Recognizing VSC DC Cable Fault Types Using Bayesian Functional Data Depth

Abstract

Diagnostics of power and energy systems is obviously an important matter. In this paper we present a contribution of using new methodology for the purpose of signal type recognition (for example, faulty/healthy or different types of faults). Our approach uses Bayesian functional data analysis with data depths distributions to detect differing signals. We present our approach for discrimination of pole-to-pole and pole-to-ground short circuits in VSC DC cables. We provide a detailed case study with Monte Carlo analysis. Our results show potential for applications in diagnostics under uncertainty.

Keywords

• cable diagnostics
• Bayesian statistics
• functional data analysis
• uncertainty
• Hamiltonian Monte Carlo
• data depth