Multi-modal sensor-based system gives assets managers a clear picture of the health of transformer. By integrating multiple types of sensors with effective data fusion algorithms, it enables a more comprehensive evaluation of transformer health. Compared to single-sensor approaches, this method offers improved fault detection, health analysis, and operational reliability. Experimental results validate its accuracy, while case studies and in-field measurements demonstrate its effectiveness in supporting transformer asset management. The integration of AI and machine learning (ML) with expert knowledge further strengthens data-driven decision-making, offering improved predictive maintenance and smarter transformer fleet management.
Enhancing transformer reliability with multi-modal sensor-based monitoring
This article presents a multi-modal sensor-based system for condition monitoring power transformers.
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