Vibration Analysis and Condition Monitoring of Power Transformers

Power transformers underpin modern electrical grids, and their uninterrupted operation is critical to energy security and economic stability. Vibration analysis exploits the mechanical oscillations generated by electromagnetic forces, core magnetostriction and winding interactions to assess transformer health without interrupting service. Condition monitoring systems employ an array of sensors—typically piezoelectric accelerometers or non-contact acoustic devices—to capture vibration signatures under varied load and environmental conditions. Signal-processing techniques such as fast Fourier transform, continuous wavelet transform and time-frequency decomposition reveal characteristic fault indicators associated with short-circuited turns, winding deformation, core loosening and dc bias. Mathematical modelling, including finite-element simulation of magnetostrictive phenomena, complements experimental measurements and aids interpretation of complex vibration patterns. In recent years, machine-learning and deep-learning frameworks have become integral to feature extraction and classification, allowing automatic identification of emerging faults from vast data streams. By linking vibration signatures to specific mechanical or electromagnetic anomalies, these approaches support predictive maintenance strategies that mitigate the risk of catastrophic failures, extend asset life and reduce operational costs. The integration of advanced analytics with real-time monitoring platforms promises a more resilient and efficient power infrastructure globally.

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Vibration Analysis and Condition Monitoring of Power Transformers publication trend

The graph below shows the total number of articles in vibration analysis and condition monitoring of power transformers across all publications each year (not limited to Nature Index journals).

Technical terms

Vibro-acoustic signal: Mechanical vibration converted into acoustic waves for diagnostic analysis of transformer internal dynamics.

Continuous wavelet transform (CWT): A time-frequency decomposition method that maps a signal into scale and translation parameters for feature extraction.

Convolutional neural network (CNN): A deep-learning architecture that processes image-like inputs to learn spatial hierarchies and patterns automatically.

Time-shift multiscale bubble entropy: A measure of signal complexity across multiple scales, designed to capture nonlinear dynamics in vibration data.

Support vector machine (SVM): A supervised learning algorithm that constructs an optimal hyperplane to separate signal features into predefined fault classes.

Field-programmable gate array (FPGA): A reconfigurable hardware platform for implementing signal-processing and classification algorithms in real time.

References

1. Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey. IEEE Access (2019).
2. Modelling of magnetostrictive vibration and acoustics in converter transformer. IET Electric Power Applications (2021).
3. Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals. Sensors (2023).
4. Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA. Sensors (2021).
5. Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network. Entropy (2022).