Applying Symmetrical Component Transform for Industrial Appliance Classification in Non-Intrusive Load Monitoring

Industrial loads offer challenges for Non-intrusive Load Monitoring (NILM), such as phase imbalance associated with 3-phase lines. However, very little NILM research has been developed so far with this respect. This work presents a load recognition technique for NILM applying low complexity Fortesque Transform (FT). The FT decomposes the unbalanced 3-phase current waveform extracted from 3-phase aggregate power measurements to balance the given load. The 3-phases current waveform is transformed into an image-like representation using a compressedeuclidean distance matrix to improve the recognition ability further. The image representation is used as input to Convolutional Neural Network (CNN) classifier to learn the patterns of labeled data. Experimental evaluation of the industrial aggregated dataset shows that FT improves recognition performance by 5.8%, compared to the case without FT.