Abstract
In order to improve the recognition accuracy of partial discharge (PD) by making full use of the time-frequency characteristics of PD signals and employing deep learning theory, a kind of PD pattern recognition method based on variational mode decomposition (VMD)-Choi-Williams distribution (CWD) spectrum and optimized convolutional neural network (CNN) with cross-layer feature fusion is proposed in this paper. Firstly, a PD signal is decomposed into several components by VMD algorithm, and the CWD analysis of the obtained components is carried out to obtain the VMD-CWD time-frequency spectrum. Secondly, the cross-layer feature fusion and optimization CNN (CFFO-CNN) is constructed by introducing cross-layer connection and optimization algorithm. Thirdly, the VMD-CWD is regarded as the input vector to train CFFO-CNN to learn and extract the intrinsic features of the spectrum. Finally, the trained network is used to recognize the PD types of the testing samples. The proposed method is compared with traditional recognition methods such as BP neural network (BPNN) and support vector machine (SVM), as well as some commonly used deep learning algorithms. The experimental results indicate that the recognition performance of the proposed method is significantly better than that of existing recognition methods with accuracy up to 99.5%. It is proved that CFFO-CNN has superior feature extraction ability, which can extract the internal features of the VMD-CWD spectrum independently with higher recognition accuracy and wider application prospect.
Highlights
Power transformers are the most crucial equipment in the power grid
ACQUISITION OF variational mode decompositon (VMD)-Choi-Williams distribution (CWD) SPECTRUM IMAGE As mentioned above, each partial discharge (PD) sample was decomposed by VMD, and CWD analysis was carried out on the band-limited intrinsic mode functions (BLIMFs) obtained by decomposition to form the corresponding VMDCWD spectrum diagram
A PD signal is composed of multiple components with different center frequencies. It can be clearly seen from the VMD-CWD spectrum that the discharge time and the main frequency components of the four types of PD signals are observably different, which has practical physical significance
Summary
Power transformers are the most crucial equipment in the power grid. Its insulation condition is directly related to the safe operation of the whole power system. In the production, transportation, installation and long-term operation of transformers, various insulation defects will inevitably appear. Partial discharge (PD) is the main reason for the final breakdown of insulation of transformers, and it is an important manifestation of the internal insulation degradation [1]. Due to the difference of insulation degradation mechanism among different discharge types, the degree of damage to the equipment is distinct. The correct identification of the detected PD type
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