针对变电站中高压隔离开关因复杂环境和长期运行引发的机械故障诊断问题，提出了一种基于格拉姆角场（Gramian angular field, GAF）结合卷积神经网络（convolutional neural network, CNN）特征提取与多策略改进麻雀搜索算法（multi-strategy improved sparrow search algorithm, MISSA）优化的极端梯度提升（extreme gradient boosting, XGBoost）故障诊断方法。通过GAF将高压隔离开关的振动信号转换为二维图像，生成相应的图像集，并利用CNN进行特征提取；引入MISSA优化XGBoost分类器的参数，建立高压隔离开关机械故障诊断模型；最后基于该模型识别不同的状态类型。实验结果表明，与传统故障诊断模型相比，所提方法的识别准确率超过96%，能够有效识别高压隔离开关的机构卡涩、松动和三相不同期故障，为高压隔离开关状态检修提供了理论依据。

To address the mechanical fault diagnosis of high-voltage disconnect switches in substations, which are affected by complex environments and prolonged operation, a fault diagnosis method based on Gramian angular field (GAF) combined with convolutional neural network (CNN) for feature extraction and multi-strategy improved sparrow search algorithm (MISSA) to optimize extreme gradient boosting (XGBoost) parameters was proposed. Vibration signals were converted into two-dimensional images using GAF, features were extracted through CNN, and MISSA was employed to optimize the XGBoost classifier, thereby constructing a fault diagnosis model. Experimental results indicate that the proposed method achieves an identification accuracy of over 96%, effectively diagnosing issues like mechanism jamming, looseness, and phase mismatch, providing a theoretical basis for condition-based maintenance of high-voltage disconnect switches.