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

Abstract

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 paper explores a mixed data augmentation-based intelligent recognition method to detect self-blast state of glass insulator, by imitating the human cognitive mode. Firstly, the mixed data augmentation is utilized to obtain the high-quality and rich sample features. Secondly, feature maps with strong semantics and adaptive multi-scale fusion are extracted using the feature pyramid network with adaptive hierarchy (FPN-A). Then, the extracted feature maps are transmitted to a two-dimensional stochastic configuration network (2DSCNs) to develop the self-blast state classification criteria with universal approximation capability. Thirdly, based on the generalized error and entropy theory, the self-optimizing adjustment and reconstruction of the feature map space with strong semantics and multi-scale fusion and its classification criteria are realized. Finally, the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy convolutional networks to improve the robustness of the recognition model. Results of experimental comparison with other machine learning and deep learning methods verify the feasibility and effectiveness of the proposed method.