Embedded Partial Discharge Classification System Using Transformer Neural Networks on Raspberry Pi
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Abstract
Partial Discharge (PD) represents a critical indicator of insulation deterioration in high-voltage equipment. This paper presents an embedded PD classification system using a Raspberry Pi platform with transformer-based neural network architecture. The system classifies four PD types—corona, surface, internal, and noise—using phase-encoded signal data based on IEC 60270 standards. PRPD patterns are converted to sequential vectors through phase encoding: each discharge pulse is mapped to a discretized phase index with its associated charge value. The Raspberry Pi platform integrates an SSD1306 OLED display (128×64 pixels) and five LED indicators for real-time visual feedback. Experimental results demonstrate that the transformer architecture achieves 94.8% validation accuracy compared to CNN baselines at 88.3%. The system enables portable, low-cost condition monitoring without expensive laboratory equipment, demonstrating the viability of embedded artificial intelligence for industrial diagnostic applications.
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