Abstract
Dissolved gas analysis (DGA) is the most important tool for fault diagnosis in electric power transformers. To improve accuracy of diagnosis, this paper proposed a new model (SDAE-LSTM) to identify the dissolved gases in the insulating oil of power transformers and perform parameter analysis. The performance evaluation is attained by the case studies in terms of recognition accuracy, precision ratio, and recall ratio. Experiment results show that the SDAE-LSTM model performs better than other models under different input conditions. As evidenced from the analyses, the proposed model achieves considerable results of recognition accuracy (95.86%), precision ratio (95.79%), and recall ratio (97.51%). It can be confirmed that the SDAE-LSTM model using the dissolved gas in the power transformer for fault diagnosis and analysis has great research prospect.
Highlights
Power transformers are considered as the core of the electric power systems, and its running state determines whether the power network is controllable or not
Evaluation of Fault Diagnosis Accuracy. e fault diagnosis accuracy is quantified by recognition accuracy (RA), which has been widely used in machine learning models. e criterion means the proportion of samples recognized correctly in the whole samples recognized, which represents the overall performance of the stacking denoising autoencoder (SDAE)-long short-term memory (LSTM) model: RA af × 100%, af + cf where af means the number of samples recognized correctly and cf gives the number of the whole samples recognized
E fault diagnosis accuracy can be evaluated by precision ratio (PR), which gives the information on the percentage of particular samples recognized correctly in the entire same samples. is criterion reflects the performance on recognizing correctly the particular patterns: PR bf1 × 100%, bf where bf means the number of samples, which come from the same pattern. bf1 gives the information about the number of samples recognized correctly in the whole same samples
Summary
Power transformers are considered as the core of the electric power systems, and its running state determines whether the power network is controllable or not. Typical machine learning methods had been implemented for DGA, including artificial neural networks (ANNs), support vector machine (SVM), relevance vector machine (RVM), and fuzzy theory [10, 11] To some extent, these approaches improve the accuracy of fault diagnosis, whereas they are usually some specific weaknesses, too. In order to enhance the ability of model to extract fault data features, an improved SDAE-LSTM transformer fault diagnosis method is proposed. Erefore, SDAE-LSTM transformer fault diagnosis method can effectively enhance the model’s ability to extract fault features and can track the variation of gas concentration in oil with time better, improve the diagnostic accuracy, and provide a strong guarantee for the safe and stable operation of power transformers.
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