A new measurement method for power signatures of non-intrusive demand monitoring and load identification

Based upon the analysis of load signatures, this paper presents a Non-intrusive load monitoring (NILM) technique. With characterizing associated with the transient response of energy signature, a reliable and accurate recognition result can be obtained. In this study, artificial neural networks (ANN), in combination with turn-on transient energy analysis, are used to improve recognition accuracy and computational speed of NILM results. To minimize the distortion phenomenon in current measurements from the hysteresis of traditional current transformers (CTs) iron cores, coreless Hall effect current transformer is adopted to accurately detect non-sinusoidal waves to improve NILM accuracy. The experimental results indicate that the incorporation of turn-on transient energy algorithm into NILM significantly improve the recognition accuracy and computational speed.