Novel reconstruction method of power quality data based on regularized adaptive matching pursuit algorithm

In order to solve the problems of resource wasting and low reconstruction performance that traditional methods face when used in the acquisition and compression of power quality signals. This paper proposes a reconstruction method of power quality data based on regularized adaptive matching pursuit algorithm. This method uses compressive sensing theory to conduct the sampling, compression and processing of the power quality signals. Firstly, the atoms in the perception matrix are selected first time and the correlation coefficients are calculated. The selected index values of the atoms are then kept in the candidate set. Then, under the precondition that the sparsity K of the power quality signals is not used as the prior condition, this method adaptively adjusts the number of the atoms in the candidate set and realizes the second time selection of the support set with regularization processing. The step size is used to gradually approach the proper sparsity K of the signals; and then, the original power quality signals can be reconstructed precisely. Simulation experiment results show that the reconstruction accuracy of the signals is higher than 98.2% and the most energy of the original power quality signals can be preserved. Also, the signal to noise ratio of the reconstructed signals is high and the mean square error is small. ©, 2015, Science Press. All right reserved.