Reactive Power Optimization In Power System Based On Ant Particle Colony Algorithm

Reactive power optimization with multiple variables, both continuous also scattered at the same time constraints are more diverse optimization objectives, a mixed linear programming problem, the multi-objective, multi-constraint, multi-variable optimization features make problem solving more difficult. Reactive power optimization problem for the security of the grid, and stable operation has an important role, so to find a fast solution speed, high-quality algorithms to solve reactive power optimization is of great significance.Ant colony algorithm and particle swarm algorithm belong to the swarm intelligence algorithm, ant colony algorithm has strong robustness and the ability to search for better solutions, and solve the discrete optimization of its superior performance issues; particle swarm optimization algorithm to solve continuous good The optimization problem, with parallel processing, robustness and computational efficiency. In solving reactive power optimization, the ant colony algorithm and particle swarm algorithm has its own strengths and weaknesses, a strong local search ability of ant colony algorithm, but the operation takes a long time, convergence is slow; particle swarm optimization Solving local search capabilities faster and less prone to fall into local optimal solution.In this paper, the lack of both a hybrid algorithm to improve the solution quality and solution speed for reactive power optimization problems solver add new ideas. The main work is as follows:1. On the ant algorithm and particle swarm algorithm, the algorithm steps, mathematical model is analyzed, the parameters of each of the two algorithms were discussed, presents two algorithms to solve the problem of reactive power optimization solution strategy, the algorithm steps.2. Particle swarm ant colony algorithm are compared, analyzed the similarities and differences between the two algorithms, the combination of the two algorithms are given strategies to the introduction of reactive power optimization, design and implementation of a reactive power can be effectively resolved hybrid optimization algorithm for optimization problems.3. From an economic point of view with minimal power loss as the objective function, the use of the power system IEEE6, IEEE30 test system to test the algorithm, and results with other literature were compared, the optimized voltage quality, the number of iterations , the net loss fell and so a comparative analysis confirmed the hybrid algorithm.