.png){kind=icon}
Return
Dynamic multi-group self-adaptive differential evolution algorithm for reactive power optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijepes.2009.11.009.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a novel algorithm, dynamic multi-group self-adaptive differential evolution (DMSDE), for reactive power optimization of power system. In DMSDE, the population is divided into multi-groups vector-individuals, which can exchange information dynamically. Also, in the mutation phase the best vector, among the three vectors selected randomly in the search space, is chosen as the base vector. The direction of the difference vector is determined by the other two stochastic vectors. Moreover, two parameters, scaling factor and crossover rate, are self-adapted. The objective of optimization is minimizing active power losses in transmission network while maintaining the quality of voltages. The new method is tested on IEEE 30-Bus, IEEE 57-Bus and IEEE 118-Bus power systems. The numerical results, compared with other stochastic search algorithms, show that DMSDE could find high-quality solutions with more reliability and efficiency. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Dynamic multi-group self-adaptive differential evolution
Reactive power optimization
Power system
Journal
I
IF:
5
Papers:
1.1W
Citations:
3.1W
Organization
Cited Papers
.png){kind=icon}
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
ShareSaveShareSaveShareSaveShareSave
ShareSave
View more