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Research Article

Fault Prognosis in Smart Distribution System with Distributed Generation Using Fast Fourier Transform Assisted Machine Learning

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References

  • D. Zheng, W. Zhang, S. Netsanet Alemu, P. Wang, G. T. Bitew, D. Wei, and J. Yue, “Chapter 1—The concept of microgrid and related terminologies,” in Microgrid Protection and Control, Academic Press, 2021, pp. 1–12.
  • S. Faazila Fathima, and L. Premalatha, “Protection strategies for AC and DC microgrid—a review of protection methods adopted in recent decade,” IETE. J. Res., Vol. 69, pp. 6573–89, 2021.
  • F. C. Sampaio, F. L. Tofoli, L. Silveira Melo, G. C. Barroso, R. F. Sampaio, and R. P. S. Lea˜o, “Adaptive fuzzy directional bat algorithm for the optimal coordination of protection systems based on directional overcurrent relays,” Electr. Power Syst. Res., Vol. 211, pp. 108619, 2022. DOI:10.1016/j.epsr.2022.108619.
  • D. Acharya, and D. K. Das, “An efficient optimizer for optimal overcurrent relay coordination in power distribution system,” Expert. Syst. Appl., Vol. 199, pp. 116858, 2022. DOI:10.1016/j.eswa.2022.116858.
  • N. Mohammad Zadeh, R. M. Chabanloo, and M. G. Maleki, “Optimal coordination of directional overcurrent relays considering two-level fault current due to the operation of remote side relay,” Electr. Power Syst. Res., Vol. 175, pp. 105921, 2019. DOI: 10.1016/j.epsr.2019.105921.
  • M. N. Alam, B. Das, and V. Pant, “Protection coordination scheme for directional overcurrent relays considering change in network topology and OLTC tap position,” Electr. Power Syst. Res., Vol. 185, pp. 106395, Aug. 2020.
  • M. El-kordy, A. El-fergany, and A. F. A. Gawad, “Various metaheuristic-based algorithms for optimal relay coordination: Review and prospective,” Arch. Comput. Methods Eng., Vol. 28, no. 5, pp. 3621–9, 2021. DOI:10.1007/s11831-020-09516-z.
  • A. Srivastava, J. Mani Tripathi, S. R. Mohanty, and B. Panda, “Optimal over-current relay coordination with distributed generation using hybrid particle swarm optimization–gravitational search algorithm,” Electr. Power Compon. Syst., Vol. 44, no. 5, pp. 506–17, 2016. DOI:10.1080/15325008.2015.1117539.
  • A. Srivastava, and S. K. Parida, “Data driven approach for fault detection and Gaussian process regression based location prognosis in smart AC microgrid,” Electr. Power Syst. Res., Vol. 208, pp. 107889,  Jul. 2022.
  • P. I. N. Barbalho, V. A. Lacerda, R. A. S. Fernandes, and D. V. Coury, “Deep reinforcement learning-based secondary control for microgrids in islanded mode,” Electr. Power Syst. Res., Vol. 212, pp. 108315, 2022. DOI:10.1016/j.epsr.2022.108315.
  • R. K. Jalli, S. P. Mishra, P. K. Dash, and J. Naik, “Fault analysis of photovoltaic based DC microgrid using deep learning randomized neural network,” Appl. Soft. Comput., Vol. 126, pp. 109314, 2022. DOI:10.1016/j.asoc.2022.109314.
  • R. Aiswarya, D. S. Nair, T. Rajeev, and V. Vinod, “A novel SVM based adaptive scheme for accurate fault identification in microgrid,” Electr. Power Syst. Res., Vol. 221, pp. 109439, 2023. DOI:10.1016/j.epsr.2023.109439.
  • L. M. Kandasamy, and K. Jaganathan, “Intelligent fault diagnosis using deep learning for a microgrid with high penetration of renewable energy sources,” Electr. Power Compon. Syst., Vol. 51, no. 4, pp. 332–50, 2023. DOI:10.1080/15325008.2023.2168091.
  • D. K. J. S. Jayamaha, N. W. A. Lidula, and A. D. Rajapakse, “Wavelet-multi resolution analysis based ANN architecture for fault detection and localization in DC microgrids,” IEEE. Access., Vol. 7, pp. 145371–84, 2019. DOI:10.1109/ACCESS.2019.2945397.
  • B. Roy, et al., “Deep learning based relay for online fault detection, classification, and fault location in a grid-connected microgrid,” IEEE. Access, Vol. 11, pp. 62674–96, 2023.
