DING Jian;CHEN Jianxiang;LIU Wangpeng;DING Yifan;WANG Puyu;

• 1:School of Automation
• 2: Nanjing University of Science and Technology

Abstract：

Current research on non-intrusive load monitoring(NILM) based on V-I trajectories faces challenges such as low recognition accuracy for appliances with similar trajectories and complex network structures. To address these issues, the paper proposes a non-intrusive load monitoring method based on improved color coding and dense convolutional network(DenseNet). First, the paper analyzes the principles of the event detection algorithm based on a sliding window. Then, it introduces an improved color coding algorithm for plotting V-I trajectories to address the problem of diverse appliance types in low-power ranges and to enhance the differentiation of V-I trajectories.Next, the plotted V-I trajectories are used to train the dense convolutional network, resulting in a recognition model suitable for low-resolution V-I trajectories. Finally, validation on the PLAID and WHITED demonstrates that the proposed method achieves higher accuracy and recognition efficiency.

Key Words： NILM;V-I trajectory;image recognition;DenseNet;household appliance

Abstract:

Keywords:

Foundation: 江苏省自然科学基金（BK20242048）

Authors: DING Jian;CHEN Jianxiang;LIU Wangpeng;DING Yifan;WANG Puyu;

DOI: 10.19585/j.zjdl.202502001

References：

• [1]武昕，严萌，郭一凡，等.基于结构化特征图谱的组合支持向量机非侵入式负荷辨识[J].电力系统自动化，2022,46(12):210-219.WU Xin,YAN Meng,GUO Yifan,et al. Non-intrusive load identification by combined support vector machine based on structured characteristic spectrum[J]. Automation of Electric Power Systems,2022,46(12):210-219.
• [2]许烽，陶远超，陆翌，等.基于源荷预测的含多能储能区域电热系统的优化调度[J].浙江电力，2023,42(9):17-26.XU Feng,TAO Yuanchao,LU Yi,et al.Optimal dispatch of regional electricity-thermal system with multi-energy storage based on source and load forecasting[J].Zhejiang Electric Power,2023,42(9):17-26.
• [3]向颖，严慧峰，余旭阳，等.基于特征优选及改进自组织神经网络的非侵入式负荷辨识[J].中国电机工程学报，2022,42（增刊1）:106-114.XIANG Ying,YAN Huifeng,YU Xuyang,et al. Noninvasive load identification based on feature optimization and improved self-organizing neural network[J].Proceedings of the CSEE,2022,42(S1):106-114.
• [4]张玉天，邓春宇，刘沅昆，等.基于卷积神经网络的非侵入负荷辨识算法[J].电网技术，2020,44(6):2038-2044.ZHANG Yutian,DENG Chunyu,LIU Yuankun,et al.Non-intrusive load identification algorithm based on convolution neural network[J]. Power System Technology,2020,44(6):2038-2044.
• [5]周晨轶，闫娇娇，刘晨阳.基于贝叶斯准则的非侵入式负荷监测方法[J].浙江电力，2018,37(5):7-11.ZHOU Chenyi,YAN Jiaojiao,LIU Chenyang. Nonintrusive load monitoring based on Bayes criterion[J].Zhejiang Electric Power,2018,37(5):7-11.
• [6]王异成，罗华峰，马骏超，等.一种小微负荷智能监测与聚合分析系统研究[J].浙江电力，2021,40(12):37-45.WANG Yicheng,LUO Huafeng,MA Junchao,et al.Research on smart monitoring and aggregate analysis system for small and micro loads[J]. Zhejiang Electric Power,2021,40(12):37-45.
• [7]邓晓平，张桂青，魏庆来，等.非侵入式负荷监测综述[J].自动化学报，2022,48(3):644-663.DENG Xiaoping,ZHANG Guiqing,WEI Qinglai,et al.A survey on the non-intrusive load monitoring[J].Acta Automatica Sinica,2022,48(3):644-663.
