Abstract
Detecting, measuring, and classifying partial discharges (PDs) are important tasks for assessing the condition of insulation systems used in different electrical equipment. Owing to the implementation of the phase-resolved PD (PRPD) as a sequence input, an existing method that processes sequential data, e.g., the recurrent neural network, using a long short-term memory (LSTM) has been applied for fault classification. However, the model performance is not further improved because of the lack of supporting parallel computation and the inability to recognize the relevance of all inputs. To overcome these two drawbacks, we propose a novel deep-learning model in this study based on a self-attention mechanism to classify the PD patterns in a gas-insulated switchgear (GIS). The proposed model uses a self-attention block that offers the advantages of simultaneous computation and selective focusing on parts of the PRPD signals and a classification block to finally classify faults in the GIS. Moreover, the combination of LSTM and self-attention is considered for comparison purposes. The experimental results show that the proposed method achieves performance superiority compared with the previous neural networks, whereas the model complexity is significantly reduced.
Highlights
The popularity of power systems is rapidly increasing as the power demand increases, and the reliability of a power grid is important for a stable power-system operation
To overcome the aforementioned drawbacks, we propose new classification methods to classify faults in a gas-insulated switchgear (GIS) using phase-resolved PD (PRPD), namely, self-attention-based neural network for PRPDs (SANPD) and long short-term memory (LSTM) SANPD (LSANPD) methods
Self-attention offers the advantages of classification accuracy and computational efficiency compared with deep neural networks (DNNs), convolutional neural networks (CNNs), and Recurrent neural networks (RNNs) [41,42,46] because it can capture the relevance among the phases of the PRPDs by considering their entire interaction sequence input regardless of distance [44]
Summary
The popularity of power systems is rapidly increasing as the power demand increases, and the reliability of a power grid is important for a stable power-system operation. Self-attention offers the advantages of classification accuracy and computational efficiency compared with DNNs, CNNs, and RNNs [41,42,46] because it can capture the relevance among the phases of the PRPDs by considering their entire interaction sequence input regardless of distance [44]. In the LSTM self-attention model, the self-attention mechanism assists the LSTM to simultaneously compute and focus on the important information from the data inputs, which improve the classification accuracy of the PRPD classification relative to that of the LSTM RNN [46]. The experimental results reveal that our models outperform the previous RNN model [46] in terms of the PRPD classification accuracy with a lower complexity because the self-attention mechanism recognizes the different relevance of the information among the inputs and takes advantage of simultaneous computation [45].
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