# Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
## 1 Introduction  
The main function of metal-oxide surge arrersers (MOSAs) is to restrict the voltage across equipment terminals to non-damaging levels when lightning impulse surges are forced on an electrical system. A surge arrester is a protective device for limiting surge voltages on equipment from over-voltage transients caused by lightning or switching events [1]. Facing the threat of lightning and voltage surges in power equipment switches, the protection of power systems is an important guideline of surge arrersers [2, 3, 4, 5]. As an index of high-fusion equipment insulation deterioration, the partial discharge (PD) phenomenon does not damage equipment immediately, but it will induce insulation breakdown during long-term operation [6, 7]. [PERSON] et al. [8] proposed using the thermal images generated by infrared thermal imaging to test for abnormal heating spots or regions in the surge arrester and used artificial neural networks (ANN) to analyze surge arrester faults, including sealing problems, internal dampness, and varistor aging. [PERSON] et al. [9] proposed using an acoustic camera to test high-frequency sound waves, and employed artificial intelligence (AI) or AI cloud systems to analyze the PD phenomenon of surge arresters. [PERSON] et al. [10] proposed using a high frequency current transformer (HFCT) to sense the grounding cable conductor current of a surge arrester to obtain its PD signal, and used the phase resolved partial discharge (PRPD) spectrum to evaluate its state. [PERSON] et al. [11] proposed utilizing the leakage current of surge arresters to identify the severity of surface contamination based on cross-stockwell transform and a sparse autoencoder. [PERSON] et al. [12] proposed an online monitoring method using prtransform based on feature extraction of the frequencies, phase angles, and magnitudes of all frequency components for the surge arrester considering pollution and dry conditions when measuring the total leakage current. [PERSON] et al. (2013) proposed an approach based on the analysis of the harmonic elements distribution and leakage current waveform for an evaluation of the status of gapless surge arresters without the need to examine the arrester interior. The method used the values of mean, maximal, and harmonic elements of surge arrester leakage current regardless of the voltage level of the power network where they were installed. [PERSON] et al. (2014) proposed a monitoring method based on the use of self-organizing maps (SOM) artificial neural networks to identify the harmonic features of the total leakage current for a metal-oxide surge arrester. The six fault types, including sealing loss, superficial pollution, varistor degradation, internal humidity, varistors displacement, and non-uniform voltage distribution, were built in the arresters to evaluate the technical capability. According to the results, the identification accuracy of this method had a high ration of almost 98%. While most studies focus on monitoring surge arresters, little literature has been proposed a surge arrester simulation and detection platform built based on AI. Therefore, this study constructed surge arrester fault models based on the normal state and three common fault types, including aging of the internal valve, internal humidity, and salt damage to the insulation. This study built an intelligent deep learning method detection system for surge arresters based on discrete wavelet transform (DWT) and symmetrized dot pattern (SDP) combined with CNN for identifying surge arrester PD faults, and used 240 test data to evaluate the performance. Data were measured utilizing an NI PXI high-speed data acquisition (DAQ) instrument. The proposed surge arrester fault feature learning model was expected to identify fault types so that early maintenance could be taken to avoid accidents.