionmentioning

confidence: 99%

Identifying single‐phase to ground fault based on line multi‐dimensional data in cyber‐physical distribution system

Chen

Yang

et al. 2020

IET Generation Trans & Dist

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Single‐phase to ground fault is the most common line fault in power distribution systems, which may result in an electrical fire and personal safety problems. Particularly, it is more difficult to identify the fault occurring in the system in which the neutral point is grounded via Petersen coil. This paper proposes a new scheme based on line multi‐dimensional data in cyber‐physical distribution system, where line multi‐dimensional data is used to extract the fault characteristics and thus to identify the faulty line. First, the circuit is analyzed and the difference‐like algorithm is used to extract the current signal difference value from the line multi‐dimensional data as the feature quantity. In order to reduce the impact of the measurement error on the result, the authors employ the deep neural networks to process the line multi‐dimensional data, and demonstrate that this method can effectively resist the error interference. A simulation based on a previously studied model has been carried out and the results show the superiority compared to the fifth harmonic identification method. In addition, the test results obtained in the Dynamic Simulation Laboratory of Huazhong University of Science and Technology also demonstrate the practicality of the scheme.

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Insights into applications of IEEE Standards for Ground Fault Protection in Industrial and Commercial Power Systems

Cited by 9 publications

References 18 publications

An Advanced Unmanned Aerial Vehicle (UAV) Approach via Learning-Based Control for Overhead Power Line Monitoring: A Comprehensive Review

An Advanced Unmanned Aerial Vehicle (UAV) Approach via Learning-Based Control for Overhead Power Line Monitoring: A Comprehensive Review

Deep Belief Network Based Faulty Feeder Detection of Single-Phase Ground Fault

Identifying single‐phase to ground fault based on line multi‐dimensional data in cyber‐physical distribution system

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