# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 1 Introduction
Studies have shown that waveform and contextual features can achieve high accuracy without each other, but there are high data requirements. For economic and operational reasons, data condition will not change significantly in the short term. It is necessary to study performance improvement for fault-cause identification based on current data conditions. One of the challenges is determining how to combine waveform features and multisource contextual features. This is an information fusion problem, and the simplest approach is feature concatenation. The authors of [23] tried to combine contextual features and waveform features as a mixed vector, but concatenated features reduce performance. Moreover, in contrast to focusing on either side, a few studies use both waveform and contextual characteristics for higher classification performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Article**} & \multicolumn{6}{c}{**Waveform Characteristics**} & \multirow{2}{*}{\
\begin{tabular}{c} **Time Char-** \\ **acteristics** \\ \end{tabular} } & **External Characteristics** & \multirow{2}{*}{
\begin{tabular}{c} **Classification** \\ **Methods** \\ \end{tabular} } \\  & **Singal** & **Sequence** & **Spectrum** & **Phase Angle** & **Time Char-** & **External Characteristics** & **Classification Methods** \\  & **Amplitude** & **Component** & **Analysis** & **Phase Angle** & **Accision Tree** & **Accision Tree** \\ \hline \hline \end{tabular}  
* [PERSON], [PERSON] [6]
* [PERSON], [PERSON] [7]
* [PERSON], [PERSON] [8; 9; 10]
* [PERSON], [PERSON] [11]
* [PERSON], [PERSON] [12]
* [PERSON], [PERSON] [14]
* [PERSON], [PERSON] [15]
* [PERSON], [PERSON] [20]
* [PERSON], [PERSON] [21]
* [PERSON], [PERSON] [22]
* [PERSON], [PERSON] [23]  
\end{table} 
Table 1: A summarized list of characterization and classification methods used for fault-cause identification. To tackle the fusion challenge, multiview learning (MVL) is introduced in this paper because waveform and contextual features describe the same fault event in different views. MVL aims to integrate multiple-view data properly and overcome biases between multiple views to obtain satisfactory performance. One of typical MVL methods is canonical correlation analysis (CCA), which maps multiview features into a common feature space [24]. Instead of mapping features, multiview feature selection that selects features from each view is preferred in fault-cause identification. Unlike traditional feature selection, multiview feature selection treats multiview data as inherently related and ensures that complementary information from other views is exploited [25; 26]. In [27], a review on real-time power system data analytics with wavelet transform is given. The use of discrete wavelet transform was used to identify the high impedance fault and heavy load conditions [28]. The authors of [29] propose a fault diagnosis approach for the main drive chain in a wind turbine based on data fusion. To deal with the kind of multivariable fault diagnosis problem for which input variables need to be adjusted for different typical faults, the deep autoencoder model is adopted for the fault diagnosis model training for different typical fault types. In this paper, we propose a hierarchical multiview feature selection (HMVFS) method for transmission line fault-cause identification. Two view datasets are composed of the waveform features and the contextual features. Our proposed HMVFS is applied to conduct the feature selection for the optimal feature combination. In our model, to enhance the discriminant ability of regression, an \(\varepsilon\)-dragging technology is used to enlarge the margin between classes. Next, two regularization terms, namely \(l_{2,1}\)-norm and Frobenius norm (\(F\)-norm) penalty, are adopted to perform the hierarchical feature selection. Here, the \(l_{2,1}\)-norm realizes the row sparsity to reduce the unimportant features of each view and the \(F\)-norm realizes the view-level sparsity to reduce the diversity between these two-view data.