Fault diagnostic method for power converter based on wavelet neural network with improved algorithm

As one of the core equipments in doubly-fed induction wind power generation system,the operation reliability of power converters seriously influences the safety and stability of power generation system.Since some flaws exist in Wavelet Neural Network(WNN) based on Recursive Least Square(RLS) algorithm such as low convergence precision and rate,and searching space possessing local minima and oscillation.The authors proposed a modified algorithm for fault detection of diagnostic power converters,in which variable weight and alter learning coefficient were employed to resolve above problems.After the modified WNN was trained and the faults were recognized from practical current data,comparison and analysis were carried out in simulation.The experimental results demonstrate that the modified algorithm can provide higher diagnostic precision and require less convergence time than the RLS algorithm.