# Acoustic Emission Based Fault Detection of Substation Power Transformer  
[PERSON]  
1 School of Information Science and Engineering, Shandong University, Qingdao 266237, China;  
1 [EMAIL_ADDRESS] (X.M. ): [EMAIL_ADDRESS] (H.X.) [PERSON]  
2 College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;  
2 [EMAIL_ADDRESS]  
[PERSON]  
2 College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China;  
2 [EMAIL_ADDRESS]  
[PERSON]  
1 School of Information Science and Engineering, Shandong University, Qingdao 266237, China;  
1 [EMAIL_ADDRESS] (X.M. ): [EMAIL_ADDRESS] (H.X.) Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/10/](https://www.mdpi.com/journals/pdf/10/)]([https://www.mdpi.com/journals/pdf/10/](https://www.mdpi.com/journals/pdf/10/))  
as the features of different running states of the equipment. Experienced engineers can discriminate the operational status of the transformer according to the AE signals. This is mainly based on the Human ear perception for spectral information. Especially in the power system, it has been relying on manual detection for a long time, which is low efficiency, non-real-time, and lack of security. Early fault detection of power transformer often involves routine inspections, preventive testing, and insulation oil test, which are mainly based on the traditional human participation, and this working mode for inspections has played a crucial role for safe and stable operation of substation. With the continuous expansion of the scale of the power system, the complexity of equipment, and the application of high technology, this mode cannot keep up with the need for inspection. At this point, automatic fault detection by acoustic signals utilizing its safety and convenience has been considered to meet the demand of the scale of a constantly complex system. Currently machine learning methods are more and more used in transformer fault identification. Most of the research and experiments are carried out in many cases. Due to the rapid development of speech recognition and the similarity between other kinds of AE signals and speech, many ideas of AE based event detection [13; 14] and device running monitoring [15] come from automatic speech recognition (ASR) [16], and the input feature is similar with speech (such as short-time power spectrum, short-time zero-crossing rate, short-time autocorrelation function, etc.). These algorithms used to distinguish events are relatively traditional machine learning algorithms such as k-nearest neighbor and decision tree. In recent years, with the significant improvement of computer computing speed and storage, machine learning under big data has become a research hotspot. As a branch of machine learning, Deep learning [17] imitates the human brain to model patterns in data as complex multi-layer networks. At present, deep learning has also been widely applied in AE fault-based detection [18; 19]. Although the rapid developing of deep learning has boomed, the traditional methods still retain some advantages in some aspects. While deep learning needs the support of large amount of data, the traditional methods are more suitable to small data process. In many cases, the data of different running status are severe imbalance. As a result, the trained model is easy to be trapped overfitting. In addition, the issue of efficiency and resource saving is also difficulty posted in front of system designers. Some of the traditional methods need not consume a lot of training resources, so that they can easily realize incremental improvement of the parameters in the models. Others may have good generalization suitable for small data training. Different classifier has different character, how to select a suitable classifier is also an important issue for the designer.