Detection of High Impedance Faults in Medium Voltage Distribution Networks Using Discrete Wavelet Transform

This paper proposes an analysis of high-impedance fault detection algorithms for medium voltage distribution lines based on the discrete wavelet transform (DWT) technique and a more advanced technique named independent component analysis (ICA) independently. Three-phase distribution line model and two diodes high impedance fault model, which represents the unsymmetrical fault current of electric arc, simulated using MATLAB/Simulink. High impedance fault (HIF) detection algorithm initially analyzes the sampled current waveforms through DWT and the resultant third level high-frequency components “d3” coefficients are analyzed through one cycle moving window approach. The proposed algorithm successfully detects any HIF in the distribution current even if there is a slight or no difference in the amplitude of the HIF and the waveform of the phase current. On the other hand, the ICA more developed algorithm than DWT successfully separated the noise signals from the obtained current waveforms and HIF noise signals can be differentiated with non-HIF noise signals. Because of this reason ICA is chosen in this research. The detected HIF current can be from 50 ma and up.

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A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

International Transactions on Electrical Energy Systems ◽

10.1002/2050-7038.12378 ◽

2020 ◽

Vol 30 (6) ◽

Author(s):

Veerapandiyan Veerasamy ◽

Noor Izzri Abdul Wahab ◽

Arangarajan Vinayagam ◽

Mohammad Lutfi Othman ◽

Rajeswari Ramachandran ◽

...

Keyword(s):

Wavelet Transform ◽

Power System ◽

Discrete Wavelet Transform ◽

Discrete Wavelet ◽

Graphical Language ◽

High Impedance ◽

High Impedance Fault

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High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

10.20944/preprints201810.0687.v1 ◽

2018 ◽

Author(s):

Veerapandiyan Veerasamy ◽

Noor Izzri Abdul Wahab ◽

Rajeswari Ramachandran ◽

Muhammad Mansoor ◽

Mariammal Thirumeni

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Discrete Wavelet ◽

Inference System ◽

High Impedance ◽

Neuro Fuzzy ◽

High Impedance Fault ◽

Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

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