The invention discloses a
wind power system reactive
power planning method based on the golden section
cloud particle swarm optimization
algorithm. The
wind power system reactive
power planning method comprises the steps that a reactive
power planning mathematic model is built, and a target function is determined;
original data of a
wind power system is input, and therefore an initial
population is formed; all particles are generated randomly, a golden section judging criterion is used for dividing a particle swarm into three parts according to the self-fitness value of the particle swarm, and different
inertia weight is set for each part of particles; new positions and speeds of the particles are obtained through the
particle swarm optimization algorithm, the particles are divided into three parts and iterated repeatedly according to the method before an the end condition is met, an optimal solution is searched, and therefore reactive power planning of the
wind power system is achieved. According to the
wind power system reactive power
planning method based on the golden section
cloud particle swarm optimization
algorithm, the node
voltage level of the
wind power system is effectively improved, network loss of a power network is reduced, the diversity of the particles is kept according to the algorithm, the prematurity phenomenon which easily occurs during optimization searching is avoided, and convergence rate in the optimization searching process is improved. In addition, the wind power system reactive power
planning method based on the golden section
cloud particle swarm optimization algorithm is small in calculated amount, and higher in
operability.