Intelligent Identification of Grading Electrode Scale in Converter Valve Water-Cooling Systems Using X-Ray Images

The grading electrodes in the water-cooling system is crucial for the stable operation of the converter valve in ultra-high voltage converter station. However, incrustation can significantly affect the performance of the electrode. Scaling accumulation in these systems can lead to reduced heat dissipation efficiency, increased energy consumption, and even equipment failure. Traditional scaling detection methods, such as manual inspection and chemical analysis, are often inefficient, subjective, and unable to provide real-time monitoring. To address these challenges, this study proposes an intelligent scaling recognition method based on X-ray imaging and deep learning. A YOLOv8_OBB based model is designed to automatically detect and classify scaling on grading electrodes. The proposed approach utilizes electrode diameter regression and corrected rules to assess the severity of scaling. The accuracy of this model in detecting scaling reaches 98.5%, and the average error in diameter prediction is 0.016mm. Experimental results demonstrate that the model achieves high precision in identifying scales and measure diameter. This research contributes to the development of automatic online monitoring equipment in the future.