Abstract
Power transformer is pivotal equipment in a power system, which is responsible for energy transmission and transformation, and its operating condition is related to the safe operation of the power system. In the 21st century, computer science has entered a stage of rapid development, advanced network structures and algorithms have been applied to the field of artificial intelligence, and pattern recognition theory and technology have also made great progress. In the past, the identification of partial discharge type mainly relied on the experience of operation and maintenance personnel, and manual analysis and judgment were made based on partial discharge mapping, which was not very accurate. The application of the computer pattern recognition method in the field of partial discharge type identification has changed the status quo of manual identification, and this method has substantially improved the accuracy and efficiency of identification. Pattern recognition using computer technology has been applied to the field of partial discharge analysis. Compared with manual recognition, its recognition results are accurate, recognition speed is fast, and it has great potential for development. This paper proposes an artificial neural network-based model for transformer partial discharge pattern recognition, which combines the advantages of artificial neural networks with accurate extraction of local spatial higher-order features and provides a new solution for transformer partial discharge pattern recognition. Extended experiments show that the method proposed in this paper achieves leading performance and has practical application value.
Highlights
China’s energy distribution and demand distribution are extremely uneven, with about 80% of energy resources concentrated in the northwest, while nearly 70% of the power load is concentrated in the economically developed areas of the central and eastern regions, showing the characteristics of “source and load cutoff” [1]. e unbalanced geographical distribution of energy and economy requires that in the process of power transmission, transmission line losses should be reduced as much as possible, reducing the area of the transmission corridor, while improving the transmission capacity
Feature extraction is a key step in fault diagnosis, and feature extraction mostly relies on personal experience and has some limitations. e main contribution of this paper is to propose a partial discharge pattern recognition method based on a convolutional neural network for the limitations of traditional methods and to input data into the convolutional neural network model. e features are extracted by a convolutional layer to avoid the manual operation of feature extraction and selection and enhance the intelligence of recognition, while the two features of local sensing and weight sharing speed up the training of samples and better adapt to today’s big data era
48 discharge patterns were classified according to the alternating current (AC)-DC voltage ratio, defect type, and severity, and feature extraction and selection were performed on the discharge signals. e selected feature quantities were used as input feature quantities for five classification methods, namely, repetitive clip, fuzzy recognition, Random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN), and the 48 discharge patterns were identified directly and stepwise, respectively, and the results showed that RF had the highest recognition accuracy in both recognition modes. e EMDSVD features were used as input feature quantities for the SVM classifier and RF classifier to identify the seven thermal aging stages of oil-paper insulation, and the results showed that the classification accuracy of RF was higher than that of SVM [19]
Summary
China’s energy distribution and demand distribution are extremely uneven, with about 80% of energy resources concentrated in the northwest, while nearly 70% of the power load is concentrated in the economically developed areas of the central and eastern regions, showing the characteristics of “source and load cutoff” [1]. e unbalanced geographical distribution of energy and economy requires that in the process of power transmission, transmission line losses should be reduced as much as possible, reducing the area of the transmission corridor, while improving the transmission capacity. As one of the most intelligent features and cutting-edge research fields in artificial intelligence, the artificial neural network can effectively mine the implicit information in the data, extract key information, and gain insight into minute features, which is suitable for transformer partial discharge pattern recognition in the context of electric power big data. On the basis of analyzing the local discharge signal of power transformer, two aspects need to be studied, firstly, considering that there are more useless noise interference in the discharge signal if the signal analysis is carried out directly, the effect is very poor, and the noise elimination process of the discharge signal is needed first to ensure the stability of the signal; secondly, considering the limitations of traditional methods, this paper uses deep learning to build a suitable local model to solve the limitations.
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