Implementation of support vector machine on LVMDP panel with overheating protection system

Abstract

Electricity is a critical requirement in industrial operations, where the continuity and stability of power distribution directly affect safety and productivity. The low voltage main distribution panel (LVMDP) functions as the main node of electrical power distribution; however, conventional LVMDP systems generally lack intelligent protection mechanisms capable of detecting overheating-related fire hazards and initiating preventive action before failure occurs. This study proposes an intelligent monitoring and protection system for LVMDP panels that combines real-time multi-sensor monitoring, support vector machine (SVM)-based hazard classification, and an automatic shutdown mechanism. The main contribution of this work lies in the integration of predictive thermal risk detection with autonomous protective action, enabling the system not only to monitor panel conditions but also to respond immediately to hazardous states before they escalate into fire incidents. SVM was selected because of its strong capability to classify complex and nonlinear patterns from sensor data with high reliability. The developed system continuously evaluates panel conditions and triggers auto-shutdown when an overheating risk is identified, thereby improving preventive protection compared with conventional alarm-based monitoring systems. Experimental results show that the sensor measurements achieved error rates mostly below 5% compared with calibrated instruments, indicating good accuracy. In addition, the SVM model obtained an overall accuracy of 93%, with a macro-average F1-score of 92% and a weighted-average F1-score of 93%. These results demonstrate that the proposed system is effective for early detection and active protection of LVMDP panels against overheating hazards.