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Open AccessArticle

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

1
Power Grid Planning Research Center of Guangdong Power Grid Co., Ltd., Guangzhou 510062, China
2
College of Electric Power, South China University of Technology, Guangzhou 510640, China
3
College of Engineering, Shantou University, Shantou 515063, China
4
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
*
Author to whom correspondence should be addressed.
Processes 2019, 7(6), 321; https://doi.org/10.3390/pr7060321
Received: 6 May 2019 / Revised: 23 May 2019 / Accepted: 24 May 2019 / Published: 31 May 2019
A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperation. Behavior transfer was introduced, and prior knowledge of the source task was used to process the new task according to its similarity to the source task, so as to accelerate the convergence of the transfer bees optimizer. Moreover, the solution space was decomposed into multiple low-dimensional solution spaces via associated state-action chains. The transfer bees optimizer performance of reactive power optimization was assessed, while simulation results showed that the convergence of the proposed algorithm was more stable and faster, and the algorithm was about 4 to 68 times faster than the traditional artificial intelligence algorithms. View Full-Text
Keywords: transfer bees optimizer; reinforcement learning; behavior transfer; state-action chains; reactive power optimization transfer bees optimizer; reinforcement learning; behavior transfer; state-action chains; reactive power optimization
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MDPI and ACS Style

Cao, H.; Yu, T.; Zhang, X.; Yang, B.; Wu, Y. Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application. Processes 2019, 7, 321.

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