Author(s)

Qiuping Zheng¹, Ting Chen², Haitao Hu², Yingli Wang², Dawei Zhao², Chuntian Chen², Dianchun Zheng²

¹Instrumentation Technology & Economy Institute, Beijing, China.
²Harbin University of Science and Technology, Harbin, China.

Five-electrode configurations were designed to simulate the distribution inhomogeneity of electric field intensities in the air-insulating medium, and the characteristic data waveforms of partial discharge generated by different electrode configurations under the excitation of power frequency AC voltage were carefully collected in this paper. Furthermore, the feature vectors of the corresponding fingerprint, contained in partial discharge data, were extracted by rigorous mathematical algorithms, and the artificial neural network was employed to realize the pattern recognition of partial discharge caused by the inhomogeneity of electric field intensity with different electrode configurations. The results indicate that the J₄ value in the space of 7 feature quantities is 1905.6, and the recognition rate is 100% when the hidden layer neuron of the network is 19. However, the J₅ value of 9 feature quantities is 1589.9, and the purpose of recognition has been achieved when the number of hidden layer neurons of the network is 6. Increasing the number of hidden layer neurons will only waste computing resources. Of course, PD information collection mode, feature quantity selection, optimal feature space composition, network structure and classification algorithm are the key to realizing PD fault intelligence identification.

PD, Fingerprint, Feature Extraction, Pattern Recognition, Class Separability

Zheng, Q. , Chen, T. , Hu, H. , Wang, Y. , Zhao, D. , Chen, C. and Zheng, D. (2022) Research on Intelligent Identification of PD Patterns Based on the Fingerprint Features. Applied Mathematics, 13, 896-916. doi: 10.4236/am.2022.1311057.