Fault pattern recognition of partial discharge based on improved particle swarm optimization

摘要

To solve the problem of low recognition rate which is the existing identification methods of partial discharge faults, a new method was designed with wavelet, singular value and improved particle swarm algorithm to optimize the BP neural network. First, using continuous wavelet and singular value decomposition to get the signal characteristic value; then combined with the significance of inertia weight and learning factor which is the key parameter of iteration and convergence in the algorithm, a nonlinear improved method of key parameters is proposed which is improved by indexing and triangulation. Simulation results show that the proposed method has a faster convergence speed and higher accuracy in training, and it has a high degree of recognition which is better than the standard particle swarm algorithm and the traditional BP neural network in the test.