Solar Hotspot Detection Using VHDL-Simulated Fixed-Point SVM: A Methodology Toward FPGA Realization Solar Hotspot Detection via FPGASVM

Abstract

The early and accurate detection of thermal hotspots in photovoltaic modules is critical to ensure the efficiency, safety, and longevity of solar power systems. This study presents a complete end-to-end methodology for implementing a fixed-point Medium Gaussian Support Vector Machine classifier using Very High-Speed Integrated Circuit - Hardware Description Language, optimized for Field Programmable Logic Array. The approach begins with feature extraction from thermal images, focusing on MPEG-7 descriptors and blue chrominance. The SVM model is trained in MATLAB and converted into a fixed-point Q1.15 format for hardware compatibility. Key parameters, including support vectors, Lagrange multipliers, bias, and kernel scale, are extracted and verified in a custom Python environment to ensure numerical alignment with MATLAB results. The validated model is then implemented in synthesizable VHDL and verified using GHDL and GNU Tool Kit waveform viewer, confirming bit-accurate hardware behavior. Results show classification accuracy exceeding 99.3% with negligible performance loss due to quantization. The design achieves deterministic latency based on FSM structure and parallel feature processing, completing classification within 2702 clock cycles for a 300-support-vector, 222-feature system. Unlike floating-point models, this approach enables low-power, real-time inference on edge platforms such as drones.