电力系统保护与控制2023，Vol.51Issue(24)：154-163,10.DOI:10.19783/j.cnki.pspc.230659
基于格拉姆角场与迁移学习-AlexNet的变压器绕组松动故障诊断方法
A fault diagnosis method for transformer winding looseness based on Gramian angular field and transfer learning-AlexNet
摘要
Abstract
The winding looseness fault is one of the main mechanical faults in transformers,and there is still a lack of effective intelligent diagnosis methods.Therefore,a diagnosis method for such a fault based on Gramian angular field and transfer learning-AlexNet is proposed.The periodic characteristics of the vibration signals during steady-state operation of transformers make it difficult to construct a sufficient set of images with time correlation.Therefore,a sample construction method is proposed to generate the Gramian angular field image set of transformer vibration signals.The generated image set is sent to AlexNet for transfer learning to obtain the fine-tuned neural network model.The image set generated by the sample construction method is used as the training and validation set,and the experimental results are that training and validation accuracy of the convolutional neural network model established are both above 99%.The image set generated by the periodic vibration signal of the transformer is used as the test set,with a testing accuracy of over 99%,achieving accurate diagnosis of transformer winding looseness faults.It also provides a way for constructing a sufficient sample set using time-related image transformation methods for periodic signals.关键词
变压器/绕组松动/振动信号/格拉姆角场/AlexNet/迁移学习/样本构建/故障诊断Key words
transformer/winding looseness/vibration signals/Gramian angular field/AlexNet/transfer learning/sample construction/fault diagnosis引用本文复制引用
薛健侗,马宏忠,杨洪苏,倪一铭,万可力,迮恒鹏..基于格拉姆角场与迁移学习-AlexNet的变压器绕组松动故障诊断方法[J].电力系统保护与控制,2023,51(24):154-163,10.基金项目
This work is supported by the National Natural Science Foundation of China(No.51577050). 国家自然科学基金项目资助(51577050) （No.51577050）
国网江苏省电力有限公司重点科技项目资助(J2021053) （J2021053）
