Combination of Data Mining and Ant Colony Algorithm for Reactive Power Optimization

2011 ◽  
Author(s):  
Gong Jinxia ◽  
Xie Da ◽  
Zhang Yanchi ◽  
Jiang Chuanwen
Keyword(s):  
Data Mining ◽  
Ant Colony Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Ant Colony ◽  
Reactive Power Optimization

scholarly journals Research on Reactive Power Optimization for Distribution Network Based on Dual Population Improved Ant Colony Algorithm

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Yiran Jiang
Keyword(s):  
Ant Colony Algorithm ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Distribution Network ◽  
Optimal Solution ◽  
Ant Colony ◽  
Reactive Power Optimization ◽  
Heuristic Search Algorithm ◽  
Improved Ant Colony Algorithm

Ant Colony Algorithm is a heuristic search algorithm based on probability selection. It fits for solving the reactive power optimization problem of distribution network, but at the same time, easily falling into the problems of local optimal solution. So Dual Population Improved Ant Colony Algorithm is used to study Reactive Power Optimization Solution. Finally, with an actual example calculation and analysis, and node voltage comparison with and without compensation, the results are proved to be satisfactory. It verified the effectiveness and feasibility of the algorithm and the results show that the algorithm has better effect on optimization.

scholarly journals Ant colony search algorithm for optimal reactive power optimization

2006 ◽  
Vol 3 (1) ◽  
pp. 77-88 ◽  
Author(s):  
K. Lenin ◽  
M.R. Mohan
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Ant Colony ◽  
Adaptive Genetic Algorithm ◽  
Reactive Power Optimization ◽  
Ant Colonies

The paper presents an (ACSA) Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents? approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical) test bus system. The proposed approach is tested and compared to genetic algorithm (GA), Adaptive Genetic Algorithm (AGA).

Sign in / Sign up

Export Citation Format

Share Document