A Fault Diagnosis Method in VSC-HVDC Simulation System Based on BRBP Neural Networks

Hao Yang Cui; Yong Peng Xu; Jun Jie Yang; Jun Dong Zeng; Zhong Tang

doi:10.4028/www.scientific.net/AMR.860-863.2269

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 860-863A Fault Diagnosis Method in VSC-HVDC Simulation...

A Fault Diagnosis Method in VSC-HVDC Simulation System Based on BRBP Neural Networks

Abstract:

As the feature of faulty signal in high voltage direct current transmission technology based on voltage source converter (VSC-HVDC) system is complicated to extract and its difficult to carry on the fault diagnosis. On the basis of the PSCAD simulation model of VSC-HVDC system, the DC current faulty signal is analyzed. Then, the wavelet analysis method was adopted to extract the eigenvector of faulty signal, and combined with method of Bayesian regularization back-propagation (BRBP) neural networks, the system fault was identified. The simulation results show that the method is more efficiently and more rapidly than the adding momentum BP neural network on the VSC-HVDC system faults diagnosing.