• [8]张继东，陈雪薇，徐述，等.非侵入式负荷辨识技术研究及应用[J].机电信息，2021(23):1-4.ZHANG Jidong,CHEN Xuewei,XU Shu,et al.Research and application of non-invasive load identification technology[J].Mechanical and Electrical Information,2021(23):1-4.
• [9]杨秀，李安，孙改平，等.基于改进GMM-CNN-GRU混合的非侵入式负荷监测方法研究[J].电力系统保护与控制，2022,50(14):65-75.YANG Xiu,LI An,SUN Gaiping,et al.Non-invasive load monitoring based on an improved GMM-CNN-GRU combination[J].Power System Protection and Control,2022,50(14):65-75.
• [10]陈飞.面向住宅用户的非侵入式负荷监测方法研究[D].贵阳：贵州大学，2022.CHEN Fei. Research on non-invasive load monitoring method for residential users[D].Guiyang:Guizhou University,2022.
• [11]班昭.面向家居负荷的非侵入式负荷辨识方法研究[D].石家庄：河北科技大学，2023.BAN Zhao. Research on non-invasive load identification method for household load[D]. Shijiazhuang:Hebei University of Science and Technology,2023.
• [12] YU Z W,ZHAO R F,LIU B,et al.Deep co-training for semi-supervised non-intrusive load monitoring based on VI trajectory[C]//2023 8th Asia Conference on Power and Electrical Engineering(ACPEE). Tianjin,China. IEEE,2023:1458-1463.
• [13] GAO J K,KARA E C,GIRI S,et al.A feasibility study of automated plug-load identification from high-frequency measurements[C]//2015 IEEE Global Conference on Signal and Information Processing(GlobalSIP).Orlando,FL,USA.IEEE,2015:220-224.
• [14] LAM H Y,FUNG G S K,LEE W K.A novel method to construct taxonomy electrical appliances based on load signaturesof[J]. IEEE Transactions on Consumer Electronics,2007,53(2):653-660.
• [15] DE BAETS L,DHAENE T,DESCHRIJVER D,et al.VI-based appliance classification using aggregated power consumption data[C]//2018 IEEE International Conference on Smart Computing(SMARTCOMP).Taormina,Italy.IEEE,2018:179-186.
• [16] LIU Y C,WANG X,YOU W.Non-intrusive load monitoring by voltage-current trajectory enabled transfer learning[J].IEEE Transactions on Smart Grid,2019,10(5):5609-5619.
• [17] SHI Z L,YIN B.An improved Non-intrusive load identification method for V-I trajectory based on amplitude to pixel value[C]//2021 IEEE 5th Information Technology,Networking,Electronic and Automation Control Conference(ITNEC).Xi’an,China.IEEE,2021:1510-1516.
• [18] WANG S X,CHEN H W,GUO L Y,et al.Non-intrusive load identification based on the improved voltage-current trajectory with discrete color encoding background and deep-forest classifier[J].Energy and Buildings,2021,244:111043.
• [19]解洋，梅飞，郑建勇，等.基于V-I轨迹颜色编码的非侵入式负荷识别方法[J].电力系统自动化，2022,46(4):93-102.XIE Yang,MEI Fei,ZHENG Jianyong,et al. Nonintrusive load monitoring method based on V-I trajectory color coding[J].Automation of Electric Power Systems,2022,46(4):93-102.
• [20]崔昊杨，蔡杰，陈磊，等.基于颜色编码的非侵入式负荷细粒度识别方法[J].电网技术，2022,46(4):1557-1567.CUI Haoyang,CAI Jie,CHEN Lei,et al. Non-intrusive load fine-grained identification based on color encoding[J].Power System Technology,2022,46(4):1557-1567.
• [21]宰州鹏，赵升，朱翔鸥，等.基于颜色编码与谐波特征融合的非侵入式负荷识别方法[J].电气技术，2022,23(12):9-16.ZAI Zhoupeng,ZHAO Sheng,ZHU Xiang’ou,et al.Nonintrusive load monitoring based on color coding and harmonic feature fusion[J]. Electrical Engineering,2022,23(12):9-16.
• [22]杨苗，游文霞，刘玥，等.基于颜色编码和残差神经网络的非侵入式负荷识别[J].电工材料，2024(2):94-99.YANG Miao,YOU Wenxia,LIU Yue,et al. Nonintrusive load identification based on binary V-I trajectory color coding and residual neural network[J].Electrical Engineering Materials,2024(2):94-99.
