CLASSIFICATION OF ELECTRICAL FAULT SEVERITY IN A MODERN POWER SYSTEM OPERATING ENVIRONMENT
DOI:
https://doi.org/10.11113/jurnalteknologi.v86.19980Keywords:
Electrical power systems, faults, severity classification, artificial intelligence algorithms, reliability, IEEE 9-busAbstract
Electrical power systems frequently experience different kinds of faults while they are used on a daily basis. Therefore, it is crucial to classify faults according to their severity in order to keep the system operating reliably. In this study, a novel method for categorising the severity of faults in the stability of the power system into three cases namely Minor, Moderate, and Major Fault was presented. This method is based on cutting-edge artificial intelligence algorithms. Under different types of faults, the suggested methodology was used in IEEE 9-bus. The study's findings give network operators important information that they can use to spot electrical system weaknesses during serious faults and maintain the power system's dependability and continuity of energy flow.
References
S. I. Khalel, N. H. Aziz, and M. A. Al-Flaiyeh. 2022. Smart Grid Application in the Iraqi Power System: Current and Future Challenges. Bulletin of Electrical Engineering and Informatics. 11: 3042-3050.
https://doi.org/10.11591/eei.v11i6.4099.
F. H. Jufri, V. Widiputra, and J. Jung. 2019. State-of-the-art Review on Power Grid Resilience to Extreme Weather Events: Definitions, Frameworks, Quantitative Assessment Methodologies, and Enhancement Strategies. Applied Energy. 239: 1049-1065.
https://doi.org/10.1016/j.apenergy.2019.02.017.
A. Karić, T. Konjić, and A. Jahić. 2017. Power System Fault Detection, Classification and Location using Artificial Neural Networks. International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies. 89-101.
https://doi.org/10.1007/978-3-319-71321-2_8.
H. Haes Alhelou, M. E. Hamedani-Golshan, T. C. Njenda, and P. Siano. 2019. A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges. Energies. 12: 682.
https://doi.org/10.3390/en12040682.
N. Masoudvaziri, P. Ganguly, S. Mukherjee, and K. Sun. 2020. Integrated Risk-informed Decision Framework to Minimize Wildfire-induced Power Outage Risks: A County-level Spatiotemporal Analysis. Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management conference, Venice Italy.
https://doi.org/10.3850/978-981-14-8593-0_4243-cd.
C. M. Furse, M. Kafal, R. Razzaghi, and Y.-J. Shin. 2020. Fault Diagnosis for Electrical Systems and Power Networks: A Review. IEEE Sensors Journal. 21: 888-906.
https://doi.org/10.1109/JSEN.2020.2987321.
Y. G. Paithankar and S. Bhide. 2022. Fundamentals of Power System Protection. PHI Learning Pvt. Ltd.
S. R. Fahim, S. K. Sarker, S. Muyeen, S. K. Das, and I. Kamwa. 2021. A Deep Learning based Intelligent Approach in Detection and Classification of Transmission Line Faults. International Journal of Electrical Power & Energy Systems. 133: 107102.
https://doi.org/10.1016/j.ijepes.2021.107102.
R. Bhandia, J. de Jesus Chavez, M. Cvetković, and P. Palensky. 2020. High Impedance Fault Detection using Advanced Distortion Detection Technique. IEEE Transactions on Power Delivery. 35: 2598-2611.
https://doi.org/10.1109/TPWRD.2020.2973829.
W. Sima, H. Zhang, and M. Yang. 2022. Edge-cloud Collaboration Detection Approach for Small-sample Imbalanced Faults in Power Lines. IEEE Transactions on Instrumentation and Measurement.
https://doi.org/10.1109/TIM.2022.3175262.
M. Azeroual, Y. Boujoudar, K. Bhagat, L. El Iysaouy, A. Aljarbouh, A. Knyazkov, et al. 2022. Fault Location and Detection Techniques in Power Distribution Systems with Distributed Generation: Kenitra City (Morocco) as a Case Study. Electric Power Systems Research. 209: 108026.
https://doi.org/10.1016/j.epsr.2022.108026.
C. Li, A. M. Gole, and C. Zhao. 2018. A Fast DC Fault Detection Method using DC Reactor Voltages in HVdc Grids. IEEE Transactions on Power Delivery. 33: 2254-2264.
https://doi.org/10.1109/TPWRD.2018.2825779.
Cai, N. F. Thornhill, and B. C. Pal. 2017. Multivariate Detection of Power System Disturbances based on Fourth Order Moment and Singular Value Decomposition. IEEE Transactions on Power Systems. 32: 4289-4297.
https://doi.org/10.1109/TPWRS.2016.2633321.
S. S. Gururajapathy, H. Mokhlis, and H. A. Illias. 2017. Fault Location and Detection Techniques in Power Distribution Systems with Distributed Generation: A Review. Renewable and Sustainable Energy Reviews. 74: 949-958.
https://doi.org/10.1016/j.rser.2017.03.021.
