Insulation health decision support model
Project description
Due to the energy transition non-conventional power sources and power loads are connected to our electricity grid. This non-conventional equipment type, mostly power electronics, will impose high frequency transients & harmonics, ripple and DC-components to the conventional power grid components, with yet unknown effects on degradation mechanism and remaining life. Furthermore, power grid components are loaded differently due to large scale renewable integration (e.g. suddenly higher or with large load variations), also having expected other (and partly already experienced) changes in degradation and failure modes. Even after failure it is expected that traces of pre-failure degradation will differ depending on the type of stress subjected to the failed equipment.
The study is designed into two main topics:
- Decision support model where DNV’s existing health index methodology will be enhanced by an automated trend analysis tool (Prediction), allowing more advanced predictions of health and degradation based on historical results. In the graph shown as the blue part. The PhD-applicant has performed already a substantial amount of the health index methodology research and development in the past which can be utilized during this PhD-study. This work is anticipated to consume approximately 25% of the PhD effort, because the Health and Risk models are already developed, and the prediction part is to be newly developed in this PhD-study. During the PhD-work validation of the model will assessed.
- Fundamental research on degradation of insulation media. Within this PhD-study, insulation media will be subjected to many different types of voltage, thermal and or mechanical stresses to identify different degradation behaviour and traces of degradation, e.g. electrical treeing. The aim of his study is to identify classifications of types of stresses and degradation and identify knowledge rules for degradation mechanism and failure modes.
Both Technical Universities of Eindhoven and Delft are supporting this PhD-study. DNV sponsors this PhD project and Mischa is working on the project for two days per week (Thursdays and Fridays) bi-weekly in Eindhoven and Delft.
PhD Candidate: Mischa Vermeer m.e.vermeer@tue.nl
Supervisor:
Dr.ir. Mohamad Ghaffarian Niasar: M.GhaffarianNiasar@tudelft.nl
Promotors:
Prof. Dr. Ir. Peter van der Wielen: peter.vanderwielen@dnv.com
Prof. Ir. Peter Vaessen: P.T.M.Vaessen@tudelft.nl