A Comparative Review of Fault Location/Identification Methods in Distribution Networks

I, Abdul Haleem M; Sharma, Madhu; Sajan, K. S.; Babu, K. N. Dinesh

doi:10.1109/aresx.2018.8723300

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…In this respect, eleven types of fault (AG, BG, CG, AB, AC, BC, AB-G, AC-G, BC-G, ABC, ABC-G) and also ten fault resistance in the case of ground short-circuit fault R = 0, 20,50,75,100,125,150,175,200 ), five inception angles • (including 0 • , 45 • , 90 • , 135 • ,180 • ) and nineteen distances of fault from recording point (including 500m, 1km,1.5km, 2km, 2.5km, 3km, 3.5km, 4km, 4.5km, 5km, 5.5km, 6km, 6.5km, 7km, 7.5km, 8km, 8.5km, 9km, 9.5km) are simulated. Figs.…”

Section: Atp/emtp Simulated Fault Resultsmentioning

confidence: 99%

“…Ultimately the accuracy of the fault identification techniques is reduced as well. As a result, more accurate fault locating or identifying techniques need to be explored by addressing all the inherent behaviors of the DNs [20], [21]. An advanced signal processing technique called the Stockwell Transform (ST) that combines the advantages of wavelet transform and short-time Fourier Transform (FT) was employed recently to detect and classify faults in distribution grids in [22], but not employed in locating faults and identifying faulty sections in distribution grids.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Fault Classification and Location for Underground Power Cable of Nuclear Facilities

et al. 2022

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Worldwide, Nuclear Power Plants (NPPs) must have higher security protection and precise fault detection systems, especially underground power cable faults, to avoid causing national disasters and keep on safe national ratios of electricity production. Hence, this paper proposes an automatic, effective, and accurate Deep Learning (DL)-based fault classification and location technique for these cables via a One-dimensional Convolutional Neural Network (1D-CNN) and a Binary Support Vector Machine (BSVM). The proposed approach includes four main steps: data collection, feature extraction and reduction, fault detection, and fault classification and location. Signal collection from the underground cable's sending end is performed via the Alternating Transient Program/Electromagnetic Transient Program (ATP/EMTP). Feature extraction and reduction are performed via Fractional Discrete Cosine Transform (FrDCT) and Singular Value Decomposition (SVD) methods. Fault detection is performed through leveraging BSVM with the linear Kernel method in the third step. Finally, this permits 1D-CNN to classify the fault type and locate it. Simulation results confirmed the efficiency of our proposed method, especially for 11kV underground cable faults, including different fault resistances and inception angles. Moreover, the proposed technique is applicable in real-time scenarios with a 99.6% accuracy rate, 0.15sec lowest execution time, and 0.095% maximum error rate for fault location at fractional factor (α) equals to 0.8.

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Section: Atp/emtp Simulated Fault Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Fault Classification and Location for Underground Power Cable of Nuclear Facilities

et al. 2022

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“…Most of the transient faults restorations lead to an open circuit fault in the OHL, as the reclosing surges weaken and open the OHL jumper connections between the overhead line poles. The four primary types of short circuit faults-line to ground (LG), line to line (LL), double line to ground (DLG), and three line to ground (LLLG)-are regarded as permanent faults [23].…”

Section: Fault Eventsmentioning

confidence: 99%

Realistic μPMU Data Generation for Different Real-Time Events in an Unbalanced Distribution Network

2023

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Monitoring, protection, and control processes are becoming more complex as distributed energy resources (DERs) penetrate distribution networks (DNs). This is due to the inherent nature of power DNs and the bi-directional flow of current from various sources to the loads. To improve the system’s situational awareness, the grid dynamics of the entire DER integration processes must be carefully monitored using synchronized high-resolution real-time measurement data from physical devices installed in the DN. μPMUs have been introduced into the DN to help with this. In comparison to traditional measurement devices, μPMUs can measure voltage, current, and their phasors, in addition to frequency and rate of frequency change (ROCOF). In this study, an approach to generating realistic event data for a real utility DN utilizing strategically installed μPMUs is proposed. The method employs an IEEE 34 test feeder with 12 μPMUs installed in strategic locations to generate real-time events-based realistic μPMU data for various situational awareness applications in an unbalanced DN. The node voltages and line currents were used to analyze the various no-fault and fault events. The author generated the data as part of his PhD research project, utilizing his real-time utility grid operation experience to be used for various situational awareness and fault location studies in a real unbalanced DN. The DN was modeled in DIgSILENT PowerFactory (DP) software. The generated realistic μPMU data can be utilized for developing data-driven algorithms for different event-detection, classification and section-identification research works.

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“…Protective relays can detect breaker states [1,2], types of electrical faults [3,4], and fault locations [5][6][7] in a few seconds. Different protection functions [8] of relays are available for a variety of applications such as to trip/close breakers [9], to locate electrical faults in power grids or lines [10], and detecting faulted phases [4]. Protective relays have front panels with light-emitted diodes (LEDs) that represent the ground and A, B, and C phase states to identify the types of electrical faults [11].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Phase-to-Ground Fault Apparent Admittance Method with Phase/Ground Boundaries to Detect Types of Electrical Faults for Protective Relays Using Signature Library and Simulated Events

Piesciorovsky

Rodriguez

2022

International Transactions on Electrical Energy Systems

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Protective relays in electric power grids recognize the types of electrical faults in a few seconds. The most common detection method to detect the types of electrical faults is based on measuring the angle between the zero and negative sequence currents. However, it is not completely accurate because the phase-to-phase-ground and phase-to-ground electrical faults could have the same detection conditions. Therefore, engineers need to plot the events after an electrical fault to observe the nature of the incidents in detail. In this study, the phase-to-ground fault apparent (PGFA) admittance method with phase/ground boundaries identified the types of electrical faults located in distribution power lines and feeders. This method was based on measuring the PGFA admittance magnitudes for the faulted and nonfaulted phases, resulting in greater than zero and near zero, respectively. The PGFA admittance algorithm was built with MATLAB/Simulink software and tested with signature library and grid simulation events. The PGFA method with phase/ground boundaries was evaluated with the confusion matrix. The measured and predicted values matched in more than 90% of the tests, and the PGFA admittance method with phase/ground boundaries presented an accuracy of 94.3% and a precision of 100%.

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A Comparative Review of Fault Location/Identification Methods in Distribution Networks

Cited by 10 publications

References 32 publications

Deep Learning-Based Fault Classification and Location for Underground Power Cable of Nuclear Facilities

Deep Learning-Based Fault Classification and Location for Underground Power Cable of Nuclear Facilities

Realistic μPMU Data Generation for Different Real-Time Events in an Unbalanced Distribution Network

Assessment of the Phase-to-Ground Fault Apparent Admittance Method with Phase/Ground Boundaries to Detect Types of Electrical Faults for Protective Relays Using Signature Library and Simulated Events

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