| 引用本文: | 谢 静,肖 韩,刘志坚,等.基于多模态改进残差网络的输电线路绝缘子覆冰类型识别方法[J].电力系统保护与控制,2026,54(01):130-142.[点击复制] |
| XIE Jing,XIAO Han,LIU Zhijian,et al.A multimodal enhanced ResNet-based method for identifying icing type on transmission line insulators[J].Power System Protection and Control,2026,54(01):130-142[点击复制] |
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| 摘要: |
| 电力输电设备覆冰不仅会增加绝缘子表面的负荷，还会导致电弧击穿和绝缘失效等严重问题，威胁电力输送的可靠性与安全性。传统的人工观察、图像处理的边缘检测和基于支持向量机(support vector machine, SVM)的方法受限于复杂的环境和不稳定的气象条件，难以满足实时监测和精确分类的需求。为此，提出了一种基于多模态改进残差网络的深度学习模型。该模型结合图像特征、覆冰图像的纹理特征以及气象数据3种模态，通过特征层融合提升覆冰类型分类的准确性。首先对覆冰图像进行去雾处理，利用基于暗通道先验的改进型去雾算法去除雾霾干扰，显著提升图像的清晰度和对比度。然后，通过灰度共生矩阵(gray-level co-occurrence matrix, GLCM)提取去雾后图像的纹理特征，并结合改进的残差网络(residual network, ResNet)对纹理特征和图像特征进行处理，以全面捕捉覆冰图像中的细微结构和表面特性。接着，构建包含温度、湿度、风速的气象信息数据集。最后，将图像特征、纹理特征与气象特征相结合，形成融合多模态特征的深度学习模型。通过现场实际工况下的绝缘子覆冰样本的训练和测试，算法对覆冰类型识别的准确率达到92.9％，验证了去雾技术与融合多模态特征的深度学习模型在提升覆冰类型识别精度方面的有效性。 |
| 关键词: 绝缘子覆冰 类型识别 纹理特征 微气象 动态赋权 |
| DOI：10.19783/j.cnki.pspc.250052 |
| 投稿时间：2025-01-15修订日期：2025-07-15 |
| 基金项目:云南省重点基金项目资助(202303AA080002)“高海拔极端环境下输电线路智能运维关键技术及系统研发” |
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| A multimodal enhanced ResNet-based method for identifying icing type on transmission line insulators |
| XIE Jing,XIAO Han,LIU Zhijian,LONG Zhihong,ZHANG Delong,HAN Yirui,ZHANG Shuailong |
| (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China) |
| Abstract: |
| Icing on power transmission equipment not only increases the mechanical load on insulator surfaces but can also lead to arc flashover and insulation failure, thereby posing significant threats to the reliability and safety of power delivery. Traditional approaches, such as manual visual inspection, edge detection-based image processing, and support vector machine (SVM)-based classification, are constrained by complex environmental conditions and unstable meteorological factors, making it difficult to achieve real-time monitoring and accurate classification. To address these challenges, a multimodal deep learning model based on an improved residual network (ResNet) is proposed. The model integrates three features: image features, texture features from icing images, and meteorological data, and enhances classification accuracy through feature-level fusion. First, an improved dehazing algorithm based on the dark channel prior (DCP) is employed to reduce haze interference, significantly enhancing image clarity and contrast. Subsequently, texture features are extracted from the dehazed images using the gray-level co-occurrence matrix (GLCM). These texture features are combined with image features processed using the improved ResNet to comprehensively capture fine structures and surface characteristics of icing. Next, a meteorological dataset comprising temperature, humidity, and wind speed is then constructed and integrated into the model. By fusing image, texture, and meteorological features, robust multimodal feature learning is achieved. Experimental results on real-world insulator icing samples show that the proposed model reaches an accuracy of 92.9% in icing type identification, demonstrating the effectiveness of the dehazing technique and multimodal deep learning framework in improving classification performance. |
| Key words: insulator icing type identification texture feature micro-meteorology dynamic weighting |
