# Defect Prediction for Capacitive Equipment in Power System
###### Abstract  
As a core component of the smart grid, capacitive equipment plays a critical role in modern power systems. When defects occur, they pose a significant threat to the safety of both other equipment and personnel. Hence, it is of great significance to predict whether defects occur in capacitive equipment in advance. To achieve this goal, we propose a novel method that integrates the weight of evidence (WOE) feature encoding with machine learning (ML). Five models, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and linear classification, are employed with WOE features for defect prediction. Furthermore, based on the prediction of equipment with defects, an additional prediction is conducted to determine the potential defect level of the equipment. Experimental results demonstrate that the performance of each algorithm significantly improves with WOE encoding features. Particularly, the RF model with WOE encoding features exhibits optimal performance. In conclusion, the proposed method offers a promising solution for predicting the occurrence of defects and the corresponding defect levels of capacitive equipment. It enables relevant personnel to focus on and inspect equipment predicted to be at risk of defects, thereby preventing major malfunctions.  
defect prediction; capacitive equipment; WOE encoding; machine learning +
Footnote †: journal: Applied  
0000-0001-4000-0000-0000-0000-0000-0000-00000-0000-0000-0000-00000-00000-00000-0000-00000-00000-00000-0000-00000-00000-00000-00000-00000-00000-0  
Undoubtedly, online monitoring methods play a crucial role in promptly detecting and addressing faults in capacitive equipment. Our proposed approach offers an alternative perspective for studying defects in capacitive equipment by leveraging historical defect information to train machine learning models. Subsequently, current device information is utilized as an input to predict the occurrence of device defects in advance, along with the potential defect severity levels. This enables relevant personnel to concentrate on monitoring the operational status of devices predicted to be at risk of experiencing defects. The key contributions of our work are summarized as follows:  
* Unlike traditional methods relying on online monitoring and diagnostic techniques, we introduce a proactive approach. By utilizing machine learning algorithms, we predict whether defects will occur in capacitive equipment and their severity level before they manifest. This proactive prediction enables preemptive maintenance and intervention, ultimately enhancing the reliability and safety of the equipment.
* Successful application of the weight of evidence (WOE) feature encoding, based on the scorecard model, for preprocessing capacitive equipment data. This approach enhances the data preparation stage and improves the effectiveness of subsequent analysis.  
The remainder of this paper is organized as follows. Section 2 reviews the current research status. Section 3 provides the process of constructing the model and related algorithms. Section 4 introduces the data preprocessing and model construction. Section 5 displays the experimental results and discusses the findings. Finally, Section 6 concludes our work.