Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine.

Haikun Shang,Feng Li,Yingjie Wu

doi:10.3390/e21010081

Haikun Shang, Feng Li + Show 1 more

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Partial discharge (PD) fault analysis is an important tool for insulation condition diagnosis of electrical equipment. In order to conquer the limitations of traditional PD fault diagnosis, a novel feature extraction approach based on variational mode decomposition (VMD) and multi-scale dispersion entropy (MDE) is proposed. Besides, a hypersphere multiclass support vector machine (HMSVM) is used for PD pattern recognition with extracted PD features. Firstly, the original PD signal is decomposed with VMD to obtain intrinsic mode functions (IMFs). Secondly proper IMFs are selected according to central frequency observation and MDE values in each IMF are calculated. And then principal component analysis (PCA) is introduced to extract effective principle components in MDE. Finally, the extracted principle factors are used as PD features and sent to HMSVM classifier. Experiment results demonstrate that, PD feature extraction method based on VMD-MDE can extract effective characteristic parameters that representing dominant PD features. Recognition results verify the effectiveness and superiority of the proposed PD fault diagnosis method.

Highlights

Partial discharge (PD) is an important symptom of insulation degradation for electrical equipment.PD fault diagnosis plays an irreplaceable role in the evaluation of insulation condition [1]
variational mode decomposition (VMD) is employed for PD signal decomposition to extract effective intrinsic mode functions (IMFs) from PD signals
A novel PD fault diagnosis method is proposed. This method combines PD feature extraction based on VMD-multi-scale dispersion entropy (MDE) and PD pattern recognition based on hypersphere multiclass support vector machine (HMSVM)

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Introduction

Partial discharge (PD) is an important symptom of insulation degradation for electrical equipment. PD fault diagnosis plays an irreplaceable role in the evaluation of insulation condition [1]. PD feature extraction is an important step in insulation fault diagnosis. The common methods include statistical atlas (SA) [2], wave analysis (WA) [3] and wavelet transform (WT) [4]. SA has the limitations of high request of sampling rate, large data size and slow speed of data processing which are not suitable for on-line monitoring. It is difficult to extract PD phase information during statistical atlas construction. WT has some inherent limitations such as the difficulty of selection of the wavelet basis, wavelet thresholds, decomposition levels, and so on [5]

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