Machine Learning Model Applications for Fault Detection and Classification in Distributed Power Networks

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Jose Eduardo Urrea Cabus
İsmail Hakkı Altaş


This paper compares various unsupervised feature extraction techniques and supervised machine learning models for fault detection and classification over a power distributed generation system. The modified IEEE 34 bus test feeder was implemented for the study case simulated through PowerFactory DigSILENT software. Data analysis results from three-phase voltages and currents collected were performed in Python. Simulation results confirm that by applying dimensionality reduction techniques as feature extraction and wavelet family selection adequately, a high identification and classification accuracy can be obtained, excluding the less essential characteristics and preventing the machine learning models from overfitting or underfitting the datasets.


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J. Urrea Cabus and İsmail Altaş, “Machine Learning Model Applications for Fault Detection and Classification in Distributed Power Networks”, DataSCI, vol. 4, no. 2, pp. 11-18, Jun. 2022.
Research Articles


[1] Karić A., Konjić T. Jahić A. Power System Fault Detection, Classification and Location using Artificial Neural Networks, In: Hadžikadić M., Avdaković S. (eds) Advanced Technologies, Systems, and Applications II. IAT 2017. Lecture Notes in Networks and Systems, vol 28. Springer, Cham. (2018)
[2] Y. Mo et al., Cyber–Physical Security of a Smart Grid Infrastructure, in Proceedings of the IEEE, vol. 100, no. 1, pp. 195-209, Jan. 2012, doi: 10.1109/JPROC.2011.2161428.
[3] H. Okumuş and F.M. Nuroglu, Wavelet Based Fault Detection and Classification Algorithm for a Real Distribution Feeder. EMITTER International Journal of Engineering Technology. 2019. 7. 10.24003/emitter.v7i1.382.
[4] Jamil, M., Sharma, S.K. and Singh, R. Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus 4, 334 (2015).
[5] H. A. Tokel, R. A. Halaseh, G. Alirezaei and R. Mathar, A new approach for machine learning-based fault detection and classification in power systems, 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2018, pp. 1-5, doi: 10.1109/ISGT.2018.8403343.
[6] Dehghani, Farzad & Khodnia, Fereydoun & Dehghan, Esfandiar. Fault location of unbalanced power distribution feeder with distributed generation using neural networks. CIRED - Open Access Proceedings Journal. 2017. 1134-1137. 10.1049/oap-cired.2017.0007.
[7] N. Shahid, S. A. Aleem, I. H. Naqvi and N. Zaffar, Support Vector Machine based fault detection & classification in smart grids, 2012 IEEE Globecom Workshops, Anaheim, CA, USA, 2012, pp. 1526-1531, doi: 10.1109/GLOCOMW.2012.6477812.
[8] A. V. Masa, S. Werben and J. C. Maun, Incorporation of data-mining in protection technology for high impedance fault detection, 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 2012, pp. 1-8, doi: 10.1109/PESGM.2012.6344893.
[9] S. Samantaray, D. Mishra and G. Joos, A Combined Wavelet and Data-Mining Based Intelligent Protection Scheme for Microgrid, 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 2018, pp. 1-1, doi: 10.1109/PESGM.2018.8586480.
[10] Chen, K., Huang, C. and He, J., Fault detection, Classification and location for transmission lines and distribution systems: a review on the methods. High Voltage,1:25,33,2016.
[11] C. Rudin et al., Machine Learning for the New York City Power Grid, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 2, pp. 328-345, Feb. 2012, doi: 10.1109/TPAMI.2011.108.
[12] T. S. Abdelgayed, W. G. Morsi and T. S. Sidhu, Fault Detection and Classification Based on Co-training of Semisupervised Machine Learning, in IEEE Transactions on Industrial Electronics, vol. 65, no. 2, pp. 1595-1605, Feb. 2018, doi: 10.1109/TIE.2017.2726961.
[13] C., Hwan Kim and R. Aggarwal, Wavelet transforms in power systems. II. Examples of application to actual power system transients, in Power Engineering Journal, vol. 15, no. 4, pp. 193-202, Aug. 2001, doi: 10.1049/pe:20010404.
[14] H. Okumuş And F. M. Nuroğlu, Power System Event Classification Based on Machine Learning, 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia And Herzegovina, pp.402-405, 2018.
[15] N. Toma, R. and Kim, J.-M. Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms. Appl.Sci. 2020, 10,5251.
[16] R., Papia and M., Debani, Support vector machine-based fault classification and location of a long transmission line. Engineering Science and Technology, an International Journal. 2016, 19. 10.1016/j.jestch.2016.04.001.
[17] Guo, Mou-Fa, Z. Xiao-Dan, C. Duan-Yu and Y. Nien-Che. Deep-Learning-Based Earth Fault Detection Using Continuous Wavelet Transform and Convolutional Neural Network in Resonant Grounding Distribution Systems. IEEE Sensors Journal.2017, PP. 1-1. 10.1109/JSEN.2017.2776238.
[18] Ray, P., Panigrahi, B.K. and Senroy, N., Hybrid methodology for fault distance estimation in series compensated transmission line. IET Gener. Transm. Distrib.,7:431-439.2013, /iet-gtd.2012.0243
[19] J. Jiang et al., A Hybrid Framework for Fault Detection, Classification, and Location—Part I: Concept, Structure, and Methodology, in IEEE Transactions on Power Delivery, vol. 26, no. 3, pp. 1988-1998, July 2011, doi: 10.1109/TPWRD.2011. 2141157.Qwqw
[20] M. Davoudi, V. Cecchi and J. R. Agüero, Effects of stiffness factor on bus voltage variations in the presence of intermittent distributed generation, 2015 North American Power Symposium (NAPS), Charlotte, NC, USA, 2015, pp. 1-6, doi: 10.1109/NAPS.2015.7335187.
[21] R. C. Dugan and W. H. Kersting, Induction machine test case for the 34-bus test feeder -description, 2006 IEEE Power Engineering Society General Meeting, Montreal, QC, Canada, 2006, pp. 4 pp.-, doi: 10.1109/PES.2006.1709506.
[22] K. Balamurugan, D. Srinivasan, and T. Reindl, Impact of Distributed Generation on Power Distribution Systems, Energy Procedia, Volume 25, 2012, Pages 93-100,ISSN1876-6102, j.egypro.2012.07.013.Sfds
[23] Sasa Mujovic, Snezana Vujosevic and Luka Vujosevic (2018) Zero-Sequence Voltage-based Method for Determination and Classification of Unloaded Overhead Line Operating Conditions at the Moment of Energization, Electric Power Components and Systems,46:2,162-176,DOI:10.1080/15325008.201 8.1433252
[24] Raschka S. and Mirjalili V., Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-learn, and TensorFlow, Packt Publishing,2017,ISBN1787125939/9781787125933
[25] Ankur A. Patel, Hands-On Unsupervised Learning Using Python, O’Reilly Media, Inc., 2019, ISBN9781492035640
[26] (Accessing date: 9 Feb 2021)
[27] (Accessing date: 14 Feb 2021)
[28] (Accessing date: 20 Feb 2021)