Classification of PRPD Pattern in Cast- Resin Transformers Using CNN and Implementation of Explainable AI (XAI) With Grad-CAM

Abstract

Cast-resin transformers are affected by deterioration due to manufacturing defects and continuous load. Studying PD, which is capable of detecting defects or degradation in advance, is important. With the rapid advancement of AI technologies, research on PD classification using CNN models is being actively conducted. However, due to the black box problem, it is impossible to explain the reasoning behind the learning outcomes. Therefore, relying solely on predictive outcomes of learning for PD classification raises issues of reliability. Recent studies in various fields are progressing with the application of XAI to address the black box issue of CNNs, aiming to identify the criteria used for making predictions. However, research on applying XAI in AI-based PD classification is currently insufficient. Therefore, further study on the implementation of XAI is necessary. In this paper, an excellent CNN model was applied to image classification for PD classification of cast-resin transformers, and the grad-cam model was used for XAI. This approach proposes a method for humans to comprehend the rationale behind the learning outcomes. The training data includes artificial defects created in laboratory settings and noise captured in cast-resin transformers using UHF sensors. Our research demonstrated that PD and noise due to defects can be identified with an accuracy of approximately 97%. The reasons for successful and failed results were analyzed through XAI. Consequently, it was observed that the application of XAI to CNN models leads to the construction of a more reliable model.

Keywords

• Cast-resin transformer
• PD
• pattern classification
• convolution neural network (CNN)
• explainable artificial intelligence (XAI)
• gradient weighted class activation mapping (Grad-CAM)