Reactive Power Optimization Of Wind Power Distribution Network Based On Chaotic Particle Swarm Optimization

With the continuous access to wind power distribution network, resulting in changes in distribution network loss, the study of wind power distribution network reactive power optimization problem is very important. In solving the problem of multi-objective optimization,multiple objective functions such as minimum net loss and reactive power compensation capacity are contradictory, and can not be optimized at the same time. Considering that the wind power is random and intermittent, according to the fixed fan output The reactive power optimization does not reflect the characteristics of wind power change. Therefore, based on the analysis of the scene, the improved chaotic particle swarm optimization algorithm is used to optimize the reactive power distribution network.Firstly, the equivalent treatment method of the wind turbine in the distribution network is analyzed, and the calculation method of the power distribution network of the doubly fed wind turbine is given. Secondly, based on the Pareto optimal solution, the chaos particle swarm optimization (PSO) algorithm is applied to solve the multi-objective optimization problem, and the scene analysis method is used to analyze the multi-objective optimization problem. The uncertainty of the scene is similar to the uncertainty of the output of the fan, the scene analysis method combined with the different conditions of the fan, divided into a single scene and the whole scene. Finally, a fixed-node access fan and reactive power compensation device are selected in IEEE 3G node distribution network. The reactive power optimization model with multiple active and negative power compensation capacity is established. Chaos particle swarm optimization (PSO) is used to solve the reactive power compensation capacity. On - load transformer tap position.In the process of solving the single objective optimization model with the minimum net loss as the objective function, the scheme under some kind of scenario is used to make the network loss higher than the optimal value in other scenarios. The minimum net loss and the capacity of reactive power compensation are the target The optimal solution and the Pareto optimal frontier are obtained in the process of multi - objective optimization of the function.The results show that the optimal scheme in a specific scenario does not apply to other scenarios, but the scheme in the whole scene is an overall optimal scheme. The improved chaotic particle group multi-objective optimization algorithm can be used to solve the problem of wind power distribution network Multi-objective reactive power optimization problem, and the effect is better than particle group multi-objective optimization method.