Transformer Health Monitoring Using Dissolved Gas Analysis A Technical Brief

• Published in: International journal of prognostics and health management Vol. 13; no. 2
• Main Authors: Walker, Cody, Al Rashdan, Ahmad, Agarwal, Vivek
• Format: Journal Article
• Language: English
• Published: The Prognostics and Health Management Society 12.07.2022
• Subjects: chendong model; diagnostic; dissolved gas analysis; health monitoring; prognostic; transformer
• ISSN: 2153-2648; 2153-2648
• DOI: 10.36001/ijphm.2022.v13i2.3141

As integral components of any power plant, transformers sup-ply the generated electricity to the grid. However, the trans-former’s cellulose-based paper insulation and the mineral oilin which it is immersed break down over time under stan-dard operating conditions—or more rapidly due to potentialfaults within the system. This technical brief exhibits a col-lection of diagnostic and prognostic techniques that utilitiescan adopted in lieu of labor-intense periodic preventive main-tenance routines. Furthermore, prognostic models have beenincorporated using the latest version of the Institute of Elec-trical and Electronics Engineers (IEEE) standard (IEEE StdC57.104TM-2019) for dissolved gas analysis (DGA), thusexpanding it to include estimation of the time to maintenance.Overall, four different methodologies are explained, each ofwhich aid in determining a transformer’s state of health. Thesemethodologies include the Chendong model, the IEEE C57.91-2011 thermal life consumption model, a diagnostic model forDGA, and a prognostic model for DGA that uses an autore-gressive integrated moving average (ARIMA) model. An ad-ditional improvement for estimating missing system parame-ters from monitoring data (i.e., a tool for parameter estimationutilizing Powell’s method) is presented, enabling the IEEEthermal life consumption model to benefit not only the col-laborating power plant, but also the power industry at large.