Load modeling based on power quality monitoring system applied compressed sensing

Power quality disturbances carry a large amount of information reflecting the operating conditions of the system and equipment, providing a data source for load modeling. Avoiding high complexity of computation in sampling side and waste of hardware, the compressed sensing (CS) theory is applied to processing the power quality monitoring signal. On the basis of the disturbance data processed by compressed sensing, the approach to data processing and load modeling is analyzed. Moreover, considering the correlation among power quality signals, an improved SP recovery algorithm is proposed. The method realizes the accurate reconstruction of the power quality disturbance data and shortens the reconstruction period. Finally, the experiment simulation results verify that the load model identified by the recovered data is effective in good self-fitting ability and adaptability.

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