Classification of OLTC defects based on AE signals measured by two different transducers

Wotzka, Daria; Cichoń, A.; Manowski, Piotr

doi:10.1109/epe.2019.8778140

Cited by 2 publications

(1 citation statement)

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“…Several data feature extraction methods have been presented in the literature. The usage of Hilbert transform was shown in [11]. An attempt to analyze OLTC AE signals in the time-frequency domain is presented in [12].…”

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confidence: 99%

OLTC Fault detection Based on Acoustic Emission and Supported by Machine Learning

Cichoń,

Włodarz

2023

Energies

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Power transformers are an essential part of the power grid. They have a relatively low rate of failure, but removing the consequences is costly when it occurs. One of the elements of power transformers that are often the reason for shutting down the unit is the on-load tap changer (OLTC). Many methods have been developed to assess the technical condition of OLTCs. However, they require the transformer to be taken out of service for the duration of the diagnostics, or they do not enable precise diagnostics. Acoustic emission (AE) signals are widely used in industrial diagnostics. The generated signals are difficult to interpret for complex systems, so artificial intelligence tools are becoming more widely used to simplify the diagnostic process. This article presents the results of research on the possibility of creating an online OLTC diagnostics method based on AE signals. An extensive measurement database containing many frequently occurring OLTC defects was created for this research. A method of feature extraction from AE signals based on wavelet decomposition was developed. Several machine learning models were created to select the most effective one for classifying OLTC defects. The presented method achieved 96% efficiency in OLTC defect classification.

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confidence: 99%