Conference Agenda

Recent developments in instrumentation have demonstrated that it is feasible to monitor the condition of energized cables online using frequency domain reflectometry (FDR) and spread spectrum time domain reflectometry (SSTDR). However, the response spectra from these measurements are complex to interpret and do not lend themselves to simple threshold alarms. To address this challenge, machine learning (ML) techniques have been used, demonstrating high accuracy in predicting normal or anomalous cable behavior when using binary normal and anomalous training and test data. A more plausible scenario for cable insulation damage, however, involves a slowly developing material change due to long-term exposure to thermal or radiation stresses, subtly altering material properties and resulting in an altered reflectometry response. The practical challenge lies in recognizing when such changes are significant enough to raise concern. The Pacific Northwest National Laboratory (PNNL) Accelerated and Real-Time Experimental Nodal Analysis (ARENA) cable motor test bed was used to measure the FDR and SSTDR responses of an energized cable as a section of it was thermally aged over 70 days. Online reflectometry spectra were collected and post-processed to simulate real-time analysis, aiming to distinguish normal from anomalous behavior. These spectra were also contrasted with off-line and direct conductor-coupled reflectometry tests. For use with ML algorithms, it was necessary to label the reflectometry results as either normal or anomalous. This was accomplished with witness samples, which were aged alongside the main cable and periodically tested for elongation at break and tensile strength—two offline destructive tests indicative of cable damage. These destructive tests revealed a natural breakpoint for differentiating the two conditions at ~35 days. Both supervised and unsupervised ML methods were applied. The results indicated that the ML methods could effectively classify cable data as normal or anomalous and that there was a strong correlation between the reflectometry results and the elongation at break and Fourier transform infrared spectroscopy destructive off-line tests of the witness samples. In addition, the online testing was nearly as clear and effective as off-line tests. These findings suggest that the integration of ML techniques with reflectometry can provide a reliable method for monitoring and early detection of cable insulation damage.

In Gas lnsulated Switchgear (GIS), basin-type insulators serve as critical components, providing both insulation support and sealing functions. Due to their complex geometrical structure, these insulators are highly sensitive to electrical field distribution under extreme operating conditions. The details of the geometric model directly influence the concentration and uniformity of the electrical field, making optimized design essential for improving insulation performance and preventing flashover and breakdown. To address the issue of automatic annotation of the geometric model's physical properties, this paper proposes a refined construction method for basin-type insulators based on the integration of 3D reconstruction and numerical simulation. The results show that the proposed automated reconstruction method can effectively calculate the electric field distribution，providing a new avenue for electrical field simulation and design optimization of basin-type insulators.

Over the past 25 years, thousands of kilometres of installed XLPE High Voltage (HV) and Extra High Voltage (EHV) cable systems have been subjected to after-laying commissioning testing prior to energization. The commissioning test usually consists of a combination AC withstand (HiPot) & Partial Discharge (PD) testing. This paper presents more than two decades of experiences with non-monitored and monitored withstand testing on HV and EHV cables obtained globally with withstand levels and durations as in accordance with IEC standards and PD monitoring in accordance with IEEE & CIGRE recommendations.

The paper discusses influence of level and frequency of the applied withstand voltage for successfully identifying typical life limiting laying or installation related defects in newly laid HV & EHV Cable systems both in terms of dielectric break-down and also in terms of detection of partial discharge sources from same defects. The paper further provides statistical summary of tests performed on more than 10,000km of HV and EHV cable systems including failure rates and non-pass PD rates of accessories distinguishing between HV and EHV cable systems. The paper also provides several case studies of PD detected in cable accessories during AC HiPot commissioning testing. The paper also discusses the influence of PD sensitivity on the results obtained especially considering that different types of PD sensors and measurement instruments have been used.