Representational and sequential feature learning model for fault classification in power system

References

• Abiodun, O. I., A. Jantan, A. E. Omolara, K. V. Dada, A. M. Umar, O. U. Linus, H. Arshad, A. A. Kazaure, U. Gana, and M. U. Kiru. 2019. “Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition.” Institute of Electrical and Electronics Engineers Access 7:158820–158846. http://dx.doi.org/10.1109/access.2019.2945545.
   Google Scholar
• Albawi, S., T. A. Mohammed, S. Al-Zawi. (2018) “Understanding of a Convolutional Neural Network.” Proc. 2017 Int. Conf. Eng. Technol. ICET 2017. 2018-Janua, 1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186.
   Google Scholar
• Alzubaidi, L., J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie, and L. Farhan. 2021. “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions.” Journal of Big Data 8 (1). https://doi.org/10.1186/s40537-021-00444-8.
   PubMedGoogle Scholar
• Baskar, D., and P. Selvam. 2020. “Machine Learning Framework for Power System Fault Detection and Classification.” International Journal of Scientific & Technology Research 9 (2): 2002–2008.
   Google Scholar
• Brown, M., M. Biswal, S. Brahma, S. J. Ranade, H. Cao. (2016) “Characterizing and Quantifying Noise in PMU Data.” In 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 1–5.
   Google Scholar
• Chen, K., C. Huang, and J. He. 2016. “Fault Detection, Classification and Location for Transmission Lines and Distribution Systems: A Review on the Methods.” High Voltage 1 (1): 25–33. https://doi.org/10.1049/hve.2016.0005.
   Web of Science ®Google Scholar
• Chen, X., B. Zhang, and D. Gao. 2021. “Bearing Fault Diagnosis Base on Multi-Scale CNN and LSTM Model.” Journal of Intelligent Manufacturing 32 (4): 971–987. https://doi.org/10.1007/s10845-020-01600-2.
   Web of Science ®Google Scholar
• Garrett, P. M. 2017. “Appendix A: Soc.” Work Irish People Britain 169–170. https://doi.org/10.2307/j.ctt1t8935r.15.
   Google Scholar
• Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. United Kingdom: MIT Press.
   Google Scholar
• Ioffe, S., and C. Szegedy. 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. 32nd Int.” Conference Machine Learning ICML 2015 (1): 448–456.
   Google Scholar
• Kingma, D. P., and J. L. Ba. 2015. “Adam: A Method for Stochastic Optimization.” In 3rd International Conference for Learning Representations, San Diego, 1–15. https://doi.org/10.48550/arXiv.1412.6980.
   Google Scholar
• Magagula, X. G., Y. Hamam, J. A. Jordaan, A. A. Yusuff. (2017) Fault Detection and Classification Method Using DWT and SVM in a Power Distribution Network. Proc. - 2017 IEEE PES-IAS PowerAfrica Conf. Harnessing Energy, Inf. Commun. Technol. Afford. Electrif. Africa, PowerAfrica 2017. 1–6. https://doi.org/10.1109/PowerAfrica.2017.7991190.
   Google Scholar
• Majid, M. A., Z. A. Huneiti, W. Balachandran, and Y. B. King. 2013. “Matlab As a Teaching and Learning Tool for Mathematics: A Literature Review.” International Journal Arts Science 6 (3): 23–44.
   Google Scholar
• Mehta, V. K., R. Mehta. 2005. “Variable Load on Power Stations.” In Principles of Power System: Including Generation, Transmission, Distribution, Switchgear and Protection: for B.E/B.Tech., AMIE and Other Engineering Examinations, 41–68. S. Chand Publishing.
   Google Scholar
• Nagi, J., F. Ducatelle, G. A. Di Caro, D. Cireşan, U. Meier, A. Giusti, F. Nagi, J. Schmidhuber, L. M. Gambardella. (2011) Max-Pooling Convolutional Neural Networks for Vision-Based Hand Gesture Recognition. 2011 IEEE Int. Conf. Signal Image Process. Appl. ICSIPA 2011. 342–347. https://doi.org/10.1109/ICSIPA.2011.6144164.