  • J. B. Thomas, S. G. Chaudhari, K. V. Shihabudheen, and N. K. Verma, “CNN-based transformer model for fault detection in power system networks,” IEEE Trans. Instrum. Meas., Vol. 72, pp. 1–10, 2023. DOI:10.1109/TIM.2023.3238059.
  • S. Dutta, S. Kumar Sahu, M. Roy, and S. Dutta, “A data driven fault detection approach with an ensemble classifier based smart meter in modern distribution system,” Sustain. Energy, Grids Netw., Vol. 34, pp. 101012, 2023. DOI:10.1016/j.segan.2023.101012.
  • F. M. Almasoudi, “Enhancing power grid resilience through real-time fault detection and remediation using advanced hybrid machine learning models,” Sustainability, Vol. 15, no. 10, p. 8348, 2023. DOI:10.3390/su15108348.
  • S. K. Sahu, M. Roy, S. Dutta, D. Ghosh, and D. K. Mohanta, “Machine learning based adaptive fault diagnosis considering hosting capacity amendment in active distribution network,” Electr. Power Syst. Res., Vol. 216, pp. 109025, 2023.
  • S. Dutta, S. Kumar Sahu, S. Dutta, and B. Dey, “Leveraging a micro synchrophasor for fault detection in a renewable based smart grid—A machine learned sustainable solution with cyber-attack resiliency,” e-Prime - Adv. Electric. Eng., Electron. Energy, Vol. 2, pp. 100090, 2022.
  • M. O. Faruq Goni, et al., “Fast and accurate fault detection and classification in transmission lines using extreme learning machine,” e-Prime - Adv.Electric. Eng., Electron. Energy, Vol. 3, pp. 100107, 2023.
  • E. A. Bhuiyan, M. Azad Akhand, S. R. Fahim, S. K. Sarker, and S. K. Das, “A deep learning through DBN enabled transmission line fault transient classication framework for multimachine microgrid systems,” Int. Trans. Electric. Energy Syst., Vol. 2022, pp. 12, 2022.
  • Y. Jiang, T. Zhou, Y. Tang, and Y. Wang, “Twiddle-factor-based DFT algorithm with reduced memory access,” in Proceedings 16th International Parallel and Distributed Processing Symposium, Ft. Lauderdale, FL, 2002, p. 8. DOI:10.1109/IPDPS.2002.1015572.
  • I. W. Selesnick and G. Schuller, “The discrete Fourier transform,” in The Transform and Data Compression Handbook, K. R. Rao and P. C. Yip, Eds. Boca Raton: CRC Press LLC, 2001.
  • A. K. Gangwar, O. P. Mahela, B. Rathore, B. Khan, H. H. Alhelou, and P. Siano, “A novel k -means clustering and weighted k -NN- regression-based fast transmission line protection,” IEEE Trans. Ind. Inf., Vol. 17, no. 9, pp. 6034–43, Sept. 2021. DOI:10.1109/TII.2020.3037869.
  • V. Ashok, and A. Yadav, “A novel decision tree algorithm for fault location assessment in dual-circuit transmission line based on DCT-BDT approach,” in Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, 941, A. Abraham, A. Cherukuri, P. Melin, and N. Gandhi, Eds. Cham.: Springer, 2020.
  • P. Virdi, Y. Narayan, P. Kumari, and L. Mathew, “Discrete wavelet packet based elbow movement classification using fine Gaussian SVM,” in 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, 2016, pp. 1–5. DOI:10.1109/ICPEICES.2016.7853657.
  • N. Zhang, J. Xiong, J. Zhong, and K. Leatham, “Gaussian process regression method for classification for high-dimensional data with limited samples,” in 2018 Eighth International Conference on Information Science and Technology (ICIST), 2018, pp. 358–63. DOI:10.1109/ICIST.2018.8426077.
  • P. Stefanidou-Voziki, N. Sapountzoglou, B. Raison, and J. L. Dominguez-Garcia, “A review of fault location and classification methods in distribution grids,” Electr. Power Syst. Res., Vol. 209, Aug. 2022. DOI:10.1016/j.epsr.2022.108031.
  • Y. Jiang, “Data-Driven fault location of electric power distribution systems with distributed generation,” IEEE Trans. Smart Grid, Vol. 11, no. 1, pp. 129–37, Jan. 2020.

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