LAUSR

To improve adaptability, feature resolution, and identification accuracy when diagnosing mechanical faults in an on-load tap changer (OLTC) of a transformer, in the present research, wavelet packet energy entropy is used to describe the information comprising vibration signal in the switch process of an OLTC, and a fuzzy weighted least squares support vector machine (CSA-fuzzy weighted LSSVM) model based on the cuckoo search algorithm is proposed to identify mechanical fault types. Specifically, according to the different importance of the sample data in different periods, the idea of fuzzy weighting of training samples is proposed. The cuckoo search algorithm is used to optimise regularisation parameters, kernel function width, and weight control factor of CSA-fuzzy weighted LSSVM. Finally, the real experimental platform for typical mechanical faults of an OLTC is established, and the vibration signals of several typical mechanical faults under different degrees of fatigue are obtained. The results show that the new method achieves a higher accuracy rate of fault identification compared with other common methods. It can better deal with small sample and nonlinear prediction problems and shows higher fitting accuracy than CSA-LSSVM, single LSSVM, and radial basis neural network methods and is thus better suited for mechanical fault diagnosis in OLTCs. This paper presents a new intelligent diagnosis scheme for mechanical faults of on-load tap changers, which can achieve noninterruption and nonintrusive detection. The proposed diagnosis method would change the traditional diagnosis method of the on-load tap changer and improves the power supply quality and the detection efficiency under the premise of ensuring the safety of the staff.