# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 11 Conclusions  
A comprehensive review of fault detection, classification, and location in transmission lines has been presented in this paper. A range of techniques and methods are presented in addition to representative works. Before introducing methods used in fault detection, classification and location, an overview of feature extraction methods are presented, the groundwork for fault identification algorithms. Various transforms along with dimensionality reduction techniques have also been discussed. Newly developed ideas and their comparison with some noteworthy aspects regarding fault detection are also discussed. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technique** & **Strength** & **Weakness** \\ \hline \multirow{8}{*}{SVM Technique} & SVM is a highly accurate approach. & \\ \cline{2-3}  & SVM works quite well even for non-linearly separable data in base feature space. & \\ \cline{2-3}  & The probability of misclassification is very low. & Demands for more size and speed for the testing and training \\ \cline{2-3}  & To reduce error bound, it maximizes the margin. & \\ \cline{2-3}  & Upper bound error does not affect the space dimension & Complexity is high in classification and thus large memory is required \\ \hline \multirow{8}{*}{Decision Tree} & Easy interpretation and understanding & When high uncertainty or a number of outcomes are involved, calculations become very complex. \\ \cline{2-3}  & Compatible with other available decision methods. & DT may suffer from over-fitting \\ \cline{2-3}  & Rules can be set easily & Information gain in DTs is biased in favor of those features which have more levels. \\ \hline \multirow{2}{*}{Wide-area Fault Location} & It performs both control and monitoring operations. & PMU placement is a tough task in power systems \\ \hline \multirow{8}{*}{Modal Transform} & It is not dependent on electrical values and frequency & \\ \cline{2-3}  & The single transformation matrix is for the three-phase system (identical for current and voltages) & Modal parameters are required \\ \cline{2-3}  & Transposition and non-transposition of electrical values are done by simple multiplication of matrices. No convolution methods are required. & Not reliable for complex structures \\ \hline \multirow{8}{*}{Deep Learning} & Best-in-class performance on problems that significantly outperforms other solutions in multiple domains. This is not by a little bit, but by a significant amount. & A large amount of data is required \\ \cline{2-3}  & DL reduces the need for feature engineering, one of the most time-consuming parts of machine learning practice. & DL is computationally expensive to train and takes weeks to train via hundreds of machines equipped with expensive graphical processing units (GPUs) \\ \cline{2-3}  & It is an architecture that can be adapted to new problems relatively with ease e.g., time series, languages, etc., are using techniques like convolutional neural networks, recurrent neural networks, long short-term memory, etc & Determining the topology/training method for DL is a black art with no theory \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cont. Machine learning-based methods are widely employed by the researchers for fault-type classifications. However, in addition to SVM, FIS, ANN, and DT, deep learning-based promising algorithms such as; CNN and RBM, are recommended for fault classification. Fault location finding algorithms are discussed with AI-based methods. Machine learning including deep learning methods is recommended for future FL finding methods due to increased involvement of communication and computation in transmission systems. Generalized strengths and weaknesses of different artificial intelligence and machine learning-based algorithms are discussed. A comparative survey on all three tasks; fault detection, classification, and the location is also presented in a tabulated form considering features, inputs, complexity, system used and results.