   Google Scholar
• Naik, S., and E. Koley. 2019. “Fault Detection and Classification Scheme Using KNN for AC/HVDC Transmission Lines.” Proceedings 4th International Conference Communications Electronic Systems ICCES 2019:1131–1135. https://doi.org/10.1109/ICCES45898.2019.9002230.
   Google Scholar
• Özcanlı, A. K., and M. Baysal. 2022. “A Novel Multi-LSTM Based Deep Learning Method for Islanding Detection in the Microgrid.” Electric Power Systems Research 202:107574. https://doi.org/10.1016/j.epsr.2021.107574.
   Web of Science ®Google Scholar
• Qu, N., Z. Li, J. Zuo, and J. Chen. 2020. “Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge.” Institute of Electrical and Electronics Engineers Access 8:87060–87070. https://doi.org/10.1109/ACCESS.2020.2992790.
   Google Scholar
• Rai, P., N. D. Londhe, and R. Raj. 2021. “Fault Classification in Power System Distribution Network Integrated with Distributed Generators Using CNN.” Electric Power Systems Research 192:106914. https://doi.org/10.1016/j.epsr.2020.106914.
   Web of Science ®Google Scholar
• Recioui, A., B. Benseghier, and H. Khalfallah. 2016. “Power System Fault Detection, Classification and Location Using the K-Nearest Neighbors. 2015 4th Int.” Conference Electronic Engineering ICEE 2015. https://doi.org/10.1109/INTEE.2015.7416832.
   Google Scholar
• Shadi, M. R., M. T. Ameli, and S. Azad. 2022. “A Real-Time Hierarchical Framework for Fault Detection, Classification, and Location in Power Systems Using PMUs Data and Deep Learning.” International Journal of Electrical Power & Energy Systems 134:107399. https://doi.org/10.1016/j.ijepes.2021.107399.
   Web of Science ®Google Scholar
• Shi, S., B. Zhu, S. Mirsaeidi, and X. Dong. 2019. “Fault Classification for Transmission Lines Based on Group Sparse Representation.” IEEE Transactions on Smart Grid 10 (4): 4673–4682. https://doi.org/10.1109/TSG.2018.2866487.
   Web of Science ®Google Scholar
• Tikariha, A., B. N. Bag, N. D. Londhe, R. Raj. (2021) Fault Classification in an IEEE 30 Bus System Using Convolutional Neural Network. In: 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, India, 57–61. https://doi.org/10.1109/RDCAPE52977.2021.9633569.
   Google Scholar
• Tikariha, A., N. D. Londhe, B. Bag, and R. Raj. 2021. “Classification of Faults in an IEEE 30 Bus Transmission System Using Fully Convolutional Network.” International Transactions on Electrical Energy Systems 31 (11). https://doi.org/10.1002/2050-7038.13134.
   Web of Science ®Google Scholar
• Van Houdt, G., C. Mosquera, and G. Nápoles. 2020. “A Review on the Long Short-Term Memory Model.” Artificial Intelligence Review 53 (8): 5929–5955. https://doi.org/10.1007/s10462-020-09838-1.
   Web of Science ®Google Scholar
• Yadav, A., and Y. Dash. 2014. “An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination.” Advances in Artificial Neural Systems 2014:1–20. https://doi.org/10.1155/2014/230382.
   Google Scholar
• Zhang, J., Z. Y. He, S. Lin, Y. B. Zhang, and Q. Q. Qian. 2013. “An ANFIS-Based Fault Classification Approach in Power Distribution System.” International Journal of Electrical Power & Energy Systems 49:243–252. https://doi.org/10.1016/j.ijepes.2012.12.005.
   Web of Science ®Google Scholar