中圖分類號(hào)： TP391
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.200223
中文引用格式： 萬(wàn)濤，吳立剛，陸燁，等. 基于多尺度網(wǎng)絡(luò)的絕緣子自曝狀態(tài)智能認(rèn)知方法研究[J].電子技術(shù)應(yīng)用，2021，47(8)：91-96.
英文引用格式： Wan Tao，Wu Ligang，Lu Ye，et al. Research on intelligent cognition method of insulator self-blast state based on multi-scale network[J]. Application of Electronic Technique，2021，47(8)：91-96.

Research on intelligent cognition method of insulator self-blast state based on multi-scale network

Abstract： In view of the drawbacks of the existing insulator state recognition models, and the scale and softmax loss function of deep network, imitating the mode of personnel operation and maintenance, that is, dynamic decision-making based on the credibility of the evaluation results, this paper constructs an intelligent cognition method of insulator self-blast states based on the multi-scale network. Firstly, for the pre-processed insulator images with localization and normalization, based on ResNet-18, branches with different network structure are added to improve the network ability to adapt to different resolutions. At the same time, the multi-scale information fusion module is added at the end of the network. Secondly, facing multiple scale features, stochastic configuration network(SCN) constructs a generalized cognition criterion of self-blast state classification. Finally, in order to evaluate the credibility of the self-blast state cognition result, based on the defined error index, the multi-scale network architecture is self-adjusted to reconstruct the feature vector and classification cognition criterion under the constraint of the uncertain cognition result, which carries out the self-blast state renewal cognition.The experimental results show that the proposed intelligent cognition method enhances the generalization ability and cognition accuracy compared with other methods.

Key words : insulator state；ResNet；feedback cognition；multi-resolution；multi-scale