Power Transformer Fault Classification Using Industry 4.0-Compliant Machine Learning Under Data Missing Conditions

Abstract

This work presents an innovative approach for fault classification in power transformers, combining advanced wavelet transform with machine learning techniques. The proposed method stands out for its robustness against data missing conditions, which is a critical challenge in fault diagnosis for these systems. The approach employs machine learning algorithms for fault classification, even when data quality and availability are compromised due to transmission failures or possible interference in the connection circuit between the current transformers and the transformer protective relay. Its ability to adapt to data loss makes it highly suitable for real-world industrial applications, aligning with Industry 4.0 principles, particularly in environments where data integrity is essential for real-time analysis and decision-making. The approach’s effectiveness is validated through a comprehensive evaluation of a diverse dataset covering several critical faults. When compared with an existing threshold-based fault classificator, the proposed method demonstrated outperformance in fault classification both in scenarios with all data available and in scenarios with missing data, reaching expressive success rates of 100% and 92.9%, respectively, against 42.5% and 36.9% obtained by the conventional one for a signal-to-noise ratio of 40 dB. The proposed method’s robustness in challenging high-noise and missing-data scenarios reinforces its viability in industrial operations, where measurement infrastructure may be compromised by hardware malfunctions.