# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 1 Introduction  
Transmission lines cover a wide area and work in diverse outdoor environments to achieve long-distance, high-capacity power transmission. In order to maintain stable power supply, high-speed fault diagnosis is indispensable for line maintenance and fault disposal. Traditional fault diagnosis technologies concerning fault detecting, fault locating, and phase selection are well developed [1, 2], while diagnosis on external causes is still underdeveloped. Operation covers attack great importance to fault location for line patrol and manual inspection. However, on-site inspection is labor-intensive and depends on subjective judgment. Moreover, cause identification after inspection is too late for dispatchers to give better instructions according to the external cause, such as forced energization. Fault-cause identification is expected to help dispatch and maintenance personnel make a proper and speedy fault response. Transmission line faults are more often triggered by external factors due to environmental change or surrounding activities. Though the cause categories are slightly different between regions or institutions, the common causes can be listed as lighting, tree, animal contact, fire, icing, pollution and external damage [3]. Considering complexity and variability of open-air work, it is hard to model fault scenarios for diverse root causes [4, 5]. Thus, these existing studies on line fault-cause identification have been developed based on data-driven methods rather than physical modeling. The early identification methods were rule-based, such as statistical analysis, CN2 rule induction [6] and fuzzy inference system (FIS) [7; 8; 9]. Their identification frameworks are finally presented in the form of logic flow, demanding a great degree of robustness and generality for their rules or thresholds. In recent years, various machine learning (ML) techniques that attach great importance to hand-crafted features have been applied to diagnose external causes [10; 11; 12; 13; 14], such as logistic regression (LR), artificial neural network (ANN), k-nearest neighbor (KNN) and support vector machine (SVM). Deep learning (DL) provides a more efficient way in the field of fault identification. In [15], deep belief network (DBN) is used as the classification algorithm after extracting time-frequency characteristics from traveling wave data. Even when using DL methods, feature engineering is still an inevitable part to achieve high accuracy. Feature signature study provides knowledge about fault information and plays a critical role in fault-cause identification. On the one hand, when fault events happen, power quality monitors (PQMs) enable us to have easy access to electrical signals and time stamps [16]. Time-domain features extracted from fault waveform and time stamp were used to construct logic flow to classify lightning-, animal- and tree-induced faults [6]. To exploit transient characteristics in the frequency domain, signal processing techniques such as wavelet transform (WT) and empirical mode decomposition (EMD) are used for further waveform characteristic analysis [17; 18; 19; 20]. In [21], a fault waveform was characterized based on the time and frequency domain to develop an identification logic. However, a fault waveform is easily affected by the system operation state, and there is no direct connection between these characteristics and external causes. On the other hand, weather condition is directly relevant to many fault-cause categories such as lightning, icing and wind. With the development of monitoring equipment and communication technology, dispatchers now can make judgments with more and more outdoor information [22]. These nonwaveform characteristics such as time stamps, environment attributes and other textual data are called contextual characteristics in this paper. Table 1 lists and compares the characterization and classification methods in existing works.