Polymeric materials degrade over time, leading to insulation failures. Finding
new materials with strong dielectric properties and robust mechanical strength is crucial for reliable electricity supply. These materials must address defects from manufacturing faults, water tree traces, or mechanical stresses. Polyurethane (PU) is known for its self-healing properties, notably its ability to recover from surface dents due to high elasticity. However, self-healing decreases with increased hardness. This study investigates PU's dielectric breakdown properties at varying hardness levels, revealing an increase in breakdown strength with hardness. Balancing hardness and self-healing are essential, providing insights into PU's dielectric properties. In parallel to engineering novel insulating materials, detecting defects early is crucial to prevent deterioration from partial discharges (PDs). Detecting PDs using acoustic emission (AE) signals is not fully explored for cable insulation. Therefore, the study investigates acoustic pulse propagation from PD events in polymer using finite element methods (FEM) in COMSOL. An analytical model in MATLAB quantifies the impact of multiple propagation paths in a cylinder, aided by a Perfect Matched Layer (PML) in the COMSOL model for
reflection-free modelling. The models reveal that acoustic pulse magnitudes
decrease rapidly with distance, following the inverse square law. Moreover, the study also explores the effects of PU hardness on the propagation characteristics of the AE signal, revealing a high decay rate in AE signal peak magnitude and energy with increasing PU hardness. Frequency spectra analysis indicates stronger attenuation of higher frequency components with distance. The study's revelations on the impact of PU hardness on AE signal characteristics provide engineers with valuable insights for material selection in high voltage systems and applying the AE detection technique to locate the PD events. Industries stand to benefit from material choices, leading to enhanced system reliability and potential cost savings in maintenance.
| Date of Award | 3 Jun 2024 |
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| Original language | English |
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| Awarding Institution | - University Of Strathclyde
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| Sponsors | University of Strathclyde |
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| Supervisor | Martin Given (Supervisor), Wah Hoon Siew (Supervisor) & John Liggat (Supervisor) |
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