Back
 EPE  Vol.13 No.4 B , April 2021
Non-Intrusive Load Identification Model Based on 3D Spatial Feature and Convolutional Neural Network
Abstract:
Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID.
Cite this paper: Liu, J. , Liu, N. , Song, H. , Liu, X. , Sun, X. , Zhang, D. (2021) Non-Intrusive Load Identification Model Based on 3D Spatial Feature and Convolutional Neural Network. Energy and Power Engineering, 13, 30-40. doi: 10.4236/epe.2021.134B004.
References

[1]   Grillo, G. and Hart, G.W. (1992) Non-Intrusive Appliance Load Monitoring. Proceedings of the IEEE, 80, 1870-1891. https://doi.org/10.1109/5.192069

[2]   Kang, W.T., Lin, X.H., Shi, S.B., Zhou, D.G., Hu, W.S. and Deng, Q.J. (2019) Non-Intrusive Load Identification Method Based on Two-Dimensional Discrete Fuzzy Numbers. Electrical Measurement & Instrumentation, 56, 13-18.

[3]   Machlev, R., Tolkachov D., Levron Y. and Beck, Y. (2020) Dimension Reduction for NILM Classification Based on Principle Component Analysis. Electric Power Systems Research, 187, Article ID: 106459. https://doi.org/10.1016/j.epsr.2020.106459

[4]   Asres, M.W., Girmay, A.A., Camarda, C. and Tesfamariam, G.T. (2019) Non-Intrusive Load Composition Estimation from Aggregate ZIP Load Models Using Machine Learning. International Journal of Electrical Power & Energy Systems, 105, 191-200. https://doi.org/10.1016/j.ijepes.2018.08.016

[5]   Himeur, Y., Alsalemi, A., Bensaali, F. and Amira, A. (2020) Robust Event-Based Non-Intrusive Appliance Recognition Using Multi-Scale Wavelet Packet Tree and Ensemble Bagging Tree. Applied Energy, 267, Article ID: 114877. https://doi.org/10.1016/j.apenergy.2020.114877

[6]   Xie, S.Y., Li, J.H., Wang, J.F., Xiong, S.J. and Tang, Y. (2019) Non-Invasive Load Identification of Weighted Random Forest Based on Particle Swarm Optimization. Electrical & Energy Management Technology, 2019, 22-26+44.

[7]   Baets, L.D., Ruyssinck, J., Develder, C., Dhaene, T. and Deschrijver, D. (2017) On the Bayesian Optimization and Robustness of Event Detection Methods in NILM. Energy and Buildings, 145, 57-66. https://doi.org/10.1016/j.enbuild.2017.03.061

[8]   Liu, H.Y., Shi, S.B., Xu, X.H., Zhou, D.G., Min, R.L. and Hu, W.S. (2019) A Non-Intrusive Load Identification Method Based on RNN Model. Power System Protection and Control, 47, 162-170.

[9]   Zhang, Y.M., Yang, G.H. and Ma, S.D. (2019) Non-Intrusive Load Monitoring Based on Convolutional Neural Network with Differential Input. 11th CIRP Conference on Industrial Product-Service Systems, China, January 2019, 670-674. https://doi.org/10.1016/j.procir.2019.04.110

[10]   Zhang, Y.T., Deng, C.Y., Liu, Y.K., Chen, S. and Shi, M.J. (2020) Non-Intrusive Load Identification Algorithm Based on Convolution Neural Network. Power System Technology, 44, 2038-2044.

[11]   GAO, J.K., Kara, E.C., Giri, S. and Mario, B. (2015) A Feasibility Study of Automated Plug-Load Identification from High-Frequency Measurements. 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, USA, December 2015, 14-16. https://doi.org/10.1109/GlobalSIP.2015.7418189

[12]   Liu, Y.P., He, J.H., Xu, Z.Q., Wang, Q., Li, Z. and Gao, S.G. (2020) Equalization Method of Power Transformer Fault Sample Based on SVM SMOTE. High Voltage Engineering, 46, 2522-2529.

[13]   Wang, B., Fang, G.D., Liu, S. and Liang, J. (2019) Effect of Heterogeneous Interphase on the Mechanical Properties of Unidirectional Fiber Composites Studied by FFT-Based Method. Composite Structures, 220, 642-651. https://doi.org/10.1016/j.compstruct.2019.04.049

[14]   Wang, S.X., Guo, L.Y., Chen, H.W. and Deng, X.Y. (2020) Non-Intrusive Load Identification Algorithm Based on Feature Fusion and Deep Learning. Automation of Electric Power Systems, 44, 103-110.

[15]   Rao, C.P. and Liu, Y. (2020) Three-Dimensional Convolutional Neural Network (3D-CNN) for Hetero-geneous Material Homogenization. Computational Materials Science, 184, Article ID: 109850. https://doi.org/10.1016/j.commatsci.2020.109850

[16]   Xu, X.F., Caulfield, S., Amaro, J., Falcao, G. and Moloney, D. (2020) 1.2 Watt Classification of 3D Voxel Based Point-Clouds Using a CNN on a Neural Compute Stick. Neurocomputing, 393, 165-174. https://doi.org/10.1016/j.neucom.2018.10.114

[17]   Fei, Z.G., Wu, Z.Y., Xiao, Y.Q., Ma, J. and He, W.B. (2020) A New Short-Arc Fitting Method with High Precision Using Adam Optimization Algorithm. Optik, 212, Article ID: 164788. https://doi.org/10.1016/j.ijleo.2020.164788

 
 
Top