Non-Intrusive Diagnostics of Outdoor Ceramic Insulators Using Ultrasonic Signatures and Deep Learning Models

Abstract

Ceramic insulators have been widely used in overhead power lines for over a century. However, in recent years, transmission and distribution networks have been gradually shifting toward polymeric insulators. Despite this transition, many ceramic insulators remain in service, and a significant portion are now approaching or exceeding their intended lifespans. This aging infrastructure poses an increasing risk of sudden failure, thereby compromising network reliability. Insulator failures account for nearly half of maintenance costs in transmission lines [1], prompting a growing demand among utilities for fast, reliable, and cost-effective condition monitoring systems. Defective ceramic insulators that experience internal punctures, broken discs, or cracks will ultimately initiate partial discharge (PD) activities. Additionally, ceramic insulators exposed to high contamination levels are prone to dry band arcing (DBA), which increases the risk of flashover. Both PD and DBA emit electromagnetic, ultraviolet, infrared and/or ultrasonic radiation, serving as critical indicators that can trigger corrective maintenance actions. Employing sensors to detect these early-stage discharge activities is essential for preventing insulator failure and reducing the risk of power outages. Furthermore, insulator strings often exhibit multiple concurrent defects, resulting in various discharge activities—such as corona, PD, or DBA—each characterized by distinct properties. The overlapping nature of these discharge activities poses significant challenges to accurate diagnosis. This thesis introduces a novel, non-contact method for assessing the condition of outdoor ceramic insulators by employing an ultrasonic sensor in conjunction with deep learning techniques to detect and classify insulator defects. Notably, it demonstrates how the strong directionality of ultrasonic sensors can be leveraged to indirectly identify internal punctures by monitoring surface discharges on adjacent discs, overcoming attenuation limitations caused by the porcelain body and metallic caps. The dissertation is structured into three phases. In the first phase, ultrasonic data from defective insulator strings is generated under controlled laboratory conditions. A multi-class classification model is developed and trained to diagnose individual defects; the model’s performance is then tested in both laboratory and field environments to evaluate its robustness and real-world applicability. The second phase extends this approach to the diagnosis of insulators with multiple concurrent defects. Here, a multi-label classification model is developed to identify and categorize overlapping defect signatures within a single insulator. This approach captures multiple defect types simultaneously, thereby enhancing diagnostic accuracy and reflecting the true operational conditions of outdoor insulators, where different kinds of degradation can co-exist. The final phase involves in-depth analysis of ultrasonic signals by leveraging model-learned features to identify distinct temporal characteristics for each defect type. This enables precise defect characterization and further boosts the accuracy and reliability of outdoor insulator condition assessment. Additionally, findings from Shannon entropy analyses corroborate the presence of unique entropy profiles for different defect classes, improving classification performance—though internal puncture and corona classes can exhibit overlapping energy and entropy characteristics. The results highlight the potential for real-time, non-intrusive monitoring, while emphasizing future work on advanced time-frequency analysis and exploring diagnostic methods for polymeric insulators to address broader asset management challenges.