Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection

Shengchao Jian,Haoliang Yuan,Chun Sing Lai,Xiangang Peng,Loi Lei Lai

doi:10.3390/app11177804

Shengchao Jian, Haoliang Yuan + Show 3 more

Open Access

https://doi.org/10.3390/app11177804

Copy DOI

Abstract

Fault-cause identification plays a significant role in transmission line maintenance and fault disposal. With the increasing types of monitoring data, i.e., micrometeorology and geographic information, multiview learning can be used to realize the information fusion for better fault-cause identification. To reduce the redundant information of different types of monitoring data, in this paper, a hierarchical multiview feature selection (HMVFS) method is proposed to address the challenge of combining waveform and contextual fault features. To enhance the discriminant ability of the model, an ε-dragging technique is introduced to enlarge the boundary between different classes. To effectively select the useful feature subset, two regularization terms, namely l2,1-norm and Frobenius norm penalty, are adopted to conduct the hierarchical feature selection for multiview data. Subsequently, an iterative optimization algorithm is developed to solve our proposed method, and its convergence is theoretically proven. Waveform and contextual features are extracted from yield data and used to evaluate the proposed HMVFS. The experimental results demonstrate the effectiveness of the combined used of fault features and reveal the superior performance and application potential of HMVFS.

Highlights

Single-view learning is represented via best single view (BSV) method, through which the most informative view achieves the best performance among views
Ensemble models promote fault-cause identification by combining individual learners [22], so we explored the performance of various ensemble models, including random forest (RF), AdaBoosting, stacking ensemble and dynamic ensemble
A novel hierarchical multiview feature selection method based on an ε-dragging technique and sparsity regularization is proposed to perform hierarchical feature selection with multiview data

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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 crews attach great importance to fault location for line patrol and manual inspection. On-site inspection is labor-intensive and depends on subjective judgment. Cause identification after inspection is too late for dispatchers to give better instructions according to the external cause, such as forced energization

Methods

Findings

Discussion

Conclusion