Characterization of 400 Volt High Impedance Fault With Current and Magnetic Field Measurements

Sifat, Anwarul Islam; McFadden, Fiona Stevens; Bailey, Joseph; Rayudu, Ramesh; Hunze, Arvid

doi:10.1109/tpwrd.2020.3044545

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…For the low fault-current stages (initial stage and sizzling stage) the load current dominates, and the fault current characteristics cannot be readily observed in either the current or magnetic field measurements. A more detailed analysis of this data is given in [12], where we have applied discrete Fourier Transform and Hilbert Transform algorithms to characterize the frequency and transeient characteristic of the TB-HIF stages.…”

Section: Sensors and Data Acquisitionmentioning

confidence: 99%

Monitoring and Detection of Low-current High-Impedance Faults in Distribution Networks

Sifat¹,

McFadden²,

Rayudu³

et al. 2022

Preprint

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Faults in electricity distribution networks have the potential to ignite fires, cause electrocution, and/or damage the system itself. High-current Low-Impedance Faults (LIF) are typically detected and mitigated via over-current, distance, directional relays, fuses, etc. In contrast, while High Impedance Faults (HIF) are equally hazardous, they are much more challenging to detect due to the fault current being much lower than load currents and their time-varying and nonlinear behaviour. A solution for HIF detection will require, firstly, the continuous observation of system current waveforms and, secondly, the development of sophisticated pattern recognition algorithms to apply to these measurements. However, New Zealand distribution networks are extensive and largely unmonitored beyond the substation, and suitable HIF detection schemes are still an ongoing research challenge.Robinson Research Institute has been developing a solution to enable widespread monitoring of overhead lines in distribution networks and detecting faults. The sensing system we have developed and are evaluating utilizes magnetic field sensors (specifically the Giant Magneto Resistive type), pole-mounted at a distance from the overhead lines for ease of installation. These sensors are an attractive option compared to traditional current transformers (CTs) due to their non-contact sensing ability, wide frequency bandwidth, low cost, miniature size, and ease of integration with required digital componentry. Coupled with the algorithms we have developed, these sensors allow observation of the individual overhead line currents and detect both Low Impedance and High Impedance faults.To date, we have built a physical test facility for power system fault analysis and the development and evaluation of our sensing and fault detection system. We have simulated LIF and HIF with different fault surface materials and load-switching events. From the data collected, we have characterized the unique fault behaviour for both LIF and HIF in 400V networks and trained a Deep Learning classifier to recognize the type of fault present from its unique signature. We have developed an outdoor pole-mountable sensing system and have installed this in Wellington Electricity's network for ongoing data collection and evaluation. This paper will describe the test facility and our experience developing and implementing the sensing system. The widest range of HIF phenomena observed was in the fault experiments involving the tree branch. For brevity, therefore, this paper reports on the results of just these tree-branch experiments. HIF faults on other surface materials will be reported elsewhere. Finally, we will detail the pole-mountable sensing system installed in Wellington Electricity's network and the outcomes thus far.

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Section: Sensors and Data Acquisitionmentioning

confidence: 99%