Risk-based Reactive Power Optimization Based on Tribe Q-Learning Algorithm

2018 International Conference of Green Buildings and Environmental Management

土木建筑工程;生态环境科学

Feng, Li^1 ; Zhibin, Xu^1 ; Li, Xiao^1

Guangdong Electric Power Research Institute of Energy Technology Limited Liability Company, China^1

关键词: Artificial intelligence algorithms;    Global conver-gence;    IEEE 118-bus system;    Knowledge matrix;    Knowledge transfer;    Q-learning algorithms;    Reactive power optimization;    Search information;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/186/6/012010/pdf
DOI  :  10.1088/1755-1315/186/6/012010

来源: IOP

【 摘 要 】

In this paper, the risk assessment theory is introduced into the traditional reactive power optimization problem. Moreover, a novel tribe Q-learning algorithm with knowledge transfer is proposed, which is developed from the search mechanism of artificial intelligence algorithm and the iteration mode of Q-learning. The Q matrix is adopted as the knowledge matrix for the storage of the search information of the tribe. During online learning, the rate of TQL can be accelerated significantly via the knowledge transfer. The simulation on IEEE 118-bus systems demonstrates that the rate of TQL is two to twenty times faster than that of other AI algorithms while the global convergence can be ensured.