Distribution System Fault Detection through Feature-Summarizing Matrix: A Case Study

Wang, Anqi; Wang, Yue

doi:10.1109/pesgm41954.2020.9281780

Cited by 1 publication

(1 citation statement)

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“…The authors of [27] present a fault detection method that uses a summarized matrix of the characteristics of the power system, RMS voltage information provided by the Hilbert transform, harmonic information obtained by the discrete Fourier transform, and a detection method based on the Hilbert-Huang transform. A typical 10.5 kV radial distribution network with three feeders is used to demonstrate, through simulations, the performance of the proposed fault detection method compared to several classical methods.…”

Section: Introductionmentioning

confidence: 99%

Differential Analysis of Fault Currents in a Power Distribution Feeder Using abc, αβ0, and dq0 Reference Frames

2022

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This paper proposes a methodology to monitor the instantaneous value of the current and its derivative in the abc, αβ0, and dq0 reference frames to act in the detection of fault current in medium-voltage distribution systems. The method employed to calculate the derivative was Euler’s, with processing sampling rates of 10, 50, 100, and 200 μs. Using the MATLAB/Simulink platform, fault situations were analyzed on a real feeder of approximately 1.1132 km in length, fed by an 11.4 kV source, composed of 26 unbalanced loads and modeled as constant power. The simulation results show that the detection occurred in the different fault situations implemented in the feeder and that the detection speed is related to the value of the processing sampling rate (PSR) used. Considering all fault situations and regardless of the PSR value used, the total average detection time was 49 µs. Besides that, the joint action of the detection system with the Thyristor Controlled Series Capacitor (TCSC) limited the fault current in each situation. The average detection time for each fault situation analyzed was below the typical time for a recloser to act, regardless of the reference adopted for the analysis.

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Section: Introductionmentioning

confidence: 99%