This paper proposed an improved temperature prediction model for oil-immersed transformer.The influences of the environmental temperature and heat-sinking capability changing with temperature were considered.When calc...This paper proposed an improved temperature prediction model for oil-immersed transformer.The influences of the environmental temperature and heat-sinking capability changing with temperature were considered.When calculating the heat dissipation from the transformer tank to surroundings,the average oil temperature was selected as the node value in the thermal circuit.The new thermal models will be validated with the delivery experimental data of three transformers: a 220 kV-300 MV.A unit,a 110 kV40 MV.A unit and a 220 kV-75 MV.A unit.Meanwhile,the results from the proposed model were also compared with two methods recommended in the IEC loading guide.展开更多
The paper provides a general overview of chemical processes leading to the degradation of oil-paper insulation in oil-immersed electrical current transformers. Previous knowledge available in literature is complemente...The paper provides a general overview of chemical processes leading to the degradation of oil-paper insulation in oil-immersed electrical current transformers. Previous knowledge available in literature is complemented by new results placing a specific emphasis on the physicochemical factors which affect the copper release in the insulation oil and the oil oxidation kinetics. It is demonstrated that various ageing processes interact with each other, with one or another process dominating under specific conditions. Comprehensive but disjoint studies focusing on separate sub-processes may produce rather misleading results, and occasionally, lie behind rather irrelevant quality demands imposed on the insulating liquids.展开更多
针对无人机场景下行人重识别所呈现的多视角多尺度特点,以及传统的基于卷积神经网络的行人重识别算法受限于局部感受野结构和下采样操作,很难对行人图像的全局特征进行提取且图像空间特征分辨率不高。提出一种无人机场景下基于Transfor...针对无人机场景下行人重识别所呈现的多视角多尺度特点,以及传统的基于卷积神经网络的行人重识别算法受限于局部感受野结构和下采样操作,很难对行人图像的全局特征进行提取且图像空间特征分辨率不高。提出一种无人机场景下基于Transformer的轻量化行人重识别(Lightweight Transformer-based Person Re-Identification,LTReID)算法,利用多头多注意力机制从全局角度提取人体不同部分特征,使用Circle损失和边界样本挖掘损失,以提高图像特征提取和细粒度图像检索性能,并利用快速掩码搜索剪枝算法对Transformer模型进行训练后轻量化,以提高模型的无人机平台部署能力。更进一步,提出一种可学习的面向无人机场景的空间信息嵌入,在训练过程中通过学习获得优化的非视觉信息,以提取无人机多视角下行人的不变特征,提升行人特征识别的鲁棒性。最后,在实际的无人机行人重识别数据库中,讨论了在不同量级主干网和不同剪枝率情况下所提LTReID算法的行人重识别性能,并与多种行人重识别算法进行了性能对比,结果表明了所提算法的有效性和优越性。展开更多
文摘This paper proposed an improved temperature prediction model for oil-immersed transformer.The influences of the environmental temperature and heat-sinking capability changing with temperature were considered.When calculating the heat dissipation from the transformer tank to surroundings,the average oil temperature was selected as the node value in the thermal circuit.The new thermal models will be validated with the delivery experimental data of three transformers: a 220 kV-300 MV.A unit,a 110 kV40 MV.A unit and a 220 kV-75 MV.A unit.Meanwhile,the results from the proposed model were also compared with two methods recommended in the IEC loading guide.
文摘The paper provides a general overview of chemical processes leading to the degradation of oil-paper insulation in oil-immersed electrical current transformers. Previous knowledge available in literature is complemented by new results placing a specific emphasis on the physicochemical factors which affect the copper release in the insulation oil and the oil oxidation kinetics. It is demonstrated that various ageing processes interact with each other, with one or another process dominating under specific conditions. Comprehensive but disjoint studies focusing on separate sub-processes may produce rather misleading results, and occasionally, lie behind rather irrelevant quality demands imposed on the insulating liquids.
文摘针对无人机场景下行人重识别所呈现的多视角多尺度特点,以及传统的基于卷积神经网络的行人重识别算法受限于局部感受野结构和下采样操作,很难对行人图像的全局特征进行提取且图像空间特征分辨率不高。提出一种无人机场景下基于Transformer的轻量化行人重识别(Lightweight Transformer-based Person Re-Identification,LTReID)算法,利用多头多注意力机制从全局角度提取人体不同部分特征,使用Circle损失和边界样本挖掘损失,以提高图像特征提取和细粒度图像检索性能,并利用快速掩码搜索剪枝算法对Transformer模型进行训练后轻量化,以提高模型的无人机平台部署能力。更进一步,提出一种可学习的面向无人机场景的空间信息嵌入,在训练过程中通过学习获得优化的非视觉信息,以提取无人机多视角下行人的不变特征,提升行人特征识别的鲁棒性。最后,在实际的无人机行人重识别数据库中,讨论了在不同量级主干网和不同剪枝率情况下所提LTReID算法的行人重识别性能,并与多种行人重识别算法进行了性能对比,结果表明了所提算法的有效性和优越性。