International Journal of Research in Advanced Electronics Engineering
2026, Vol. 7, Issue 1, Part A
Implementation of predictive maintenance alert system for distribution transformers using thermal imaging
Author(s): Müller and Stefan K
Abstract: Distribution transformers represent critical assets within electrical networks, with unexpected failures causing significant economic losses through equipment damage, service interruption penalties, and emergency repair costs. Traditional time-based maintenance strategies often prove inefficient, either replacing components with remaining useful life or failing to detect developing faults before catastrophic failure occurs. This research presents the implementation and field validation of a predictive maintenance alert system utilizing automated thermal imaging to detect incipient transformer faults through temperature anomaly identification. The system employs FLIR A615 thermal cameras with 640×480 pixel resolution mounted at distribution substations, capturing scheduled thermal images of transformer assemblies including bushings, tank surfaces, and radiator components. Edge computing platforms running OpenCV-based image processing algorithms extract temperature features from defined regions of interest, comparing measured values against adaptive thresholds accounting for load conditions and ambient temperature. Anomalies triggering warning, critical, or emergency alerts propagate through automated notification channels while logging to time-series databases supporting trend analysis and predictive modeling. Field deployment across 45 distribution transformers in the Bavarian rural network over twelve months detected 150 thermal anomalies, with fault type distribution comprising loose connections (32%), bushing degradation (24%), cooling system issues (18%), oil quality problems (12%), overloading (8%), and winding hot spots (4%). Severity classification identified 58% warning-level, 31% critical-level, and 11% emergency-level conditions requiring immediate intervention. The system demonstrated 93.3% sensitivity in detecting confirmed faults with 3.2% false positive rate, achieving mean detection lead time of 18 days before potential failure would have occurred. The predictive maintenance approach achieved 86% reduction in unplanned transformer outages compared to the previous year operating under time-based maintenance, with estimated annual cost savings of €124,500 through avoided emergency repairs and prevented equipment damage. System improvement of distribution reliability metrics included 12.3 minute reduction in System Average Interruption Duration Index contribution from transformer failures. The research demonstrates that automated thermal imaging provides cost-effective continuous condition monitoring enabling transition from reactive to predictive transformer maintenance strategies, with documented reliability improvements and economic benefits supporting broader deployment across distribution network assets.
DOI: 10.22271/27084558.2026.v7.i1a.78
Pages: 58-63 | Views: 35 | Downloads: 16
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How to cite this article:
Müller, Stefan K. Implementation of predictive maintenance alert system for distribution transformers using thermal imaging. Int J Res Adv Electron Eng 2026;7(1):58-63. DOI: 10.22271/27084558.2026.v7.i1a.78
