Programs
- M. Tech. in Automotive Engineering -
- Clinical Fellowship in Laboratory Genetics & Genomics - Fellowship
Publication Type : Journal Article
Publisher : Springer
Source : Journal of Control, Automation and Electrical Systems
Url : https://link.springer.com/article/10.1007/s40313-024-01119-4
Campus : Bengaluru
School : School of Artificial Intelligence
Year : 2024
Abstract : Power system interruptions, though brief, carry significant costs and adverse consequences. Ensuring a reliable power supply necessitates accurate fault detection and swift resolution through fault classification and localization. The existing individual and joint approaches for fault classification and localization involve comprehensive expert-guided feature engineering and pre-processing algorithms depending on the selected classifier. This research proposes a fully automated, integrated multi-task deep learning-based framework called the Fault-MTL (multi-task learning) model for performing fault classification and localization at the same time. The multi-task feature interdependency accelerates the conduction of tasks with better performance. To the best of our knowledge, this is the first-of-its-kind study where the multi-task learning (MTL)-based deep model is developed for power system studies. To evaluate the model, we tested it on three state-of-the-art transmission line topologies, each with various initial conditions and simulated fault data variations in terms of fault type, distance, inception angle, and resistance. The tenfold cross-validation is used to measure the performance. The simulation experiments exhibit an exemplary performance by attaining the perfect validation in fault classification and the minimal mean average error of 1.59% in fault localization, across all three adapted topologies. For investigating the feasibility of the model, the ablation studies including the effect of noise, comparison with the existing state-of-the-art methods and the performance comparison of the work with the studies from the literature are performed in individual and combined simulation experiments of fault classification and fault localization.
Cite this Research Publication : Dewesh Bhardwaj, Narendra D. Londhe, and Ritesh Raj, Fault-MTL: A Multi-task Deep Learning Approach for Simultaneous Fault Classification and Localization in Power Systems, Journal of Control, Automation and Electrical Systems, 35, pp. 884-898, 2024.