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Real-time usage of individual electrical appliances is a key enabler of important advanced services for smart grids. With wide deployments of smart meters, there is a growing interest in using Non-Intrusive Load Monitoring (NILM) to acquire this information from the meter measurements. However, electrical signatures extracted from utility-side smart meters are often unreliable for NILM due to their large sampling intervals. This paper presents a new approach of using high-frequency current waveforms sampled periodically at a main branch to track reliably the on/off states of appliances in real-time. We develop an incremental training algorithm and a robust detection algorithm for the harmonic signatures, based on semi-supervised learning and a hidden Markov model, respectively. We evaluate the performance of the training and detection algorithms using simulations and a proof-of-concept testbed with five …