An Adaptive Detection Algorithm for Micro-grid Harmonic Power Based on Deep Belief Network

Abstract

There are many non-linear load devices in the micro-grid, resulting in a lot of complex harmonics, which is a key problem that leads to low measurement accuracy of electric energy metering devices. The traditional integrated empirical mode decomposition (EEMD) method can effectively deal with the problem of nonlinear and nonstationary signals, but this method has the problem of being highly dependent on artificial pre-set parameters. Here, the deep belief network (DBN) is introduced in the white noise signal generation process of EEMD. The main problems solved are as follows: one is to adaptively match the white noise signal according to the data characteristics of the current signal in the micro-grid, the other is to reduce the artificial setting error and make the separation result closer to the theoretical value. Finally, this paper uses the operating data in the actual environment to carry out experimental verification, and the results show that the error between the value of harmonic power in the production environment and the theoretical value given is reduced by 9.73%.