Optimizing Electrical System Performance with Machine Learning: An Analysis of Algorithms
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6774-2_41
Reference10 articles.
1. Shawkat, M.I.F., et al.: Application of machine learning techniques for power system analysis. Int. J. Electr. Power Energy Syst. (2019). https://doi.org/10.1016/j.ijepes.2019.03.018
2. Chakraborty, S., et al.: Machine learning techniques for electrical load forecasting: a review. IEEE Trans. Power Syst. (2019). https://doi.org/10.1109/TPWRS.2018.2884176
3. Mirjalili, H., et al.: Application of machine learning techniques for fault diagnosis in electrical power systems: a review. Renew. Sustain. Energy Rev. (2020). https://doi.org/10.1016/j.rser.2020.110160
4. Li, F., et al.: Machine learning approaches for fault diagnosis in electrical machines: a review. IEEE Trans. Industr. Electron. (2020). https://doi.org/10.1109/TIE.2020.2975459
5. Kavousi-Fard, K.E., et al.: Machine learning for the analysis of electrical grid data: a review. Renew. Sustain. Energy Rev. (2020). https://doi.org/10.1016/j.rser.2020.110261
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