Transmission line fault-cause classification based on multi-view sparse feature selection

Abstract

With the development of communication and monitoring techniques, multi-source information in form of multi-view data is expected to support fault-cause identification for transmission line maintenance and fault disposal. However, the combined used of multi-view data poses a great challenge of information fusion. In respond to this challenge, this paper proposes a multi-view sparse feature selection (MVSFS) method to combine contextual and waveform features for fault-cause classification. l2,1-norm sparsity regularization is adopted to select discriminative features across two views and an ɛ-dragging technique is integrated into the regression model to enhance its discriminant ability. Subsequently, an iteration optimization algorithm is devised to solve the model. Experiments on a real-life fault dataset demonstrate that contextual and waveform features can achieve higher accuracy than single view learning only through appropriate fusion, and the proposed MVSFS can overcome the drawback of conventional feature selection methods and make a great improvement of classification performance with multi-view data.

Keywords

• Fault-cause classification
• Transmission line
• Sparse learning
• Multi-view learning
• Feature selection