1.Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2.Tangshan Power Supply Company of North Hebei Electric Power Co. Ltd, Tangshan, Hebei 063000, China
Abstract：A method of power quality disturbance identification based on CEEMD and GG clustering is proposed. CEEMD is a kind of CEMD improved algorithm, its characteristic is putting positive and negative pairs of white noise into the original signal, helps to reduce the residual noise in the auxiliary signal reconstruction; and adding special noise in every stage of decomposition, calculating a unique residual to get each IMF, the decomposition result is complete. Superior to EEMD, CEEMD not only effectively solve the problem of EMD mode mixing, but also retains the advantages of EMD processing non-stationary signals. The CEEMD decomposition of the IMF component of the cross approximate entropy as feature vector is inputted into the GG fuzzy classifier to classify the electric disturbance, The simulational experimental results show that this method has better spectrum separation effect, and needs less iteration times, reduce the computational cost.
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2019, Vol. 40 
