# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 4 Fault-Types Classification (FC)  
Classification of fault-type plays a vital role in protection for transmission lines, thus scholars have shown increased interest in developing robust, novel and precise fault-type classification approaches. Most of the available classification methods are based on classifiers and statistical learning theories [51] while some based on logical methods [52]. A development in fault classification techniques is highly dependent on improvements in machine learning and pattern recognition methods. A detailed review of fault-type classification techniques is presented in the subsequent section.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Algorithm** & **Input** & **Test System** & **Feature** & **Complexity** & **Result** & **Ref.** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Fuzzy-neuro} & \multirow{4}{*}{Fault current and voltage} & \multirow{4}{*}{220 kV, 177.4 km, 50 Hz} & Back-propagation and & & & \\  & & & & fuzzy controllers are & & & \\  & & & & employed. & & & \\ \cline{3-6}  & & & & & PSCAD/EMTDC used & & \\ \cline{3-6}  & & & & 177.4 km, 50 Hz & PSCAD/EMTDC used & & & \\ \cline{3-6}  & & & & 177.4 km, 50 Hz & PSCAD/EMTDC used & & & \\ \cline{3-6}  & & & & 50 Hz & High harmonic & & 10 ms & \\ \cline{3-6}  & & & & & components removed & & \\ \cline{3-6}  & & & & & via FFT. & & \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{DWT and ANNs} & \multirow{4}{*}{Current and voltage signals} & \multirow{4}{*}{60 Hz, 230 kV, 188 km} & The sampling & & & \\  & & & & frequency is 1.2 kHz. & & & \\ \cline{3-6}  & & & & & Normalization & & \\ \cline{3-6}  & & & & voltage and current & & & \\ \cline{3-6}  & & & & signals are from 1 to & & & \\ \cline{3-6}  & & & & 100\% with & & & \\ \cline{3-6}  & & & & & \(-1\). & & \\ \cline{3-6}  & & & & & & Db4 is mother & & \\ \cline{3-6}  & & & & wavelet. & & & \\ \cline{3-6}  & & & & & 720 fault cases are & & \\ \cline{3-6}  & & & & & considered. & & \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{WT and self-organized artificial neural waveforms} & \multirow{4}{*}{current and voltage} & \multirow{4}{*}{50 Hz, 500 kV, 200 miles} & 200 kHz is the & & & \\  & & & & sampling rate. & & & \\ \cline{3-6}  & & & & Db5 mother wavelet & & & accuracy is \\ \cline{3-6}  & & & & & is used to decompose & & \\ \cline{3-6}  & & & & the signal up to 3 & & & \\ \cline{3-6}  & & & & levels. & & & single line \\ \cline{3-6}  & & & & & & & \\ \cline{3-6}  & & & & & 3960 fault cases are & & \\ \cline{3-6}  & & & & & considered. & & \\ \cline{3-6}  & & & & & An adaptive & & \\ \cline{3-6}  & & & & resonance & & & 99.65\% \\ \cline{3-6}  & & & & & theory-based neural & & \\ \cline{3-6}  & & & & & network is used. & & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{Linear discriminant analysis (LDA) and WT} & \multirow{4}{*}{Current signals} & \multirow{4}{*}{100 km, 400 kV, 50 Hz} & WT is used to process & & & \\ \cline{3-6}  & & & & the current samples & & & \\ \cline{3-6}  & & & & up to 3 levels. & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & single-circuitTL & & & \\ \cline{3-6}  & & & & & set-up to 90\% of the & & \\ \cline{3-6}  & & & & TL. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different fault detection methods.