A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration
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DOI: 10.1016/j.renene.2021.06.081
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Cited by:
- Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
- M. A. Hannan & Ali Q. Al-Shetwi & M. S. Mollik & Pin Jern Ker & M. Mannan & M. Mansor & Hussein M. K. Al-Masri & T. M. Indra Mahlia, 2023. "Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions," Sustainability, MDPI, vol. 15(5), pages 1-30, February.
- Ashok Bhansali & Namala Narasimhulu & Rocío Pérez de Prado & Parameshachari Bidare Divakarachari & Dayanand Lal Narayan, 2023. "A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models," Energies, MDPI, vol. 16(17), pages 1-18, August.
- Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
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Keywords
Wind Energy; Power system stabilizers; Stability; Deep deterministic policy gradient; Sparse coordinated adaptive control;All these keywords.
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