• [23]张鲁昊.基于人工神经网络的非侵入式负荷识别算法研究[D].杭州：浙江大学，2022.ZHANG Luhao.Research on non-invasive load identification algorithm based on artificial neural network[D].Hangzhou:Zhejiang University,2022.
• [24]王丙楠.基于小样本学习的非侵入式负荷监测方法[D].杭州：浙江大学，2022.WANG Bingnan. Non-intrusive load monitoring methods based on few-shot learning[D].Hangzhou:Zhejiang University,2022.
• [25]牛卢璐，贾宏杰.一种适用于非侵入式负荷监测的暂态事件检测算法[J].电力系统自动化，2011,35(9):30-35.NIU Lulu,JIA Hongjie. Transient event detection algorithm for non-intrusive load monitoring[J].Automation of Electric Power Systems,2011,35(9):30-35.
• [26]钟韬，刘刚，黄蕾，等.基于决策树的非入侵式负荷分解算法的研究[J].计算机应用研究，2020,37（增刊1）:163-165.ZHONG Tao,LIU Gang,HUANG Lei,et al.Research on non-invasive load decomposition algorithm based on decision tree[J].Application Research of Computers,2020,37(S1):163-165.
• [27]郇嘉嘉，汪超群，洪海峰，等.基于图像编码与深度学习的非侵入式负荷识别方法[J].科学技术与工程，2021,21(21):8901-8908.HUAN Jiajia,WANG Chaoqun,HONG Haifeng,et al.Non-intrusive load monitoring method based on color encoding and deep learning[J].Science Technology and Engineering,2021,21(21):8901-8908.
• [28]王守相，郭陆阳，陈海文，等.基于特征融合与深度学习的非侵入式负荷辨识算法[J].电力系统自动化，2020,44(9):103-110.WANG Shouxiang,GUO Luyang,CHEN Haiwen,et al.Non-intrusive load identification algorithm based on feature fusion and deep learning[J]. Automation of Electric Power Systems,2020,44(9):103-110.
• [29]裘星，尹仕红，张之涵，等.基于V-I轨迹与高次谐波特征的非侵入式负荷识别方法[J].电力工程技术，2021,40(6):34-42.QIU Xing,YIN Shihong,ZHANG Zhihan,et al. Nonintrusive load identification method based on V-I trajectory and high-order harmonic feature[J]. Electric Power Engineering Technology,2021,40(6):34-42.
• [30] LI W,DONG T X,WANG Y S, et al.Research on load recognition algorithms based on current distortion and harmonic content[C]//2019 IEEE Symposium Series on Computational Intelligence(SSCI).Xiamen,China.IEEE,2019:2753-2758.
• [31]周任飞，汤鹏飞，刘三丰，等.基于卷积神经网络的非侵入式负荷识别研究[J].信息技术与网络安全，2019,38(8):64-68.ZHOU Renfei,TANG Pengfei,LIU Sanfeng,et al. Research of non-intrusive load identification based on convolutional neural network[J]. Information Technology and Network Security,2019,38(8):64-68.
• [32] HUANG G,LIU Z,VAN DER MAATEN L,et al.Densely connected convolutional networks[C]//2017IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu,HI,USA. IEEE,2017:2261-2269.
• [33]武昕，焦点，高宇辰.基于非侵入式用电数据分解的自适应特征库构建与负荷辨识[J].电力系统自动化，2020,44(4):101-109.WU Xin,JIAO Dian,GAO Yuchen.Construction of adaptive feature library and load identification based on decomposition of non-intrusive power consumption data[J].Automation of Electric Power Systems,2020,44(4):101-109.
• [34]王谱宇，丁一帆，陈鉴祥，等.基于动态谐波导纳参数的非侵入式负荷监测数据模拟生成方法[J/OL].中国电机工程学报，2024:1-14.WANG Puyu,DING Yifan,CHEN Jianxiang,et al.Simulation method of non-intrusive load monitoring data generation based on dynamic harmonic admittance parameters[J/OL].Proceedings of the CSEE,2024:1-14.
• [35] KAHL M,HAQ A U,KRIECHBAUMER T,et al.Whited-A worldwide household and industry transient energy data set[C]//3rd International Workshop NonIntrusive Load Monitoring,Vancouver,BC,Canada,2016:1-5.