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(n 2 )) 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.

This paper studies a pursuit-evasion problem involving a single pursuer and a single evader, where we are interested in developing a pursuit strategy that doesn't require continuous, or even periodic, information about the position of the evader. We propose a self-triggered control strategy that allows the pursuer to sample the evader's position autonomously, while satisfying desired performance metric of evader capture. The work in this paper builds on the previously proposed self-triggered pursuit strategy which guarantees capture of the evader in finite time with a finite number of evader samples. However, this algorithm relied on the unrealistic assumption that the evader's exact position was available to the pursuer. Instead, we extend our previous framework to develop an algorithm which allows for uncertainties in sampling the information about the evader, and derive tolerable upper-bounds on the error such that the pursuer can guarantee capture of the evader. In addition, we outline the advantages of retaining the evader's history in improving the current estimate of the true location of the evader that can be used to capture the evader with even less samples. Our approach is in sharp contrast to the existing works in literature and our results ensure capture without sacrificing any performance in terms of guaranteed time-to-capture, as compared to classic algorithms that assume continuous availability of information.

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