基于深度学习的高压隔离开关分合状态检测算法研究

高压隔离开关的正常工作是电力系统稳定运行的前提。为正确识别隔离开关的分合状态，提出一种轻量化改进型YOLOv5s目标检测算法。首先，针对隔离开关数据集，采用二次优化K-means++聚类算法重新获取锚框参数。然后，将模型中的损失函数由CIOU替换为具有更强收敛性能的EIOU，加快模型训练的收敛速度。最后，在模型主干特征提取网络的最后一层添加CBAM注意力模块，加强模型特征提取能力。在此基础上，采用通道稀疏化剪枝的方法对改进后的模型进行轻量化处理，减小模型体积和算力消耗。实验结果表明，改进后的模型识别平均精度均值到达97.4%，轻量化处理后的模型大小为3.92 MB，使得模型更加容易部署到移动端设备完成实时检测。

A detection algorithm for opening and closing states of high-voltage isolation switches based on deep learning

Normal operation of high-voltage disconnect switches is a prerequisite for the stable operation of power systems. To correctly identify the breaking and closing states of disconnect switches, a lightweight improved YOLOv5s target detection algorithm is proposed. First, the anchor frame parameters are reacquired using a quadratic optimized K-means++ clustering algorithm for the disconnecting switch dataset. Then, the loss function in the model is replaced from CIOU to EIOU with stronger convergence performance to accelerate the convergence speed of model training. Finally, a CBAM attention module is added to the last layer of the model backbone feature extraction network to strengthen the model feature extraction capability. The improved model is lightened using the channel sparsification pruning method to reduce the model size and arithmetic power consumption. The experimental results show that the average accuracy of the improved model reaches 97.4% and the model''s size is 3.92 MB after the light weighting process, making the model easier to deploy to mobile devices for real-time detection.