分布式电源大量接入，导致含源配网故障弱特征化以及故障时刻产生大量谐波信号，传统故障诊断方法应用效果不佳。提出一种基于改进图神经网络的含源配网故障诊断方法。首先，利用小波变换提取故障前后电流电压细节系数；其次，通过加权投影关联分析法计算各电气量之间的关联度；再次，选择关联度较高的电气量作为输入搭建基于图神经网络的含源配网故障诊断模型；最后，在MATLAB/Simulink中搭建了不同电压等级的含源配网故障仿真模型。结果表明，该故障诊断方法能有效强化故障信号并在不同电压等级的含源配网下对故障准确定位与分类，在数据缺失与噪声环境下也能保持良好的诊断性能，具有良好的鲁棒性与泛化性。

The weak fault characteristics and the presence of numerous harmonic signals in distribution networks with renewable energy sources reduce the effectiveness of traditional fault diagnosis methods. A fault diagnosis method based on an improved graph neural network was proposed. Wavelet transform was applied to extract the detail coefficients of current and voltage before and after faults. Weighted projection correlation analysis was performed to calculate the correlation between electrical quantities. Highly correlated quantities were selected as inputs to construct the fault diagnosis model using a graph neural network. Fault simulation models for different voltage levels were developed in MATLAB/Simulink. The results indicate that fault signals are effectively enhanced, and faults are accurately located and classified in distribution networks with renewable energy sources at different voltage levels. Good diagnostic performance is maintained in the presence of data loss and noise, demonstrating strong robustness and generalization.