# Acoustic Emission Based Fault Detection of Substation Power Transformer
## 5 Discussion  
An experiment confirmed our hypothesis that effective and quick recognition of transformer running states is possible by machine learning algorithms. Furthermore, the multi-classification experiments demonstrate that sounds with subtle differences can be discriminated by machine learning algorithms. In these experiments, the SVM classifier with 2 DPCA presents good performance, which can be supported by the results shown in Tables 2 and 3. The results show that the selection of kernel function for SVM and \(\upmu\) for PCA is important for acquiring the best performance. Different kernels and different \(\upmu\) not only have different influences on the sensitivity or accuracy of the fault detection, and can also have different effects in the time of recognition. As a general fault detection, PCA is also competent, but 2 DPCA has a stronger ability to distinguish details, so it performs better in multi-classification problems. The randomness of speech and bird calls is too strong, and PCA will filter them out, so the effect is very poor. 2 DPCA retains the details of the image, and the effect is very good. In this paper, the real-time and online fault detection method of a substation power transformer based on the amplitude-frequency properties of the AE signals signal is proposed. It can be used as an effective means of fault detection of power transformers to assist the staff of substation in routine maintenance of power transformer, which can reduce the workload of manpower and detect problems early to avoid major accidents. In this study, from the results of the experiments, it can be seen that the amplitude-frequency-based method can be effectively applied in the fault detection of the sub-station power transformer by taking advantage of the AE signals emitted by the transformer. We can also see whether PCA combined with SVM or 2 DPCA combined with SVM method, as long as suitable parameters are selected, can achieve satisfactory results. When the PCA combined with SVM is used and the linear is selected, the dimensionality reduction recon  
Figure 5: Visualization of the Sen for each class (A–G, see Table 1), presented for the best classifier SVM (RBF kernel and 2 DPCA at \(\upmu=0.99\)) and CNN. struction threshold has little effect on the accuracy rate, but for other kernel functions, the performance becomes terrible when the dimensionality reduction reconstruction threshold is relatively small. However, when the 2 DPCA combined with SVM is used, the choice of kernel function is more flexible than the PCA with SVM, but it is more easily affected by the feature dimensionality reduction reconstruction threshold whether under any kernel function. CNN method is easy to use and does not need to extract parameters manually, but it needs a long time training process and a large amount labeled data. In this work, the CNN does not show good performance as in other works perhaps because the labeled data is too small. It should be noted that a failing transformer may not make a characteristic sound, or the sound may be hidden with other characteristic noises. This is a critical problem that we faced. Nonetheless, our work supplies a reference for discriminate different running state for transformers. Although the sounds in this work are not indicative of impending fault conditions, the resolving ability presented in our experiments can help for discriminate the true sound which is indicative of impending fault conditions. Many kinds of faults will emit characteristic noises, some are continuous, which can be considered suit to be identified adopting the method of this work. Some sounds, such as that given by the ionization effect on the high voltage terminals, may be constantly monitored is indicative of the progressive deterioration of the system. This noise can be distinguished in different phases (three phases of the three-phase system) which must be analyzed to understand which phase is failing.