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Appl. Sci. 2018, 8(11), 2185; https://doi.org/10.3390/app8112185

Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems

1
College of Electrical Engineering, Guangxi University, Nanning 530004, China
2
College of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
3
College of Engineering, Shantou University, Shantou 515063, China
*
Author to whom correspondence should be addressed.
Received: 16 October 2018 / Revised: 1 November 2018 / Accepted: 2 November 2018 / Published: 7 November 2018
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Abstract

To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed. To mitigate the curse of dimensionality that arises in conventional reinforcement learning algorithms, deep forest is applied to reinforcement learning. Therefore, deep forest reinforcement learning (DFRL) as a preventive strategy for AGC is proposed in this paper. The DFRL method consists of deep forest and multiple subsidiary reinforcement learning. The deep forest component of the DFRL is applied to predict the next systemic state of a power system, including emergency states and normal states. The multiple subsidiary reinforcement learning component, which includes reinforcement learning for emergency states and reinforcement learning for normal states, is applied to learn the features of the power system. The performance of the DFRL algorithm was compared to that of 10 other conventional AGC algorithms on a two-area load frequency control power system, a three-area power system, and the China Southern Power Grid. The DFRL method achieved the highest control performance. With this new method, both the occurrences of emergency situations and the curse of dimensionality can be simultaneously reduced. View Full-Text
Keywords: deep forest reinforcement learning; preventive strategy; automatic generation control; deep forest; reinforcement learning deep forest reinforcement learning; preventive strategy; automatic generation control; deep forest; reinforcement learning
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Yin, L.; Zhao, L.; Yu, T.; Zhang, X. Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems. Appl. Sci. 2018, 8, 2185.

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