scholarly journals Opposition-Based Improved PSO for Optimal Reactive Power Dispatch and Voltage Control

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Shengrang Cao ◽  
Xiaoqun Ding ◽  
Qingyan Wang ◽  
Bingyan Chen
Keyword(s):  
Power Systems ◽  
Life Cycle Cost ◽  
Reactive Power ◽  
Power Optimization ◽  
Optimization Problems ◽  
Cost Model ◽  
Population Diversity ◽  
Reactive Power Optimization ◽  
Switching Operation ◽  
Global And Local

An opposition-based improved particle swarm optimization algorithm (OIPSO) is presented for solving multiobjective reactive power optimization problem. OIPSO uses the opposition learning to improve search efficiency, adopts inertia weight factors to balance global and local exploration, and takes crossover and mutation and neighborhood model strategy to enhance population diversity. Then, a new multiobjective model is built, which includes system network loss, voltage dissatisfaction, and switching operation. Based on the market cost prices, objective functions are converted to least-cost model. In modeling process, switching operation cost is described according to the life cycle cost of transformer, and voltage dissatisfaction penalty is developed considering different voltage quality requirements of customers. The experiment is done on the new mathematical model. Through the simulation of IEEE 30-, 118-bus power systems, the results prove that OIPSO is more efficient to solve reactive power optimization problems and the model is more accurate to reflect the real power system operation.

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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