Non-Intrusive Load Identification Using Reconstructed Voltage-Current Images

• Published in: IEEE access Vol. 9; pp. 77349 - 77358
• Main Authors: Jia, Dongning, Li, Yunxin, Du, Zehua, Xu, Jiali, Yin, Bo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Convolutional neural networks; Electric appliances; Electric potential; Electric power grids; Energy management; Feature extraction; Home appliances; Household appliances; Image reconstruction; load disaggregation; Monitoring; non-intrusive load monitoring; Power consumption; reconstructed VI image; Threshold voltage; Trajectory; Voltage; Waveforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3082432

Non-intrusive load monitoring (NILM) is crucial because it helps monitor the operating status of electrical appliances online; detailed power consumption data regarding the appliances can then be obtained. However, the identification of resistive appliances that have similar features in a power grid is still a major problem. In this study, the reconstructed image of a voltage-current (VI) trajectory is used as input data for a convolutional neural network (CNN) to classify the appliances, particularly resistive appliances. Two dataset PLAID and IDOUC are introduced to verify the performance of the proposed method. According to the results, the excellent performance of the reconstructed VI image method for the identification of the household appliances with similar waveform is validated by comparing it with the other two methods.