Transformer Fault Condition Prognosis Using Vibration Signals Over Cloud Environment

Abstract

On-line monitoring and diagnosis of transformers have been investigated and discussed significantly in the last few decades. Vibration method is considered as one of the non-destructive and economical methods to explore transformer operating condition and evaluate transformer mechanical integrity and performance. However, transformer vibration and its evaluation criteria in transformer faulty condition are quite challenging and are not yet agreed upon. At the same time, with the advent of IoT facilities and services, it is expected that classical diagnosis techniques will be replaced with more powerful data-driven prognosis methods that can be used efficiently and effectively in smart monitoring. In this paper, we first discuss in detail an analytical approach to the transformer vibration modeling. Nevertheless, precise interpretation of transformer vibration signal through analytical models becomes unrealistic as higher harmonics are mixed with fundamental harmonics in vibration spectra. Therefore, as the next step, we aim to support the Industry 4.0 concept by utilizing the state-of-the-art machine learning and signal processing techniques to develop prognosis models of transformer operating condition based on vibration signals. Transformer turn-to-turn insulation deterioration and short circuit analysis as one the most important concerns in transformer operation is practically emulated and examined. Along with transformer short-circuit study, transformer over and under excitations are also studied and evaluated. Our constructed predictive models are able to detect transformer short-circuit fault in early stages using vibration signals before transformer catastrophic failure. Real-time information is transferred to the cloud system and results become accessible over any portable device.