Conference Agenda

Transformers and instrument transformers are key equipment in the electrical grid. The condition of the insulation mostly decides its life and crucial for efficient energy transmission. Over time, the insulation inside the transformer may degrade due to various types of stresses such as thermal, electrical, mechanical and environmental that will reduce the transformer’s service life. Various monitoring and diagnostic techniques are available amongst which dielectric frequency response is an advanced insulation diagnostic technique based on the measurement of dielectric losses over a broad range of frequencies. Dielectric frequency response (DFR) provides comprehensive view on the condition of insulation having oil, paper and pressboard. This paper presents comprehensive studies to understand nature of dielectric frequency response. The different case studies include the impact of overhauling on dielectric frequency response (DFR) of a transformer and resin impregnated paper (RIP) bushing have been reported. The reported analysis is useful in insulation diagnostics to the practicing engineers to diagnose the abnormalities.

Dissolved Gas Analysis (DGA) has long been a cornerstone of transformer diagnostics, providing invaluable insights into the operational health and fault conditions of power transformers. However, the increasing complexity of transformer designs, coupled with advancements in insulating materials and diagnostic technologies, necessitates a reevaluation of traditional DGA methods. This paper presents a comprehensive framework that integrates established chemical principles, modern gas extraction techniques, and artificial intelligence (AI) to enhance the accuracy and reliability of transformer diagnostics.

The paper delves into the evolution of DGA techniques, emphasizing the transition from traditional partial vacuum extraction to Headspace methods paired with gas chromatography. While these advancements increase efficiency, they also introduce calibration challenges that can impact measurement accuracy. To address these issues, the study advocates for refined calibration protocols, including multi-level gas-in-oil mixtures (GIOM), to align diagnostic practices with contemporary transformer requirements.

A key focus of the paper is the refinement of the Key Gas Method, a well-established diagnostic approach that correlates specific gas patterns with fault types. By incorporating modern analytical chemistry principles, empirical patterns, and advanced diagnostic tools like Duval's Triangles and Pentagons, the Key Gas Method is extended to address multi-fault scenarios and accommodate the diverse gas behaviors associated with new insulating materials.

Moreover, the paper explores the potential of AI and machine learning in DGA diagnostics. By training AI algorithms on extensive datasets, diagnostic accuracy can be significantly improved, particularly in complex or ambiguous cases. AI also enables real-time analysis, facilitating proactive maintenance and reducing the risk of catastrophic transformer failures.

The proposed framework combines predictive, explanatory, and intuitive diagnostic methods to provide a comprehensive approach to transformer health monitoring. Predictive methods leverage historical fault data, explanatory methods focus on the thermodynamic and chemical mechanisms underlying gas formation, and intuitive methods incorporate expert insights. This multi-faceted approach ensures a balance between diagnostic precision and practical applicability.

Through case studies and comparative analyses, the paper demonstrates the efficacy of this integrated framework in enhancing fault detection, reducing diagnostic uncertainties, and optimizing maintenance strategies. By aligning traditional DGA methods with modern technologies and AI, this approach offers a robust and adaptable solution for ensuring the reliability of transformers in increasingly complex power systems

The ability to anticipate voltage-induced breakdown (BD) of electrical insulation is of critical importance to the power industry to maintain uninterrupted electrical service and ensure grid reliability. Gaseous voids trapped within the electrical insulation of power devices are an often unavoidable artifact of the manufacturing process. The compromising effect of voids on dielectric performance of motors and generators is difficult to predict as these effects are dependent on highly localized field geometries. Reported here are initial steps toward the development of a FEA simulation model which will help inform machine insulation design by predicting breakdown thresholds under device-specific configurations and conditions. Preliminary results consider a simplified test system of two opposing electrodes sandwiching an air gap. A finite element simulation was developed using COMSOL Multiphysics utilizing a newly released software add-on, the COMSOL Electric Discharge Module, to model the electric field generated within this simplified system under an applied DC terminal load. Electric field characteristics and associated gas breakdown thresholds were determined as a function of several geometric and environmental parameters. Modeled system geometry was specifically designed for rapid evaluation and experimental validation in the lab. Experimental air breakdown data was generated and found to be in agreement with simulated data.