Condition Monitoring and Fault Diagnosis of Power Transformer On-Load Tap Changers

On-Load Tap Changers (OLTCs) are critical components in power transformers that enable voltage regulation under load without interrupting service. Their frequent operation, however, induces mechanical stress, contact wear and oil degradation, which can compromise transformer reliability. Condition monitoring and fault diagnosis (CMFD) aim to detect early signs of deterioration—such as mechanical misalignment, contact pitting or dielectric contamination—while the transformer remains in service, thereby avoiding costly outages. Contemporary CMFD techniques harness non-invasive measurements of vibration, acoustic and electrical signals, complemented by advanced signal-processing and machine-learning algorithms. Feature extraction methods isolate characteristic patterns in time and frequency domains, including peak amplitudes, spectral harmonics and envelope dynamics. Classification frameworks—from clustering and support vector machines to deep convolutional neural networks—translate these features into diagnostic indicators. The ongoing integration of renewable energy sources and digital substation technologies underscores the global importance of robust OLTC health assessment, offering utilities improved asset management, reduced maintenance costs and enhanced grid resilience.

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Condition Monitoring and Fault Diagnosis of Power Transformer On-Load Tap Changers publication trend

The graph below shows the total number of articles in condition monitoring and fault diagnosis of power transformer on-load tap changers across all publications each year (not limited to Nature Index journals).

Technical terms

On-Load Tap Changer (OLTC): A mechanical device in a transformer that changes voltage taps under load to regulate output voltage without interruption.

Condition Monitoring (CM): Continuous or periodic surveillance of equipment parameters to assess health and predict maintenance needs.

Fault Diagnosis (FD): The process of identifying and locating defects or abnormal conditions in machinery based on observed data.

Vibro-Acoustic Signals: Mechanical vibrations coupled with acoustic emissions generated by the movement of OLTC components during switching operations.

Acoustic Emission (AE): High-frequency sound waves produced by transient events such as crack propagation or contact impacts within mechanical systems.

Convolutional Neural Network (CNN): A deep-learning architecture that automatically learns spatial hierarchies of features from data, widely used for signal and image classification.

References

1. Transformer OLTC Operation Monitoring Framework Through Acoustic Signal Processing and Convolutional Neural Networks. IEEE Transactions on Instrumentation and Measurement (2025).
2. Detection of On-Load Tap-Changer Contact Wear Using Vibroacoustic Measurements. IEEE Transactions on Power Delivery (2024).
3. Power Transformers OLTC Condition Monitoring Based on Feature Extraction from Vibro-Acoustic Signals: Main Peaks and Euclidean Distance. Sensors (2023).