Reactive power optimization for distribution network system with wind power based on improved multi-objective particle swarm optimization algorithm

Aiming at the uncertainty of the grid-connected output of wind turbines, a scenario analysis method based on probability occurrence is used to transform the uncertainty model into multi scenario problems with different occurrence probabilities, a reactive power optimization model is established with the goal of minimizing the active power network loss and voltage deviation. Aiming at the poor diversity of Pareto frontiers obtained by traditional methods, an improved multi-objective particle swarm optimization algorithm is proposed. The algorithm uses adaptive grids to obtain the density of particles in external archives, selects the global optimal particle and maintains the scale of the external repository according to the density information using a roulette mechanism, effectively ensuring the uniformity and diversity of the Pareto frontier distribution. The algorithm is used to calculate reactive power optimization of the IEEE 33-bus system with wind power, and compared with the existing NSGA-Ⅱ algorithm. The results show that the Pareto frontier obtained by the proposed algorithm is better, the voltage stability and active power loss reduction rate of the distribution network system with wind power is higher.