Abstract
In this paper, an interesting application of machine learning algorithms is presented. The main idea consists of applying both deep-learning and support vector machine supervised machine learning approaches to improve the quality and to guarantee the stability and the reliability of an electric power transmission system. These techniques are used mainly to detect, classify, and consequently locate faults in the electric power transmission network. To test the performance of the proposed techniques, the standard IEEE 14-bus power system is used. The fault free, the one fault and the multiple fault cases are investigated. Faults are applied to the IEEE 14-bus system and simulated using SimPowerSystems toolbox of Matlab. The accuracy score is used to compare the proposed techniques performances. Different results proved that studied machine learning methods made correct predictions. Nevertheless, the deep learning algorithm performances are proved while classifying all types of faults. Simulation results demonstrate that the deep learning technique can achieve an accuracy of 100% compared to the support vector machine which had an accuracy of 87%.
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Bouchiba, N., Kaddouri, A. (2023). Deep Learning and Support Vector Machine Algorithms Applied for Fault Detection in Electrical Power Transmission Network. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_56
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DOI: https://doi.org/10.1007/978-3-031-16075-2_56
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