基于迁移学习策略的压板开关状态识别

为了实现变电站压板状态的自动巡检，提升变电站运行的可靠性和安全性，提出一种基于迁移学习策略的压板开关状态识别算法。首先利用Inception-V3在ImageNet数据集上进行目标检测训练出的网络参数，得到预训练模型，接着将训练后的瓶颈层特征参数提取至目标网络，作为目标压板开关图片数据集的特征提取器，而后构造基于粒子群优化的支持向量机算法完成压板开关状态的识别。通过与常用深度学习网络在学习效率和学习精度方面的实验结果进行对比，验证本文所提出算法的有效性和优越性，说明迁移学习结合卷积神经网络可以解决电力设备巡检中的小样本问题，提高压板开关状态识别精度和效率。

Identification of Platen Switch State Based on Transfer Learning Strategy

In order to realize the automatic inspection of the platen state in substation and improve the reliability and security of substation operation, a platen switch state identification algorithm based on transfer learning strategy is proposed. Firstly, the network parameters trained by Inception-V3 in dataset ImageNet are used to obtain the pre-trained model. Secondly, the trained bottleneck layer feature parameters are extracted to the target network as the feature extractor of the target platen switch image dataset. Then, the support vector machine algorithm based on particle swarm optimization is constructed to complete the platen switch state recognition. By comparing with the experimental results of commonly used deep learning network in learning efficiency and learning accuracy, the effectiveness and superiority of the proposed algorithm are verified. It also shows that transfer learning combined with convolution neural network can solve the problem of small samples in power equipment inspection and improve the accuracy and efficiency of platen switch state recognition.