A Practical Transformer Lifespan Prediction by Integrating DGA, Insulation Parameters, and Operating Temperature Analysis Using Fuzzy Logic

Abstract

Power transformers are critical aspects in electrical power systems, and their unexpected failures can lead to significant disruptions and costs. In this article, a practical method for transformer lifespan prediction is developed by comprehensively integrating dissolve gas analysis (DGA), insulation parameters, and operating temperature, using readily available data collected during routine maintenance. The insulation parameters considered in this work include furan, moisture, and interfacial tension (IFT) as the main and noninvasive inputs. The degree of polymerization (DP) is included as an optional, high-fidelity parameter to demonstrate the model’s capacity to incorporate the laboratory data when available, without affecting its low-cost, field-applicable nature. DGA indicates transformer health by detecting the presence of fault gases dissolved, which signify the breakdown of insulation materials. The presence of low DP, increased furan and moisture content and reduced IFT are indicators of transformer insulation degradation, leading to a shortened lifespan. Furthermore, elevated operating temperatures accelerate the insulation degradation. The uncertainties inherent in transformer aging, such as insulation parameters degradation and oil condition, are addressed using fuzzy logic by representing imprecise data using a membership function (MF) and incorporating expert knowledge and assessment through “if–then” rules, resulting in a robust and practical approach. The proposed approach is tested and validated with the real-world industry data from the existing literature. Validation using real-world industry data shows that the proposed integrated approach reduces the average error in lifespan prediction by 2.41% for a diverse set of in-service transformers, confirming its superior accuracy and practical value.