摘要
负荷辨识技术可快速辨识电器类型,在家庭能量管理、危险用电预警、响应潜力评估等方面具有重要作用。针对现有负荷辨识方法多关注负荷长期或短期单尺度特征,导致特征表征能力不足而使模型识别精度和泛化性能受限的问题,提出一种基于多尺度特征融合的负荷辨识及其可解释交互增强方法。首先,从负荷采样数据中提取高频尺度的短期特征和中、低频尺度的长期特征,构建双塔结构的深层特征提取网络以利用网络的不同分支高效率挖掘各尺度深层特征。其次,设计自注意力与交叉注意力相结合的特征融合网络以实现负荷长、短期特征融合,提高模型的特征利用程度。然后,采用度量学习的训练方法,拉近同类型样本的特征距离,提升特征融合的效率和效果。最后,利用基于梯度的可解释分析方法量化特征的重要性,实现自适应的特征增强与结合专家交互的模型调优。实验结果说明所提模型识别精度与泛化能力均优于现有模型,且可解释分析验证了其有效性源于多尺度特征的充分利用。
Load identification technology can quickly identify the types of electrical appliances,and plays an important role in household energy management,hazardous warning,response potential assessment and other aspects.Aiming at the problem that the existing load identification methods focus on the long-term or short-term single-scale load features,resulting in insufficient feature representation capabilities,and thus limiting model identification accuracy and generalization performance,a load identification and its interpretable interactive enhancement method based on multi-scale feature fusion is proposed.Firstly,shortterm features of high-frequency scale and long-term features of medium-and low-frequency scales are extracted from the load sampling data,and a deep feature extraction network with a double-tower structure is constructed to efficiently mine deep features of each scale by using different branches of the network.Secondly,a feature fusion network combining self-attention and crossattention is designed to fuse long-term and short-term features,improving the feature utilization of the model.Then,a training method of metric learning is used to shorten the feature distance of the same type of samples and improve the efficiency and effect of feature fusion.Finally,the interpretable analysis method based on gradient is used to quantify the importance of features,and realize the adaptive feature enhancement and model optimization combined with expert interaction.The experimental results show that the recognition accuracy and generalization ability of the proposed model are superior to those of existing models,and the interpretable analysis verifies that its effectiveness comes from the full use of multi-scale features.
作者
刘熙鹏
罗庆全
余涛
蓝超凡
蔡清淮
吴毓峰
LIU Xipeng;LUO Qingquan;YU Tao;LAN Chaofan;CAI Qinghuai;WU Yufeng(School of Electric Power Engineering,South China University of Technology,Guangzhou 510640,China;Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid,Guangzhou 510640,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2024年第2期105-117,共13页
Automation of Electric Power Systems
基金
国家自然科学基金委员会-国家电网公司智能电网联合基金资助项目(U2066212)
中国博士后科学基金资助项目(2022M721184)。
关键词
负荷辨识
多尺度特征
特征融合
度量学习
可解释分析
load identification
multi-scale feature
feature fusion
metric learning
interpretable analysis
