Research On Reactive Power Optimization In AC-DC Power System With Wind Turbine

Application of wind power and DC power transmission increases widely. When wind turbine connected to the grid, they need to absorb a large amount of reactive power. The inverter AC and DC systems also need to consume some reactive power when they're running. Traditional reactive power optimization is not well adapted to today's complex grid structure. So it is need to determine reasonable reactive power optimization to reduce grid losses and improve voltage quality and system stability according to characteristics of wind turbine operating and AC and DC systems.The reactive power optimization isa multi-variable, multi-constrained, nonlinear programming problems,control variablesbothcontinuous variablesand discretevariables, so the optimization process isverycomplex.This paper adopts particle swarm optimization algorithmto solve the problem.Because Particle Swarm Optimization algorithm is easy to fall into local optimum, the chaotic variables is introducted. Using the randomness, ergodicity and regularity of chaotic systems, the diversity of the population is improved. At the same time, the premature convergence problem is better to avoid. The improved particle swarm algorithm is easier to find the global optimal solution.Based on the traditional model of reactive power optimization, index of static voltage stability is introduced and a model of multi-objective reactive power optimization is established,where the active power loss minimization, static voltage stability margin maximization are taken into account. Because of the randomness of the wind speed, the scenario analysis method change uncertain scene to some typical scene for reactive power optimization by using the statistical characteristics of wind speed.Then expectations significance is Optimal.The modified IEEE118-bus test system is used to verify the presented approaches. This research has theoretical and practical guiding significance for the regional power grid reactive power optimization, decrease network loss, and reliable operation.