Support Vector Machine based fault detection & classification in smart grids

Abstract

Smart Grids have recently attracted the attention of many profound research groups with their ability to create an automated and distributed energy level delivery. Computational Intelligence (CI) has been incorporated into various aspects of the smart grids, including fault detection and classification, which is a key issue in all the power systems. This paper presents two novel techniques for fault detection and classification in power Transmission Lines (TL). The proposed approaches are based on One-Class Quarter-Sphere Support Vector Machine (QSSVM). The first technique, Temporal-attribute QSSVM (TA-QSSVM), exploits the temporal and attribute correlations of the data measured in a TL for fault detection during the transient stage. The second technique is based on a novel One-Class SVM formulation, named as Attribute-QSSVM (A-QSSVM), that exploits attribute correlations only for automatic fault classification. The results indicate a detection and classification accuracy as high as 99%. Significant reduction (from O(n 4) to O(n2)) in computational complexity is achieved as compared to the state-of-the-art techniques, which use Multi-Class SVM for fault classification. Moreover, unlike state-of-the-art techniques, both of these techniques are unsupervised and online and can be implemented on the existing monitoring infrastructure for online monitoring, fault detection and classification in power sytems. © 2012 IEEE.