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.
Highlights
Effective and reliable monitoring and diagnostics of energy installations is of utmost importance, as they are an important part of the world’s economy
We propose a general algorithm for comparing signals with reference that takes uncertainty under account and uses functional data analysis to provide dimensionality reduction
We present results of our case study applying our algorithm to determining in the voltage source converter (VSC) DC cable the type of fault between pole-to-pole and pole-to-ground short circuits
Summary
Effective and reliable monitoring and diagnostics of energy installations is of utmost importance, as they are an important part of the world’s economy. Algorithms for fault detection and isolation allow extension of system lifetime, reduction in operation interruption and can lead to significant savings. The main difficulty in their development is that power installations have a high level of complexity, are usually nonlinear and are influenced by stochastic disturbances and parameter variations. Approaches based on first principles models are difficult or even impossible to use on a wider scale. That is why methods based on statistical models or machine learning are those most researched. Machine learning, data-driven models are providing complicated ‘black-box’
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
