# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 6 Conclusions  
In this paper, we propose a method for PD detection and discharge type recognition in HV equipment, based on a microcontroller and a CNN, where both data acquisition and pattern-recognition algorithms are completed by the microcontroller, characterized by low cost and low power consumption. Based on testing and analysis, the following conclusions have been reached:  
(1) The PRPD maps obtained through preprocessing and superimposing 15 consecutively collected emission datasets feature more clearly defined localized emission character  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**PD Type**} & \multicolumn{2}{c}{**Recognition Accuracy**} \\ \cline{2-3}  & **CNN** & **NanoEdge AI** \\ \hline Plate Discharge & 99.5\% & 92.5\% \\ Ball-Plate Discharge & 98\% & 88.5\% \\ Tip Discharge & 97\% & 86\% \\ \hline Average value & 98.17\% & 86.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of recognition accuracy between CNN and NanoEdge AI.  
istics, a greater number of valid emission points, and significantly improved accuracy in distinguishing between PD types.  
(2) We propose a method for transferring the complex deep learning network model to the microprocessor side, and the accuracy does not significantly degrade after the network model is ported to the microprocessor. To validate the feasibility of this approach, we designed a PD pattern-recognition system based on a microcontroller and a convolutional neural network. Through laboratory tests, the microcontroller achieved an accuracy of 98.33% in recognizing three typical discharge types, which differs by only 0.97% from that of the PC.  
(3) The PD monitoring system designed using this method has a power consumption of 5.28 W and takes approximately 12 s for a single PD pattern-recognition. The system's cost is less than $150. In the engineering application field of PD pattern-recognition, this method offers a new detection approach characterized by its affordability, low power consumption, and a high recognition accuracy, rendering it of significant practical value.  
(4) The amplification detector circuit designed in this paper can broaden a 25 MHz pulse signal to approximately 30 us while preserving the primary characteristics of amplification and phase, allowing direct sampling using the microcontroller's built-in ADC.  
(5) According to the performance of the algorithm and storage space requirements, we chose a four-layer convolutional network, trained the network model, and used ST's X-CUBE-AI control for conversion and compression. The algorithm occupies 946.01 KB of Flash space and 125.36 KB of RAM space, and can be ported to run in a STM32H7 microcontroller. The pattern-recognition of local discharges is achieved by the microcontroller without requiring data interaction with the host computer.  
The method has a high accuracy in identifying the type of PD at the embedded end, and can identify a variety of discharge types. However, due to the limitation of data samples, the PD discharge phenomena occurring in different discharge environments are different, and the PD data generated by different degrees of insulation layer damage will also be different. Therefore, in order to improve the applicability of the system, in future research, we will collect as much data as possible from different PD types in the training samples, and we hope to integrate the model training into the system to run synchronously, so that when the accuracy of identifying the discharge types decreases, the training can be carried out in time to improve the ability of identifying PD types.  
Conceptualization, X.Y., Y.B. and C.C.; Methodology, X.Y., W.Z. and J.L.; Software, Y.B.; Validation, X.Y.; Investigation, Y.B.; Writing - original draft, Y.B.; Writing - review & editing, X.Y.; Supervision, X.Y.; Project administration, Y.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.