Fault Diagnosis System for Distribution Room Simulation Circuit Based on Random Forest Algorithm

This article presents a fault diagnosis method utilizing Random Forest (RF) for analyzing faults detected within the power distribution room simulation circuit. Employing the Python programming language, the study delves into a grid failure analysis system model centered around the Random Forest algorithm. The experimental results demonstrate that the random forest-based fault diagnosis model exhibits a high level of diagnostic accuracy for circuit faults in distribution rooms, offering practical value and guidance for fault detection and prediction. Additionally, the article details the development of a visual fault diagnosis system leveraging the Unity3D engine, culminating in successful deployment and practical application.