Research on intelligent recognition method for self-blast state of glass insulator based on mixed data augmentation  

作　　者：Siyao Peng Lijian Ding Weitao Li Wei Sun Qiyue Li 

机构地区：[1]Hefei University of Technology,Hefei,China

出　　处：《High Voltage》2023年第4期668-681,共14页高电压（英文）

基　　金：National Natural Science Foundation of China,Grant/Award Number:62173120;Natural Science Foundation of Anhui Province,Grant/Award Number:2108085UD11;111 Project,Grant/Award Number:BP0719039。

摘　　要：Automatically and accurately detecting the self-blast state of glass insulators is of great significance to operation and maintenance of transmission lines.To solve the shortcomings of the existing open-loop cognitive models to detect the self-blast state of glass insulators,this study explores a mixed data augmentation-based intelligent recognition method to detect the self-blast state of the glass insulator,by imitating the human cognitive mode.Firstly,generative adversarial network is utilised to obtain the highquality generative self-blast samples of the glass insulator,and the non-generative data augmentation techniques is used to obtain rich sample features.Secondly,considering the characteristics of aerial images such as large scale variations,variable shooting angles and complex backgrounds,feature maps with strong semantics and adaptive multi-scale fusion are extracted using the feature pyramid network with adaptive hierarchy and the multi-deformable convolutional network.Then,the extracted feature maps are transmitted to a two-dimensional stochastic configuration network that can adaptively generate hidden nodes and basis functions so as to develop the self-blast state classification criteria with universal approximation capability.Thirdly,based on the generalised error and entropy theory,the semantic error entropy evaluation indices of recognition results are defined to evaluate in real time,the credibility of the uncertain recognition results for the self-blast state of the glass insulator.Then,based on transfer learning and the established self-optimising feedback mechanism for feature pyramid network,the self-optimising adjustment and reconstruction of the feature map space with strong semantics and multi-scale fusion and its classification criteria are realised.Finally,the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy and multi-channel deformable convolutional networks to improve the robustness of the recognition model.Results

关 键 词：INSULATOR NETWORK STATE 

分 类 号：TM216[一般工业技术—材料科学与工程]