Abstract
For the transient protection of HVDC transmission lines, the transient interference signal of lightning stroke may cause the protection malfunction. Research on accurate and fast identification methods for lightning transients of HVDC transmission lines is of great significance to improve the reliability of power systems. This paper proposes a lightning stroke transient identification method based on deep belief network (DBN) and wavelet energy moment, using wavelet energy moment theory to extract the characteristics of three transient time-domain waveforms to generate sample sets, building a training unit based on the DBN algorithm to construct the basic reliability assignment of a transient signal identification framework. Finally, the transient signal discrimination result can be obtained through the decision rule. Through the example analysis of building a simulation model in PSCAD, the results show that DBN has a higher recognition accuracy rate than the shallow machine learning algorithms in transient identification, and at the same time verifies that the method has a certain practicability.
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