Research On Algorithms And Application For Reactive Optimization Of Power Systems

Reactive power optimization is a kind of complicated nonlinear optimization problem. A variety of methods have been devoted to solve the problem. In early works, mathematical analysis programming methods with mature and integrated theory were used, which can obtain the final result very quickly, but unfortunately the functions must be differentiable, that's not easy to attain. Nowadays artificial intelligent algorithms that are on the basis of random search are booming. This series of algorithms with self-adapting step size can search the solution directly and more likely to acquire global optimization. In this paper the author's work is as follows:The development, status and mathematical models of VAR optimization problem are presented in this paper. Kinds of methods for solving the problem are summarized. What's more, the differences and features of these methods are given in the paper.On the basis of careful research on Artificial Fish Swarm Algorithm ,Immune Algorithm and Particle Swarm Algorithm, optimization programs are achieved. Test results on several typical reactive optimization problems show that those algorithms are successful and feasible approach for reactive power optimization.In this paper, the immune information processing mechanism of immune system is involved into original particle swarm optimizer, and the particle swarm optimization algorithm with immunity are proposed. The proposed algorithm has both the properties of the original particle swarm optimization algorithm and the immune mechanism of immune system, can improve the abilities of seeking the global excellent result. The algorithm is applied to reactive power optimization problems, the results of two IEEE systems prove the algorithm is reasonable. A practical system-network of Chaozou computed by the method proves to be practicable.