The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks
Abstract
Fault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks. The proposed scheme aims to provide fast fault type, phase, and location information for microgrid protection and service recovery. In the scheme, branch current measurements sampled by protective relays are pre-processed by discrete wavelet transform to extract statistical features. Then all available data is input into deep neural networks to develop fault information. Compared with previous work, the proposed scheme can provide significantly better fault type classification accuracy. Moreover, the scheme can also detect the locations of faults, which are unavailable in previous work. To evaluate the performance of the proposed fault detection scheme, we conduct a comprehensive evaluation study on the CERTS microgrid and IEEE 34-bus system. The simulation results demonstrate the efficacy of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
A theory for multiresolution signal decomposition: the wavelet representation
Mallat, S. G. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.192463 · Cited by 21,372
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Identification and control of dynamical systems using neural networks
Narendra, K. S., & Parthasarathy, K. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80202 · Cited by 8,014
An introduction to computing with neural nets
Lippmann, R. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,825
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Cited by
Ali, Z., Terriche, Y., Hoang, L. Q. N., Abbas, S. Z., Hassan, M. A., Sadiq, M., Su, C.-L., & Guerrero, J. M. (2021) · IEEE Access · doi:10.1109/access.2021.3112383 · Cited by 104
Alam, M. M., Hossain, M. J., Habib, M. A., Arafat, M. Y., & Hannan, M. A. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.115251 · Cited by 76
AlSaba, M., & Abido, M. (2023) · 2023 IEEE Power & Energy Society General Meeting (PESGM) · doi:10.1109/pesgm52003.2023.10252224 · Cited by 7
Metadata
- Authors
- Yu, J. J. Q., Hou, Y., Lam, A. Y. S., & Li, V. O. K.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2019-03
- Volume
- 10
- Issue
- 2
- Pages
- 1694-1703
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2769392971
- Cited by
- 451
- References
- 27
- FWCI
- 14.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yu, J. J. Q., Hou, Y., Lam, A. Y. S., & Li, V. O. K. (2019). Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2017.2776310
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