Research On The Model And Algorithms Of Reactive Power Optimization In Power System

During the connection of the big grids and the application of open-access market principles, the security and stability of the power systems become a big challenge. The scheme of optimal coordinated-correlative voltage and reactive power control are can neatly realize optimal coordinate control, its cores are reactive power optimization and voltage stability analysis.The model of reactive power optimization concerning voltage stability margin is presented based the theory of the bifurcation. The equational restrictions are power flow equations with load coefficients, the power of all the nodes increase synchronously. The difference of load coefficients between initial power system and up-limiting stable one after reactive power optimization means voltage stability margin, make the power system achieve three aims: minimal power loss, best voltage quality and maximal voltage stability margin. Genetic algorithm and particle swarm optimization are the two popular algorithms that are applied in reactive power optimization. In the paper, the operations such as code, cross, mutation and preserving best individual are improved. With BP artificial neural network, the result of genetic algorithm become more precise. The Pareto optimal set is introduced to particle swarm optimization for multi-objective reactive power optimization, the methods of selection of best situations and fitness function design based Pareto optimal set are improved.Through the Jacobi matrix eigen- value structure analysis of static stable critical point, voltage unsubstantial region and pivotal generators are found, it gives reference to preventive control of voltage stability.