Abstract: 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.

Key words: transfer learning, deep learning, particle swarm optimization, support vector machine