Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence

Abstract

This paper proposes a method for detecting and recognizing partial discharges in high-voltage (HV) equipment. The aim is to address issues commonly found in traditional systems, including complex operations, high computational demands, significant power consumption, and elevated costs. Various types of discharges were investigated in an HV laboratory environment. Discharge data were collected using a high-frequency current sensor and a microcontroller. Subsequently, this data underwent processing and transformation into feature sets using the phase-resolved partial discharge analysis technique. These features were then converted into grayscale map samples in PNG format. To achieve partial discharge classification, a convolutional neural network (CNN) was trained on these samples. After successful training, the network model was adapted for deployment on a microcontroller, facilitated by the STM32Cube.AI ecosystem, enabling real-time partial discharge recognition. The study also examined storage requirements across different CNN layers and their impact on recognition efficacy. To assess the algorithm’s robustness, recognition accuracy was tested under varying discharge voltages, insulation media thicknesses, and noise levels. The test results demonstrated that the algorithm could be effectively implemented on a microcontroller, achieving a recognition accuracy exceeding 98%.

Novel Reference Method for the Characterization of PD Measuring Systems Using HFCT Sensors

“…The recorded pulses generally correspond to a pulse-type noise signal, internal defect, surface defect, floating potential element or corona effect [ 9 , 24 ]. Several developments are focused on the mathematical and artificial intelligence processing of the information related to the PRPD patterns in order to automatically identify the defects associated with them [ 27 , 28 ]. Some of these developments are integrated in commercial measuring systems.…”