Abstract
In view of the fact that the statistical feature quantity of traditional partial discharge (PD) pattern recognition relies on expert experience and lacks certain generalization, this paper develops PD pattern recognition based on the convolutional neural network (cnn) and long-term short-term memory network (lstm). Firstly, we constructed the cnn-lstm PD pattern recognition model, which combines the advantages of cnn in mining local spatial information of the PD spectrum and the advantages of lstm in mining the PD spectrum time series feature information. Then, the transformer PD UHF (Ultra High Frequency) experiment was carried out. The performance of the constructed cnn-lstm pattern recognition network was tested by using different types of typical PD spectrums. Experimental results show that: (1) for the floating potential defects, the recognition rates of cnn-lstm and cnn are both 100%; (2) cnn-lstm has better recognition ability than cnn for metal protrusion defects, oil paper void defects, and surface discharge defects; and (3) cnn-lstm has better overall recognition accuracy than cnn and lstm.
Highlights
As the main core component of the power grid system, the transformer is directly connected to the safety and reliability of the power system
The main methods for partial discharge (PD) pattern recognition are distance-based pattern classification [3,4], fisher classification [5,6], principal component classification [7,8], neural network classification [9], fuzzy cluster classification [10], Bayesian classification [11,12], support vector machine classification [13,14,15], etc., in which neural network classification and support vector machine classification are the most mature and widely used classification algorithms. These algorithms are based on the statistical feature quantity, and the extraction of feature quantity needs to rely on the expert experience, which leads to the lack of certain generalization of these pattern recognition algorithms [16,17,18]
The results showed that the overall recognition accuracy of cnn increased by 3.71% and applied lstm to the identification of different PD defect types in insulating oil
Summary
As the main core component of the power grid system, the transformer is directly connected to the safety and reliability of the power system. Peng used cnn for experimental results show that the recognition rate of cnn is better than the traditional identification method [18]. The results that the overall recognition accuracy of cnn (phase resolved pulse sequence) map and the whole-period time-domain waveform map were increased by 3.71% and 4.06%, respectively, compared with SVM and BP neural network [25]. The results showed that the overall recognition accuracy of cnn increased by 3.71% and applied lstm to the identification of different PD defect types in insulating oil. If the cnn and lstm can be combined and the local spatial recognition accuracy of series the PD type withofthe will be effectively improved. PD map are characteristics extracted at the same time, the recognition accuracy of the PD type with the undulating characteristics will be effectively improved
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