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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References
Sharifi, A., Ahmadi, M., Ala, A.: The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic. Environ. Sci. Pollut. Res. 28(34), 46964–46984 (2021). https://doi.org/10.1007/s11356-021-15292-5
Helm, J.M., et al.: Machine learning and artificial intelligence: definitions, applications, and future directions. Curr. Rev. Musculoskelet Med. 13(1), 69–76 (2020). https://doi.org/10.1007/s12178-020-09600-8
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)
Wang, Z., Li, C., Shao, H., Sun, J.: Eye recognition with mixed convolutional and residual network (MiCoRe-Net). IEEE Access 6, 17905–17912 (2018)
Li, C., Wang, Z., Qi, H.: Fast-converging conditional generative adversarial networks for image synthesis. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2132–2136. IEEE (2018)
Wang, Z., Li, C., Wang, X.: Convolutional neural network pruning with structural redundancy reduction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14913–14922 (2021)
Liu, X., Pei, D., Lodewijks, G., Zhao, Z., Mei, J.: Acoustic signal-based fault detection on belt conveyor idlers using machine learning. Adv. Powder Technol. 31, 2689–2698 (2020)
Sabo, A., et al.: Artificial intelligence-based power system stabilizers for frequency stability enhancement in multi-machine power systems. IEEE Access (2021). https://doi.org/10.1109/ACCESS.2021.3133285
Abid, A., Khan, M.T., Iqbal, J.: A review on fault detection and diagnosis techniques: basics and beyond. Artif. Intell. Rev. 54(5), 3639–3664 (2020). https://doi.org/10.1007/s10462-020-09934-2
Zhakov, A.: Application of ANN for fault detection in overhead transport systems for semiconductor fab. IEEE Trans. Semicond. Manuf. (2020). https://doi.org/10.1109/TSM.2020.2984326
Gomes, I., Wolf, D.: Health monitoring system for autonomous vehicles using dynamic Bayesian networks for diagnosis and prognosis. J. Intell. Rob. Syst. 101(1), 1–21 (2020). https://doi.org/10.1007/s10846-020-01293-y
Kawauchi1, K., et al.: A convolutional neural network-based system to classify patients using FDG PET/CT examinations. BMC Cancer 20, 227 (2020). https://doi.org/10.1186/s12885-020-6694-x
Chelouati, N., Fares, F., Bouslimani, Y., Ghribi, M.: Lobster detection using an embedded 2D vision system with a FANUC industrual robot. In: 2021 IEEE International Symposium on Robotic and Sensors Environments, pp. 1–6 (2021). https://doi.org/10.1109/ROSE52750.2021.9611755
Biddle, L., Fallah, S.: A novel fault detection, identification and prediction approach for autonomous vehicle controllers using SVM. Automot. Innov. 4(3), 301–314 (2021). https://doi.org/10.1007/s42154-021-00138-0
Toma, R.N., Prosvirin, A.E., Kim, J.-M.: Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors 20, 1884 (2020). https://doi.org/10.3390/s20071884
Tamrakar, A.K., Koley, E.: A SVM based fault detection and section identification scheme for a hybrid AC/HVDC transmission line with wind farm integration. IEEE (2020). 978-1-7281-8873-7/20
Oumri, M.: Diagnostic des défauts de réseaux électriques filaires par la réflectométrie, Ph.D thesis, Université Paris Sud (2014)
Khan, A.Q., Ullah, Q., Sarwar, M., Gul, S.T., Iqbal, N.: Transmission line fault detection and identification in an interconnected power network using phasor measurement units. Int. Fed. Autom. Control 51(24), 1356–1363 (2018)
Adhikari, S., Sinha, N., Dorendrajit, T.: Fuzzy logic based on-line fault detection and classification in transmission line. Springerplus 5(1), 1–14 (2016). https://doi.org/10.1186/s40064-016-2669-4
Goswami, T., Roy, U.B.: Predictive model for classification of power system faults using machine learning. In: IEEE Region 10 Conference (TENCON) (2019). https://doi.org/10.1109/TENCON.2019.8929264
Jamil, M., Sharma, S.K., Singh, R.: Fault detection and classification in electrical power transmission system using artificial neural network. Springerplus 4(1), 1–13 (2015). https://doi.org/10.1186/s40064-015-1080-x
Jayatunga, U., Perera, S., Ciufo, P., Agalgaonkar, A.: Voltage unbalance emission assessment in interconnected power systems. IEEE Trans. Power Delivery (2013). https://doi.org/10.1109/TPWRD.2013.2274659
Boudreaux, J.A.: Design, simulation, and construction of an IEEE 14-bus power system. Master’s theses, Louisiana State University LSU Digital Commons, 4801 (2018)
Dantuo, J.: Load flow analysis of IEEE 14 bus test system. MATLAB Central File Exchange (2020)
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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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