基于多源数据融合的Alexnet神经网络大电网故障诊断

针对电网在台风、冰冻等极端条件下发生故障的诊断问题，提出利用标准遥信及广域测量系统(wide area measurement system,WAMS)数据训练Alexnet模型，并应用于电网故障诊断的方案。首先利用标准故障遥信信息和WAMS数据构造Alexnet的输入图片矩阵，对Alexnet进行训练。然后对Alexnet输入图片高维特征提取方法进行分析，提出构造最优分布结构的输入图片矩阵方法，并形成故障诊断模型。最后以海南岛电网遭受台风袭击为场景，搭建仿真模型对Alexnet故障诊断模型进行验证。

Fault Diagnosis of Large Grid With Alexnet Neural Network Based on Multi-source Data Fusion

In allusion to the diagnosis of power grid faults occurred in the extreme conditions such as typhoons, freezing and so on, a scheme, in which firstly an Alexnet model was trained by the data from standard telecommand and wide area measurement system (abbr. WAMS) and then the trained Alexnet was applied to power grid fault diagnosis, was proposed. Firstly, the standard fault remote signaling information and WAMS data were utilized to construct the input image matrix to train the Alexnet. Secondly, the high dimensional feature extraction method of Alexnet input image was analyzed and a method to construct the input image matrix with optimal distribution structure was put forward and a fault diagnosis model was formed. Finally, taking Hainan Island power grid subjected to typhoon attack as the scene a simulation model was built to verify the Alexnet fault diagnosis model.