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This paper focuses on identifying voltage sag-sensitive loads within unknown load type or when the load is already in operation. To achieve this, a new method of sensitive load identification based on power quality monitoring data is proposed. Firstly, the active power RMS monitoring data is used as the base data. The Hodrick-Prescott filtering and sliding mean segmentation are used to divide the period of the voltage sag event. Next, based on the division result, the differences of steady-state power quality monitoring data before and after each event are calculated as the dataset. The dynamic K-means is used to divide various load action areas. Finally, the voltage tolerance curves of each action area are fitted and compared with the preset curves, then according to the constituted rules in this paper, the type of sensitive load contained by user is recognized. The feasibility and accuracy of the proposed method are verified by analyzing the simulation examples and actual power quality monitoring data.© 2017 Elsevier Inc. All rights reserved.

Existing non-intrusive load identification methods face with the difficulty of being applied to industrial users whose operation and ON/OFF status are hard to be acquired. Focusing on the above challenge, this paper proposes a non-intrusive identification method of harmonic source loads for industrial users. Utilizing Clarke transform, an equation with respect to voltage and current is formulated to calculate the integrated equivalent impedance, which is then decomposed with the improved complex local mean decomposition (CLMD) and complex fast independent component analysis (C-FastICA) achieving non-intrusive separation of the equivalent impedance signals of individual branches. The separated impedance signals will be used as the basis for load type identification. The proposed method is validated by numerical simulations as well as actual three-phase voltage and current monitoring data. The case study shows that it can effectively identify the types of harmonic source loads while being resistant to noise and impacts of sampling accuracy and diverse load capacities, which obviously demonstrates its practical applicability. [ABSTRACT FROM AUTHOR]