## 4 Classification and Analysis of the Input Data
### Transformation and Reduction in Input Data  
Data processing essentially includes data preparation in an extensive sense, i.e., data cleaning, supplementing, extracting attributes, transformation and reduction, and many other less important actions. The speed of the next ANN layer is faster because the input data are smaller than when there is no processing. The flaw of processing is the slower operation of the input data layer due to the action. Table 3 shows different transformation methods in input data processing that use complex mathematics, the most common of which is the discrete wavelet transformation (DWT). The authors of the most recent papers use input data processing. The transformation and reduction in input data aim to present the input data as more compact as an algorithm that is sophisticated and computationally demanding and calculates the problem faster and easier. A certain share of information is lost during data reduction and transformation. The focus is on data reduction and transformation using a transformation, although the reduction process can be carried out in several different ways. What the transformation does is that it turns one column of data into several columns of shorter length and less complexity, i.e., it makes more than one dimension, which is the case with DWT. The essence of the transformation is dual, i.e., to reduce the complexity and increase the transferability of data. \begin{table}
\begin{tabular}{c c c} \hline
**Measurements** & \multicolumn{2}{c}{**Simulation**} \\ \hline Papers & Software & Papers \\ \hline \multirow{5}{*}{[49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70]} & DlgSILENT & [49; 50; 51; 81] \\ \cline{2-3}  & ATP & [52] \\ \cline{2-3}  & PSIM & [53] \\ \cline{2-3}  & MATLAB & [54; 59; 78; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91] \\ \cline{2-3}  & No name & [44; 65; 69; 72; 73; 92] \\ \hline \end{tabular}
\end{table} 
Table 2: Sources of input data. \begin{table}
\begin{tabular}{c c} \hline
**Transform** & **Papers** \\ \hline Discrete wavelet transform (DWT) & [45; 47; 49; 54; 55; 59; 65; 66; 72; 75; 80; 83; 88; 89; 97] \\ \hline Wavelet transform (WT) & [48; 59; 62; 73; 78; 87; 89; 98; 99] \\ \hline S-transform (ST) & [41; 47; 52; 54; 100] \\ \hline Wavelet packet transform (WPT) & [77] \\ \hline Fast Fourier transform (FFT) & [58] \\ \hline Discrete Fourier transform (DFT) & [44; 65; 85; 101] \\ \hline Slantlet transform (SLT) & [41; 54; 84] \\ \hline Hilbert transform (HT) & [82; 84] \\ \hline Continuous wavelet transform (CWT) & [56] \\ \hline Tunable Q wavelet transform (TQWT) & [69] \\ \hline Phase space technique (PST) & [49] \\ \hline \end{tabular}
\end{table} 
Table 3: Transformation of input data. Fourier transformation provides a representation of a signal in the frequency domain. This is useful for analyzing the frequency components of a signal, which is essential for understanding the characteristics of the input data. Fourier transformation is widely used to analyze the non-stationary signal, but computational time can be slow due to the complexity. The authors of [61] used DFT to transform the data in order to speed up standard FT. The symmetrical components of the second harmonic of voltage and current signals were used as input to MPNN. The second harmonics of voltage and current signals are almost zero under normal conditions, whereas in islanding, the symmetrical components of the second harmonic of voltage and current signals have remarkable values. The wavelet transform provides a representation of a signal in the time and frequency domain. Wavelet transformation has a high computational speed due to less complexity than Fourier transformation. Batch processing, high-frequency integration, and being heavily affected by noise are the drawbacks. The authors of [54] used DWT of the currents and voltages to capture unique signatures that can be used to reveal the cause of the corresponding transient signals.