基于模糊故障树的光伏并网微型逆变器故障识别方法研究

为缩短光伏并网微型逆变器故障提取时间、提升采样精度和效率，基于模糊故障树研究光伏并网微型逆变器故障识别方法。首先利用模糊故障树方法对光伏并网微型逆变器进行故障映射，建立故障底事件与顶事件之间的联系，然后通过小波包变换提取光伏并网微型逆变器故障特征，最后通过构造SOFTMAX光伏并网微型逆变器故障多分类器，实现对光伏并网微型逆变器故障的识别。设计仿真实验，结果证明，在有功功率为3000W-12000W的范围内该方法的采样时间较短，且采样精度和采样效率较高，在光伏并网微型逆变器故障识别方面具有优越性能。

Study on Fault Diagnosis Method for Photovoltaic Grid-connected Micro-inverter Based on Fuzzy Fault Tree

In order to shorten the fault extraction time and improve the sampling accuracy and efficiency of photovoltaic grid connected micro inverter, the fault identification method of photovoltaic grid connected micro inverter is studied based on fuzzy fault tree. First, the fuzzy fault tree method is used to map the photovoltaic grid-connected micro-inverter, and the connection between the bottom event and the top event is established. Then, the fault characteristics of the photovoltaic grid-connected micro-inverter are extracted through wavelet packet transform, and finally the SOFTMAX is constructed. The photovoltaic grid-connected micro-inverter fault multi-classifier realizes the identification of photovoltaic grid-connected micro-inverter faults. The simulation experiment is designed, and the results proves that the sampling time of this method is shorter in the range of active power of 3000W-12000W, and the sampling accuracy and sampling efficiency are higher. It has superior performance in the fault identification of photovoltaic grid-connected micro-inverters.