Mechanical Fault Identification Method for Ⅴ-type On-load Tap Changers Based on Vibration Signal Analysis
The on-load tap changer(OLTC)is a critical component in transformers,which undergoes frequent operations and is prone to mechanical faults,leading to serious safety incidents.The switching principles of the Ⅴ-type OLTC is analyzed,and three common mechanical defects are introduced on a specific Ⅴ-type OLTC.Multi-channel vibration sensors are employed to collect vibration signals during the switching processes.A vibration signal analysis-based method for mechanical fault identifica-tion in Ⅴ-type OLTCs is proposed.Firstly,the CCEMDAN algorithm is used to decompose the vibration signals.A feature ma-trix is constructed based on the correlation coefficient method,and singular value decomposition(SVD)is utilized for feature extraction.Secondly,optimal measurement points for vibration signals are selected through principal component analysis and K-means clustering.Finally,mechanical fault classification is achieved using the XGBoost ensemble learning algorithm.Experimen-tal results demonstrate that the vibration signals measured on the front side of the OLTC enclosure better reflect its internal mechanical state.The proposed method achieves an identification accuracy of 98％for the three types of mechanical faults,sur-passing ensemble learning algorithms such as random forests and GBDT.
on-load tap changermechanical faultvibration signalfeature extractionensemble learning
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