关键词: 粒子滤波, 故障诊断, 双馈发电机, 鸟群算法, 自适应系数, 自适应步长

Abstract:

In order to solve the problem of low fault diagnosis accuracy caused by Particle Filter（PF） sample depletion in the process of fault diagnosis of nonlinear and non-Gaussian complex dynamic systems, a new particle filter algorithm based on improved bird swarm optimization algorithm is proposed in this paper. Aiming at the problem that the standard bird algorithm is easy to fall into the local optimum, the dynamic self-adaptive coefficient and self-adaptive steps are introduced to introduce the position and global optimal position information of each bird into the adaptive change control, so as to improve the problem of falling into the local optimum. The improved bird swarm algorithm is adopted to optimize the particle filter resampling process, which is to imitate the bird’s foraging, warning and flight behavior to move the particles to the high likelihood region. The effectiveness of the algorithm is verified by simulation analysis of fault diagnosis of stator current sensor of doubly-fed generator. Experimental results show that this algorithm can effectively improve the accuracy of fault diagnosis.

Key words: Particle Filter（PF）, fault diagnosis, doubly-fed generator, bird swarm algorithm, self-adaptive coefficient, self-adaptive steps