CAJ | 학술논문

探讨了自组织特征映射（SOM）人工神经网络在风力机变频器故障诊断中的应用，对风力机变频器故障信息提取、故障特征数据、故障征兆向量进行了分析，采用Matlab神经网络工具箱对风力机变频器故障类型判断和故障位置确定进行仿真实验，实验结果表明，利用该方法进行风力发电机电力电子装置故障诊断能取得较好的效果，具有一定的工程应用价值。
탐토료자조직특정영사（SOM）인공신경망락재풍력궤변빈기고장진단중적응용，대풍력궤변빈기고장신식제취、고장특정수거、고장정조향량진행료분석，채용Matlab신경망락공구상대풍력궤변빈기고장류형판단화고장위치학정진행방진실험，실험결과표명，이용해방법진행풍력발전궤전력전자장치고장진단능취득교호적효과，구유일정적공정응용개치。
In this paper a fault diagnosis system of wind turbine's converter is studied by using the self-organizing feature map neural network. The fault sets, fault symptoms and fault feature data are summarized. In order to allocate position and make sure the fault type, experiments for SOM network training and simulation are carried out by neural network toolbox of MATLAB. Experimental result indicates that the proposed method can effectively diagnose the fault of frequency converter in wind power system and has a certain value in engineering application.