2022
DOI: 10.3390/en15020507
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Abstract: The high penetration of renewable energy sources, coupled with decommissioning of conventional power plants, leads to the reduction of power system inertia. This has negative repercussions on the transient stability of power systems. The purpose of this paper is to review the state-of-the-art regarding the application of artificial intelligence to the power system transient stability assessment, with a focus on different machine, deep, and reinforcement learning techniques. The review covers data generation pr… Show more

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Cited by 18 publications
(2 citation statements)
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References 85 publications
(129 reference statements)
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“…ML is further divided into supervised learning, unsupervised learning, and reinforced learning for use in power electronics. Some of the research trends and an overview of artificial intelligence in power electronics can be found in [22][23][24][25][26][27][28][29][30].…”
Section: Motivation and Related Workmentioning
confidence: 99%
“…ML is further divided into supervised learning, unsupervised learning, and reinforced learning for use in power electronics. Some of the research trends and an overview of artificial intelligence in power electronics can be found in [22][23][24][25][26][27][28][29][30].…”
Section: Motivation and Related Workmentioning
confidence: 99%
“…For the representative scenes, TTC rules were mined and a knowledge base was formed, which was better adapted to real-time monitoring of the safety of wind power transmission channels. Reference [6] uses the correlation classification method to extract the power grid stable operation rules. Since the proposed method introduces the time factor, the obtained rules can not only reveal the information of the strongly correlated influencing factors of the stable operation of the power grid, but also extract the influence factor changes on the system state change the internal relationship, thus providing support for scheduling operation decisions.…”
Section: Introductionmentioning
confidence: 99%