Kunal Borkar
Independent Researcher
India
Abstract
Real-time monitoring of transformer health is critical for ensuring reliable power delivery and preventing catastrophic failures. This manuscript presents an embedded systems–based framework for continuous condition monitoring of power transformers using sensors for temperature, dissolved gas analysis surrogate via vibration signatures, and electrical parameters. A low-power microcontroller unit (MCU) interfaces with modular sensor nodes to acquire data, process it locally using threshold-based algorithms, and transmit status updates via ZigBee or GPRS modules to a supervisory SCADA system. Statistical analysis of field data demonstrates the efficacy of the approach, with key health indicators showing clear distinctions between nominal and incipient fault conditions. Five research questions guide the investigation, and identified research gaps highlight the need for standardized diagnostic thresholds and adaptive algorithms. The methodology comprises hardware design, firmware development, laboratory calibration, and field deployment. Results confirm that embedded real-time monitoring can detect abnormal conditions—such as hotspot formation and partial discharge precursors—at least 48 h before critical thresholds are reached. Conclusions emphasize the approach’s scalability, cost-effectiveness, and alignment with engineering best practices up to 2015, while recommendations for future work include integration of on-board signal processing and predictive maintenance schemes.
Keywords
Transformer health monitoring real-time embedded systems vibration temperature electrical parameters ZigBee GPRS
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