Analysis shows improved diagnostic accuracy in power transformers with artificial neural networks, indicating better maintenance schedules and cost efficiency.
The application of artificial neural networks (ANNs) is revolutionizing the monitoring and control of power transformer health, shifting the paradigm from scheduled preventive maintenance to intelligent condition-based service. This paper presents a comprehensive study of ANN architectures and their implementation in transformer diagnostics. Using multi-source data such as dissolved gas analysis (DGA), vibration, SFRA, and partial discharge measurements, the study demonstrates how neural networks enable early fault detection, residual life prediction, and optimization of maintenance schedules. The results indicate significant improvement in diagnostic accuracy and cost efficiency compared to traditional threshold-based systems.
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Abdullabekova et al. (2025) studied this question.
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