国网新疆电力有限公司电力科学研究院,乌鲁木齐 830011
新疆输变电设备极端环境运行与检测技术重点实验室,乌鲁木齐 830011
国网新疆电力有限公司,乌鲁木齐 830011
朴文泉(1983—),男,硕士,高级工程师,主要研究方向为输变电工程安全技术、电网设备技术监督、变电设备状态分析等(通信作者) (E-mail:llbxdamcs@163.com)。
收稿:2026-01-11,
修回:2026-03-01,
纸质出版:2026-07-16
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朴文泉, 杨洋, 李晓光, 等. 多区域注意力机制变压器高压套管爆炸起火部位判定方法[J]. 高压电器, 2026,62(7):230-236.
PIAO Wenquan, YANG Yang, LI Xiaoguang, et al. Method for Determining the Explosion and Fire Location of High Voltage Bushings with Multi-region Attention Mechanism[J]. High Voltage Apparatus, 2026, 62(7): 230-236.
朴文泉, 杨洋, 李晓光, 等. 多区域注意力机制变压器高压套管爆炸起火部位判定方法[J]. 高压电器, 2026,62(7):230-236. DOI: 10.13296/j.1001-1609.hva.2026.07.024.
PIAO Wenquan, YANG Yang, LI Xiaoguang, et al. Method for Determining the Explosion and Fire Location of High Voltage Bushings with Multi-region Attention Mechanism[J]. High Voltage Apparatus, 2026, 62(7): 230-236. DOI: 10.13296/j.1001-1609.hva.2026.07.024.
为准确定位击穿位置,明确变压器高压套管爆炸起火部位,提出基于多区域注意力机制的变压器高压套管爆炸起火部位判定方法。构建变压器高压套管区域检测网络模型,将变压器高压套管原始图像作为特征提取网络的输入,获取其全局特征后,利用多区域判定模块确定可能发生爆炸起火的感兴趣区域,通过非局部关注模块捕捉各感兴趣区域位置间的联系,以感兴趣区域为注意力信息,通过基于CBAM注意力模块的预测网络实现原始变压器高压套管图像和感兴趣区域图像通道、空间信息的融合,利用通道注意力权重提升模型对关键特征通道信息的捕捉,实现变压器高压套管爆炸起火部位的精准判定。实验结果表明:该方法具有变压器高压套管爆炸起火部位判定能力,当提取的感兴趣区域为3、threshold阈值为0.3时,判定精度最优。
To ccurately locate the breakdown position and identify the explosion and fire location of the transformer high-voltage bushing
the location determination method for explosion and fire of the transformer high-voltage bushing based on the multi-region attention mechanism is proposed. A detection network model for the transformer highvoltage bushing area is constructed.The original image of the transformer high-voltage bushing is used as the input of the feature extraction network to obtain its global features. Then
a multi-region determination module is used to identify the regions of interest where explosions and fires may occur. A non-local attention module is adopted to capture the relationship among the locations of each region of interest. Through a prediction network based on CBAM attention module
the fusion of channel and spatial information between the original transformer high-voltage bushing image and the region-of-interest images is achieved. The channel attention weights are used to enhance the model's ability to capture key feature channel information
thereby realizing precise determination of the explosion and fire location of the transformer high-voltage bushing. Experimental results show that this method is capable of determining the explosion and fire location of transformer high-voltage bushing. When the number of extracted region of interest is 3 and the threshold value is 0.3
the determination accuracy is optimal.
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