S. Silva, P. Costa, M. Gouvea, A. Lacerda, F. Alves, and D. Leite. 2018. High Impedance Fault Detection in Power Distribution Systems using Wavelet Transform and Evolving Neural Network. Electric Power Systems Research. 154: 474-483,
https://doi.org/10.1016/j.epsr.2017.08.039.
J. Tavoosi, M. Shirkhani, A. Azizi, S. U. Din, A. Mohammadzadeh, and S. Mobayen. 2022. A Hybrid Approach for Fault Location in Power Distributed Networks: Impedance-based and Machine Learning Technique. Electric Power Systems Research. 210: 108073.
https://doi.org/10.1016/j.epsr.2022.108073.
I. Mousaviyan, S. G. Seifossadat, and M. Saniei. 2022. Traveling Wave-based Algorithm for Fault Detection, Classification, and Location in STATCOM-Compensated Parallel Transmission Lines. Electric Power Systems Research. 210: 108118.
https://doi.org/10.1016/j.epsr.2022.108118.
[18] S. Wang, C. Zhuang, Y. Geng, T. Wang, B. Luo, and R. Zeng. 2021. Fault location method based on edge detection for low SNR traveling waves. Electric Power Systems Research. 201: 107505.
https://doi.org/10.1016/j.epsr.2021.107505.
H. Bernardes, M. Tonelli-Neto, and C. R. Minussi. 2021. Fault Classification in Power Distribution Systems Using Multiresolution Analysis and a Fuzzy-ARTMAP Neural NetworkAnalysis and a Fuzzy-ARTMAP Neural Network. IEEE Latin America Transactions. 19: 1824-1831.
https://doi.org/10.1109/TLA.2021.9475615.
A. E. L. Rivas and T. Abrao. 2020. Faults in Smart Grid Systems: Monitoring, Detection and Classification. Electric Power Systems Research. 189: 106602.
https://doi.org/10.1016/j.epsr.2020.106602.
B. Patel. 2023. Superimposed Components of Lissajous Pattern based Feature Extraction for Classification and Localization of Transmission Line Faults. Electric Power Systems Research. 215: 109007.
https://doi.org/10.1016/j.epsr.2022.109007.
F. Rafique, L. Fu, and R. Mai. 2021. End to End Machine Learning for Fault Detection and Classification in Power Transmission Lines. Electric Power Systems Research. 199: 107430.
https://doi.org/10.1016/j.epsr.2021.107430.
A. Mukherjee, P. K. Kundu, and A. Das. 2021. Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods. Journal of The Institution of Engineers (India): Series B. 102: 855-877.
https://doi.org/10.1007/s40031-020-00530-0.
A. A. Eshkaftaki, A. Rabiee, A. Kargar, and S. T. Boroujeni. 2019. An Applicable Method to Improve Transient and Dynamic Performance of Power System Equipped with DFIG-based Wind Turbines. IEEE Transactions on Power Systems. 35: 2351-2361.
https://doi.org/10.1109/TPWRS.2019.2954497.
S. I. Khalel, M. A. A. M. Al-Rawe, and A. A. M. Alabbawi. 2021. Dynamic Security Assessment for the Power System in the Presence of Wind Turbines. Indonesian Journal of Electrical Engineering and Informatics (IJEEI). 9: 563-574.
https://doi.org/10.52549/.v9i3.3010.
P. S. Kundur and O. P. Malik. 2022. Power System Stability and Control. McGraw-Hill Education.
A. R. Al-Roomi. 2015. Power Flow Test Systems Repository. Halifax, Nova Scotia, Canada.
S. Hussain, R. Atallah, A. Kamsin, and J. Hazarika. 2018. Classification, Clustering and Association Rule Mining in Educational Datasets using data Mining Tools: A Case Study. Computer Science On-line Conference. 196-211.
https://doi.org/10.1007/978-3-319-91192-2_21.
J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez. 2020. A Comprehensive Survey on Support Vector Machine Classification: Applications, Challenges and Trends. Neurocomputing. 408: 189-215.
https://doi.org/10.1016/j.neucom.2019.10.118.
E. Y. Boateng, J. Otoo, and D. A. Abaye. 2020. Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review. Journal of Data Analysis and Information Processing. 8: 341-357.
https://doi.org/10.4236/jdaip.2020.84020.
S. S. Azmi and S. Baliga. 2020. An Overview of Boosting Decision Tree Algorithms Utilizing AdaBoost and XGBoost Boosting Strategies. Int. Res. J. Eng. Technol. 7.
R. Yacouby and D. Axman. 2020. Probabilistic Extension of Precision, Recall, and f1 Score for More Thorough Evaluation of Classification Models. Proceedings of The First Workshop on Evaluation and Comparison of NLP Systems. 79-91.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.