# Partial Discharge Inception and Breakdown Voltage Analysis in PCB-Based Electrode Structures
###### Abstract  
This paper investigates partial discharge (PD) characteristics and breakdown behavior in five electrode configurations fabricated from fiberglass circuit boards, including parallel electrode (PE), triangular electrode (TE), right-angled electrode (RE), and two floating electrode (FE) designs with 0.3 and 0.5 mm insulation gaps. The electrodes are tested according to IEC 60270:2000 using a commercial device with eight samples per configuration. Key parameters such as the PD inception voltage (PDIV) and breakdown voltage are measured. The TE configuration exhibited the highest breakdown voltage of 14.9 kV, with a PDIV of 6.13 kV and RPDIV of 8.04 kV, indicating strong dielectric properties. The RE configuration showed a PDIV of 7 kV, RPDIV of 8.5 kV, and a breakdown voltage of 13.3 kV. The FE_0.5 mm sample exhibited surface discharges, whereas the FE_0.3 mm sample experienced breakdown at 18 kV with an average breakdown voltage of 15.3 kV. The results indicate that the electrode geometry and insulation spacing strongly influence PD behavior and breakdown resilience. The phase-resolved partial discharge (PRPD) patterns at PDIV and breakdown provide further understanding of the dielectric stability. These findings offer critical insights into designing insulation systems under electrical stress. partial discharge; dielectric breakdown; partial discharge inception voltage; insulating materials 2025  
28 January 2025 February 20  
warnings of insulation degradation under electrical stress and enabling timely interventions to prevent catastrophic failures. Recent advances in PD diagnostics have increasingly integrated machine-learning (ML) techniques to improve detection accuracy and pattern recognition. ML algorithms, such as support vector machines [16], decision trees [15], and convolutional neural networks (CNNs) [17], have demonstrated success in classifying PD signals and correlating them with specific defect types [13,14]. These techniques leverage the vast datasets generated during PD testing to extract meaningful features, enabling predictive maintenance strategies in high-voltage systems. For instance, [PERSON] et al. [18] applied deep convolutional neural networks to phase-resolved PD patterns and achieved significant improvements in classification accuracy compared to traditional methods. Similarly, [PERSON] et al. [19] utilized random forest classifiers to analyze high-frequency PD signals, demonstrating a superior performance in identifying defect types. Innovations such as reinforcement learning have also been explored for adaptive PD testing in real-time environments, as shown by [PERSON] et al. [11]. Emerging applications of ML in PD diagnostics include real-time monitoring using edge computing devices [20] and advanced feature extraction from high-dimensional datasets using unsuperized learning techniques [21]. While this study focuses on PDIV and breakdown analysis in PCBs, it acknowledges the growing impact of machine learning in advancing PD diagnostics. Future research should explore such methodologies for enhanced defect recognition, particularly in complex environments like PCB-based power electronics or underground cable networks. Despite these advances, challenges remain in understanding the underlying factors that contribute to PD initiation and breakdown, particularly in PCB-based systems. [PERSON] et al. [22] demonstrated that square impulse voltages significantly affect PCG breakdown characteristics under specific temperature and pressure conditions. Electric field simulations highlight areas where breakdown is more likely, and factors such as the temperature, pulse ratio, and distance between circuits significantly influence the breakdown voltage. Scanning electron microscopy (SEM) observations after testing have shown burn marks (carbonization) caused by the temperature and pressure, highlighting the importance of design and material choices in preventing breakdown under high-voltage conditions.