# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 1 Introduction
In recent years, deep learning theory has demonstrated significant advantages in data feature extraction and the acquisition of sample feature information, pushing to the forefront of artificial intelligence. CNNs belong to a class of deep feed-forward networks capable of capturing high-dimensional nonlinear features within data. Their outstanding performance in image recognition has garnered significant attention in the realm of local discharge pattern-recognition [28; 29]. For instance, [PERSON] and [PERSON] employed the feature parameter method to extract discharge features and utilized CNNs for local discharge pattern-recognition, achieving a high recognition accuracy. In the literature [22], a PD feature dataset was initially constructed by extracting feature information such as peak voltage, pulse width, and phase angle, which were subsequently classified using CNNs. The literature [30] converted marginal spectrograms of PD into grayscale matrices, which served as input for a deep residual network. This approach facilitated the classification and recognition of feature parameters associated with PD. Both of these methods primarily extracted characteristic information of local discharges using the feature parameter method, resulting in the recognition accuracy levels reaching 90%. PRPD spectra were visually represented as images. The literature [23] extracted moment features and grayscale covariance matrix features from PRPD spectra, utilizing them as input for a support vector machine (SVM) for classification. The literature [28] employed time-domain waveform images as input and utilized CNNs for direct pattern-recognition. The literature [31] used CNNs to automatically extract image features from PRPD, enabling the classification and recognition of PRPD patterns. However, in cases where individual data profiles were closely similar, the method suffered from a lack of useful discharge features, impacting the accuracy of localized discharge pattern-recognition. The literature [32] developed an HV equipment monitoring system based on embedded technology, designed specifically to monitor the occurrence of PD phenomena in the insulation layer. Nevertheless, it could not accurately classify specific discharge types using deep-learning techniques. Similarly, the literature [27] utilized low-cost microcontrollers to capture acoustic emission pulses and detect anomalies like turn-to-turn discharges, creeping discharges, or surface discharges. However, it could not accurately classify specific discharge types. Recognizing the specific discharge type remains a challenge, as deep learning technology demands substantial computing power and memory space. Consequently, in the aforementioned literature, CNN identification was conducted on the PC side, allowing for the determination of the occurrence of PD on the embedded side. However, this did not enable the precise identification of specific discharge types. In summary, this paper proposes a PD pattern-recognition method for HV equipment that is based on a microcontroller and a CNN, where both data acquisition and algorithm implementation are handled by the microcontroller, significantly reducing system costs and power consumption. This technique captures the discharge signal through an HFCT [33; 34], and the signal, after conditioning by a hardware circuit, is sampled using the built-in ADC of the STM32F769I microcontroller. The acquired data is analyzed by PRPD to generate an n-q-\(\varphi\) plot, where \"n\" denotes the number of discharges, \"q\" denotes the amount of discharges, and \"q\" denotes the phase of discharges. Then, the PRPD spectrograms of multiple data are superimposed in the same phase, and the distribution of discharge pulses in the spectrograms is counted based on the superimposed spectrograms, and the distribution of discharge point positions in the PRPD diagrams is constructed based on the characteristic information of the discharge pulse. This is then converted into a grayscale map in PNG format.