A Comparative Analysis of Deep Learning Models for Predicting Power System Failures
DOI:
https://doi.org/10.62951/ijeemcs.v1i1.68Keywords:
predictive modelingAbstract
Power systems are critical infrastructure that face significant challenges due to increasing demand and inherent complexity. Predicting failures in power systems is crucial for enhancing grid reliability, minimizing downtime, and optimizing maintenance processes. This study evaluates various deep learning models, specifically convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models, for predicting power system failures. By analyzing these models’ performance metrics on historical power grid data, the study provides insights into the strengths and weaknesses of each approach. The findings contribute to the development of more robust predictive models for power system reliability.
References
Bengio, Y., et al. (1994). Learning Long-Term Dependencies with Gradient Descent is Difficult. IEEE Transactions on Neural Networks.
Brown, T. B., et al. (2020). Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165.
Cho, K., et al. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv preprint arXiv:1406.1078.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Graves, A., & Schmidhuber, J. (2005). Framewise Phoneme Classification with Bidirectional LSTM Networks. Proceedings of the IEEE.
He, K., et al. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems.
LeCun, Y., et al. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE.
Reddy, M., & Jena, P. (2019). Predicting Power System Failures Using Deep Learning Models. Journal of Power and Energy Systems.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature.
Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. Advances in Neural Information Processing Systems.
Vaswani, A., et al. (2017). Attention is All You Need. Advances in Neural Information Processing Systems (NIPS).
Zhang, J., et al. (2020). Failure Prediction in Power Systems Using Deep Learning. IEEE Transactions on Power Systems.
Zhang, Y., et al. (2021). Enhanced Prediction Accuracy of Power Failures Using Transformer Networks. International Journal of Electrical Power & Energy Systems.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Electrical Engineering, Mathematics and Computer Science
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
