scholarly journals Hierarchically correlated equilibrium Q-learning for multi-area decentralized collaborative reactive power optimization

2016 ◽  
Vol 2 (3) ◽  
pp. 65-72 ◽  
Author(s):  
Min Tan ◽  
◽  
Chuanjia Han ◽  
Xiaoshun Zhang ◽  
Lexin Guo ◽  
...  
Keyword(s):  
Reactive Power ◽  
Power Optimization ◽  
Correlated Equilibrium ◽  
Reactive Power Optimization ◽  
Q Learning

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

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 321 ◽  
Author(s):  
Huazhen Cao ◽  
Tao Yu ◽  
Xiaoshun Zhang ◽  
Bo Yang ◽  
Yaxiong Wu
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Reactive Power ◽  
Power Optimization ◽  
Solution Space ◽  
Reactive Power Optimization ◽  
Dimensional Solution ◽  
Q Learning ◽  
State Action ◽  
Low Dimensional

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.

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