Neural Network-Based Transformer Fault Diagnosis System

This system represents a neural network-based transformer fault diagnosis solution developed using MATLAB's graphical user interface design technology. The complete implementation is written in MATLAB, including programmed algorithms for the three-ratio method and non-coding method that handle fault pattern recognition and analysis. Key features include automated fault diagnosis and real-time monitoring capabilities, which accurately identify transformer fault types and provide corresponding solutions. The implementation likely utilizes MATLAB's Neural Network Toolbox for creating and training the diagnostic model, while the GUI components employ GUIDE or App Designer for user interaction. Through this system, users can perform transformer fault diagnosis and maintenance more efficiently, improving work productivity while reducing failure risks. The integration of multiple diagnostic methods ensures robust fault detection, with the neural network component potentially using backpropagation algorithms for pattern learning from historical fault data.