Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

Prognostic modeling for electrical treeing in solid insulation using pulse sequence analysis

Nur Hakimah Binti Ab Aziz, N and Catterson, Victoria and Judd, Martin and Rowland, S.M. and Bahadoorsingh, S. (2014) Prognostic modeling for electrical treeing in solid insulation using pulse sequence analysis. In: 2014 IEEE Conference on Electrical Insulation and Dielectric Phenomena, 2014-10-19 - 2014-10-22.

PDF (Aziz-etal-CEIDP2014-prognostic-modeling-for-electrical-treeing)
Aziz_etal_CEIDP2014_prognostic_modeling_for_electrical_treeing.pdf - Accepted Author Manuscript

Download (2MB) | Preview


This paper presents a prognostic framework for estimating the time-to-failure (TTF) of insulation samples under electrical treeing stress. The degradation data is taken from electrical treeing experiments on 25 epoxy resin samples. Breakdown occurs in all tests within 2.5 hours. Partial discharge (PD) data from 18 samples are used as training data for prognostic modeling and 7 for model validation. The degradation parameter used in this model is the voltage difference between consecutive PD pulses, which decreases prior to breakdown. Every training sample shows a decreasing exponential trend when plotting the root mean squared (RMS) of the voltage difference for 5 minute batches of data. An average model from the training data is developed to determine the RMS voltage difference during breakdown. This breakdown indicator is verified over three time horizons of 25, 50 and 75 minutes. Results show the best estimation of TTF for 50 minutes of data, with error within quantified bounds. This suggests the framework is a promising approach to estimating insulation TTF.