CAJ | 학술논문

由于小波神经网络具有较强的逼近能力和容错能力,本文研究了一套基于小波神经网络的永磁直驱风电变流器的故障诊断系统。首先对风电变流器中的故障进行设置,然后使用小波变换的多分辨率特性,提取风电变流器的故障特征向量,最后应用BP神经网络进行故障模式识别。通过仿真结果表明,此种风电变流器的故障诊断系统具有较强的稳定性和准确性。
유우소파신경망락구유교강적핍근능력화용착능력,본문연구료일투기우소파신경망락적영자직구풍전변류기적고장진단계통。수선대풍전변류기중적고장진행설치,연후사용소파변환적다분변솔특성,제취풍전변류기적고장특정향량,최후응용BP신경망락진행고장모식식별。통과방진결과표명,차충풍전변류기적고장진단계통구유교강적은정성화준학성。
Because of the wavelet neural network has strong approximation ability and fault tolerance.This paper studies a set of fault diagnosis system of wind power converter based on Wavelet Neural Network. Firstly, Setting the failure of the wind power converter, and then using the multi-resolution characteristics of wavelet transform, extracted the fault feature vectors of wind power converter, Finally, using the BP neural network for fault identification. Simulation results show that, the wind power converter fault diagnosis system has strong stability and accuracy.