Dr. Ir. Yeo was awarded the B.Eng. Degree (first class honours) in Electrical Engineering from Queensland University of Technology in 2014. He later studied in the high voltage laboratory of the Technical University of Delft where he obtained his M.Sc. degree in 2016. He completed the Engineering Doctorate in 2021 at the Singapore University of Technology and Design on the topic of partial discharge and deep learning application. Currently, he is an engineer in the condition monitoring section of the SP Group, where he focuses on AI application on partial discharge analysis.
Partial discharge diagnostics has become a fundamental process at modern electricity supply utilities. Moreover, research into the physics of PDs has enabled development of monitoring systems with high resolution and reliability to the point that spot testing and online diagnostic efforts generally yield favourable results. This presentation explains an algorithmic approach constructed by a convolutional recurrent neural network coupled with feature engineering for PD analysis measured from offline medium voltage cable tests. Two case are outined where results confirm that the methodology is able to identify and accurately localize discharge activity.