Remote Wind Energy Conversion System
Author:
Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-51992-6_21
Reference17 articles.
1. Marugán, A.P., Márquez, F.P.G.: SCADA and artificial neural networks for maintenance management. In: International Conference on Management Science and Engineering Management, pp. 912–919. Springer, Cham (2017)
2. Yang, W., Tavner, P.J., Crabtree, C.J., Feng, Y., Qiu, Y.: Wind turbine condition monitoring: technical and commercial challenges. Wind Energy 17(5), 673–693 (2014)
3. Xiao, Z.X., Guo, Z., Guerrero, J.M., Fang, H.: SCADA system for islanded DC microgrids. In: 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017, pp. 2669–2674 (2017). https://doi.org/10.1109/iecon.2017.8216449
4. Advances in Intelligent Systems and Computing;E Guran,2016
5. Pandit, R.K., Infield, D.: SCADA based wind turbine anomaly detection using Gaussian Process (GP) models for wind turbine condition monitoring purposes. IET Renew. Power Gener. 12(11), 1249–1255 (2018). https://doi.org/10.1049/iet-rpg.2018.0156 . ISSN 1752-1416
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