Series And Parallel Arc Fault Detection Based on Discrete Wavelet VS. FFT Techniques

Arc problems are most commonly caused by electrical difficulties such as worn cables and improper connections. Electrical fires are caused by arc faults, which generate tremendous temperatures and discharge molten metal. Every year, flames of this nature inflict a great lot of devastation and loss. A novel approach for identifying residential series and parallel arc faults is presented in this study. To begin, arc faults in series and parallel are simulated using a suitable simulation arc model. The fault characteristics are then recovered using a signal processing technique based on the fault detection technique called Discrete Wavelet Transform (DWT), which is built in MATLAB/Simulink. Then came db2, and one level was discovered for obtaining arc-fault features. The suitable mother and level of wavelet transform should be used, and try to compare results with conventional methods (FFT-Fast Fourier Transform). MATLAB was used to build and simulate arc-fault models with these techniques.

Download Full-text

Series and Parallel Arc Fault Detection in Electrical Buildings Based on Discrete Wavelet Theory

Iraqi Journal for Electrical And Electronic Engineering ◽

10.37917/ijeee.17.2.11 ◽

2021 ◽

Vol 17 (2) ◽

pp. 94-101

Author(s):

Elaf Saeed ◽

Khalid Abdulhassan ◽

Osama Al-Atbee

Keyword(s):

Wavelet Transform ◽

Fault Detection ◽

Detection Algorithm ◽

Discrete Wavelet ◽

Wavelet Theory ◽

Matlab Simulink ◽

Fault Features ◽

Parallel Circuits ◽

Common Causes ◽

In Series

Electrical issues such as old wires and faulty connections are the most common causes of arc faults. Arc faults cause electrical fires by generating high temperatures and discharging molten metal. Every year, such fires cause a considerable deal of destruction and loss. This paper proposes a new method for detecting residential series and parallel arc faults. A simulation model for the arc is employed to simulate the arc faults in series and parallel circuits. The fault features are then retrieved using a signal processing approach called Discrete Wavelet Transform (DWT) designed in MATLAB/Simulink based on the fault detection algorithm. Then db2 and one level were found appropriate mother and level of wavelet transform for extracting arc-fault features. MATLAB Simulink was used to build and simulate the arc-fault model.

Download Full-text

Application of the wavelet transform to sediment grain sizes analysis with an impact plate for bedload monitoring in sediment bypass tunnels

E3S Web of Conferences ◽

10.1051/e3sconf/20184004022 ◽

2018 ◽

Vol 40 ◽

pp. 04022 ◽

Cited By ~ 1

Author(s):

Takahiro Koshiba ◽

Tetsuya Sumi

Keyword(s):

Wavelet Transform ◽

Processing Technique ◽

Bedload Transport ◽

Signal Denoising ◽

Discrete Wavelet ◽

Signal Processing Technique ◽

Counting System ◽

Grain Sizes ◽

Impact Plate ◽

The Impact

An impact plate (IP) is a bedload transport monitoring device developed for a part of the sediment bypass tunnels management. In the measurement, the impact produced by bedload is recorded as the number of impulses (Ip) which is widely used in Japan. Ip, however, has several shortcomings attributed to the insufficient raw signal denoising. In this study, the discrete wavelet transform (DWT), an advanced signal processing technique especially for noisy, non-periodical, and transient signals, was introduced to devise an improved Ip count system solving the problems in the original signal denoising process. The presented results revealed that the DWT is useful for water noise reduction, signal overlap reduction, and mitigating Ip saturation at grain sizes Ds = 50 and 100 mm compared to the original Ip counting system.

Download Full-text