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at translations of 0 to 30. Then followed by the 20Hz component, second highest frequency and so on. The 5Hz component appears at the end of the translation axis and at higher scales (lower frequencies).
Now, remember that, these resolution properties unlike the STFT which has a constant resolution at all times and frequencies, the WT has a good time and poor frequency resolution at high frequencies, and good frequency and poor time resolution at low frequencies. Figure 2.7shows the same WT in Figure 2.6 from another angle to better illustrate the resolution properties: In Figure 2.7, lower scales (higher frequencies) have better scale resolution (narrower in scale, which means that it is less ambiguous what the exact value of the scale) which correspond to poorer frequency resolution. Similarly, higher scales have scale frequency resolution (wider support in scale, which means it is more ambitious what the exact value of the scale is), which correspond to better frequency resolution of lower frequencies.
The axes in Figure 2.7 and 2.8 are normalized and should be evaluated accordingly.
Roughly speaking the 100 points in the translation axis correspond to 1000 ms, and the 150 points on the scale axis correspond to a frequency band of 40 Hz (the numbers on the translation and scale axis do not correspond to seconds and Hz, respectively, they are just the number of samples in the computation).
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The illustration in Figure 2.9 is commonly used to explain how time and frequency resolutions should be interpreted. Every box in Figure 2.9 corresponds to a value of the wavelet transform in the time-frequency plane. Note that boxes have a certain non-zero area, which implies that the value of a particular point in the time-frequency plane cannot be known. All the points in the time-time-frequency plane that falls into a box are represented by one value of the WT.
Figure 2.9: Time – Frequency relationship graph (Bashier E. 2016) and Mauresh R., 2014)
Taking a closer look at Figure 2.9, first, we will notice that, although the widths and heights of the boxes change, the area is constant. That is each box represents an equal portion of the time-frequency plane, but giving different proportions to time and frequency. Note that at low frequencies, the height of the boxes are shorter (which corresponds to better frequency resolutions, since there is less ambiguity regarding the value of the exact frequency), but their widths are longer (which correspond to poor time resolution, since there is more ambiguity regarding the value of the exact time). At higher frequencies the width of the boxes decreases, i.e., the time resolution
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gets better, and the heights of the boxes increase, (the frequency resolution gets poorer).
Before we conclude this section, it is important to say how the partition looks like in the case of STFT. Recall that in STFT the time and frequency resolutions are determined by the width of the analysis window, which is selected once for the entire analysis, (both time and frequency resolutions are constant). Therefore the time-frequency plane consists of squares in the STFT case.
Regardless of the dimensions of the boxes, the areas of all boxes, both in STFT and WT, are the same. In summary, the area of a box is fixed for each window function (STFT) or mother wavelet (CWT), whereas different windows or mother wavelets can result in different areas. However, all areas are lower bounded by 1/4pi. That is, we cannot reduce the areas of the boxes as much as we want due to the Heisenberg's uncertainty principle. On the other hand, for a given mother wavelet the, dimensions of the boxes can be changed, while the area remain the same. This is exactly what wavelet transform does (Patel M. and Patel R. N., 2012).
Dhanashri D. et al (2016) used DWT to detect fault on the transmission line and ANN to classify the faults. In their work, a fault was introduced between bus 4 and 1 ranged at a distance of 0.25meters at equal four locations and with inception angle of 0, 45, and 90 degrees. They simulated a power system using PSCAD, ATP and MATLAB/SIMULINK and got results. Their results were compared with that of 9 – bus IEEE network in PSCAD, ATP and MATLAB/SIMULINK.
In their work, the generated voltage and current were recorded after simulation in PSCAD and then decomposed in Matlab using DWT in various frequency bands
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ranges. They also calculated various energy levels of the DWT voltage and current output results and used them as inputs to the ANN as classifier.
All the fault cases were shown in their work, but with concentration to the fault case with distance 0.5m. Finally, the faults cases were classified using ANN and found out that between the three softwares used, PSCAD gave clearer and better classified results with ANN than other softwares.
Similar to Dhanashri’s work, Prakash K. et al (2017) simulated a 230KV, 50Hz power system transmission line with Matlab/Simulink and used WT to decompose the voltage and current signal obtained from it. The WT detects the instant faults based on filtering them through a set of low – pass and high – pass filters. The voltage and current signals obtained after the filtering for A – B and ABCG faults clearly indicate that fault has been detected as compared with the standard fault characteristics and normal condition.
Azah M et al (2018) used continuous wavelet transform (CWT) and S – Transform for fault detection of multiple power quality disturbance, incipient fault and prediction of voltage sag sources that originates from u investpstream or downstream. According to their analysis, S – Transform proved very effective in analysis and detection of the multiple power quality and incipient faults.
Bilal M. and Umar S. used only current signal as input data for their investigation of unsymmetrical and three phase fault on the transmission line using DWT. They used five (5) level decompositions and obtained its approximation and detail coefficient.
Their results show that when the system is working under normal operating condition, the normalized values are less than the faulty threshold signal values.
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In their work, Preethi M. and Manik H. used a positive sequence component as input data to DWT. The maximum coefficients of the positive sequence current obtained after simulation of faults from all buses were compared with that of normal condition in order to detect the faulty bus on the line. However, the algorithm was able to detect the fault with high accuracy.