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Saeed, Abdulhassan & Khudair | 41
Fig.4: Parallel arc fault simulation model. The arc current waveform in these figures of series and
Start of fault parallel arc faults is identical to a sinusoidal signal, but it
varies slowly in the zero-current region, which is defined as
Fig .5: Series arc current waveform. 'zero-rest' and is also known as (flat shoulders) as shown in
Start of fault Fig.5 and 6. In the time shown above, the output current is
extremely near to zero; it falls before reaching a value of
Fig .6: Parallel arc current waveform. over-zero, but changes slowly until it reaches a value of
over-zero.
IV. CONVENTIONAL FAST FOURIER TRANSFORM
METHOD FOR ARC DETERMINING
The Fast Fourier Transform is a mathematical method
that transforms data from the time domain into the frequency
domain. Simply put, the vertical axis is still amplitude, but
it is now measured against frequency rather than time [12].
The Fast Fourier transform (FFT) is a catch-all term for a
variety of algorithms. In contrast to the direct measurement
of the DFT, they all have a lower computational complexity.
The Cooley-Tukey algorithm [13], which decomposes the
original DFT into a set of smaller DFTs, is the most widely
used FFT algorithm. Two ways of decomposition are
decimation-in-frequency (DIF) and decimation-in-time
(DIT). They achieve speed, as with all FFTs, by reusing the
effects of smaller, intermediary computations to calculate
several DFT frequency outputs. The radix-2 Decimation in
Time (DIT) FFT algorithm method is used in this case.
Radix-2 DIT was selected because of its straightforward and
easy-to-implement form.
IV. WAVELET DETECTION
The series and parallel When a single-phase series and
parallel arc fault happen, the arc voltage and current
waveforms show considerable mutation and a large
singularity in the fault time. It is capable of extracting fault
information from complex transient waveforms and
calculating the fault moment. The Fourier transform is a
fundamental technique for investigating function
singularity. It can only identify the general nature of the
singularity due to the lack of spatial localization, making it
difficult to estimate the distribution of unique points in
space [12], [13]. To circumvent the disadvantages of the
Fourier transform, wavelet method is implemented to
properly localize the arc moment.
Because the CWT will give a lot of data redundancy by
accounting for every conceivable scale and shift step, it
turns out that choosing these steps on a dyadic basis will
make the analysis more efficient and space-saving [14], 15].
This concept has been used in the discrete wavelet
transform (DWT), which is a strong practical filtering
approach that allows for a quick wavelet transformation.
Signal analysis applications as seen in a previous
subsection may require the low frequency constituents of
the signal, other applications may require the high
frequency ones. It is for that reason, the conventions
approximations and details are usually common to the
DWT. Approximations represent the low frequency large
scale constituents of the signal, whereas details represent
the high frequency small scale signal constituents [14].
