Convolutional neural network based on fast Fourier transform and gramian angle field for fault identification of HVDC transmission line. (December 2022)

• Record Type: Journal Article
• Authors: Ding, Can; Wang, Zhenyi; Ding, Qingchang; Yuan, Zhao
• Is Part Of: Sustainable energy, grids and networks. Volume 32(2022)
• Publication Date: 2022-12
• Subjects: Convolution neural network -- DC line fault classification -- Fast Fourier transform -- Gramian angular field; Renewable energy sources -- Periodicals; Smart power grids -- Periodicals; Electric power systems -- Periodicals; 333.794
• DOI: 10.1016/j.segan.2022.100888
• Languages: English
• ISSNs: 2352-4677

Abstract

Abstract: Flexible DC grid puts forward high-speed dynamic, and high-reliability requirements for DC transmission line protection. How to identify DC line faults ultra-fast and reliably is one of the critical technologies for the further development of the flexible DC grid. To solve this problem, a new DC transmission line fault identification scheme based on the convolutional neural network (CNN) based on fast Fourier transform (FFT) and gramian angular field (GAF) is proposed. The proposed scheme aims to further reduce the identification time of DC transmission line fault types under the requirements of rapidity and accuracy of DC transmission line protection. In this scheme, the obtained feature data are processed by FFT and GAF to obtain the feature images of different fault types corresponding to DC transmission lines. Then the CNN is used to classify the feature images to realize the fault identification of DC transmission lines. Compared with previous methods, this method can apply the mature image classification algorithm in artificial intelligence algorithm to DC transmission line fault identification and realize the rapid identification of DC transmission line fault types. The simulation results show that this method can only use the fault data within 1.5 ms after the fault to achieve 100% accuracy of fault recognition. When the fault data acquisition window is reduced to 1 ms it still has 99.88% classification accuracy, which has certain advantages in the speed and accuracy of DC transmission line fault recognition.