Rogowski coil (RC) is a low-cost, air-cored, and flexible induction sensor for nonintrusive condition monitoring and thus can be used in a variety of applications. In this paper, a lumped parameter model of RC is presented and an experiment-based methodology is developed to determine its parameters. The performance of the RC is analyzed for detection and measurement of high-frequency (pulsed) signals such as partial discharge (PD) current pulses. A simple and efficient technique of numerical integration is adopted to avoid the conventional type of expensive and complex design analogue integrators. RC is modeled and simulated in the alternative transient program-electromagnetic transient program environment. The designed coil is tested to measure PDs in the laboratory. Simulated and experimental performance of RC is compared with a high-frequency current transformer. This comparison shows a good match and, hence, validates the design of RC for PD applications.

Electrical insulation is an integral part of power components. The aging of electrical insulation is an undeniable fact that limits the operational lifetime of power components. Apart from regular aging, abnormal stresses and the development of defects are real threats because of their contribution in accelerating the aging rate and thereby leading to a premature failure of the power components. Over the decades, various studies have been carried out to understand the aging behavior of electrical insulation mainly considering electrical and thermal stresses. Similarly, a number of mathematical (aging) models have been developed based on the theoretical and experimental investigations and evidences. However, a dependable formulation of the models that can provide more practical estimation of the insulation degradation profile has not been achieved yet. This paper presents a comprehensive review of the aging models considering single and multistress conditions. Further, the paper discusses possible challenges and barricades averting the conventional models to achieve a suitable accuracy. Finally, suggestions are provided that can be considered to improve the modeling approaches and their performance.

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.