# An Operational Status Assessment Model for SF6 High-Voltage Circuit Breakers Based on IAR-BTR

## TL;DR

This paper introduces a new model for assessing the operational status of SF6 high-voltage circuit breakers using an integrated risk model that considers both internal and environmental factors.

## Contribution

The novel IAR-BTR model integrates internal and environmental factors for operational status assessment of SF6 circuit breakers.

## Key findings

- The proposed IAR-BTR model achieves 95.78% accuracy in operational status assessment.
- The model improves predictive performance by incorporating spatiotemporally non-stationary factors.
- DFP-Growth algorithm enhances the model's computational efficiency.

## Abstract

With the rapid advancement of digitalization and intelligence in power systems, SF6 high-voltage circuit breakers, as the core switching devices in power grid protection systems, have become critical components in high-voltage networks of 110 kV and above due to their superior insulation performance and exceptional arc-quenching capability. Their operational status directly impacts the reliability of power system protection. Therefore, real-time condition monitoring and accurate assessment of SF6 circuit breakers along with science-based maintenance strategies derived from evaluation results hold significant engineering value for ensuring secure and stable grid operation and preventing major failures. In recent years, the frequency of extreme weather events has been increasing, necessitating a comprehensive consideration of both internal and external factors in the operational status prediction of SF6 high-voltage circuit breakers. To address this, we propose an operational status assessment model for SF6 high-voltage circuit breakers based on an Integrated Attribute-Weighted Risk Model Based on the Branch–Trunk Rule (IAR-BTR), which integrates internal and environmental influences. Firstly, to tackle the issues of incomplete data and feature imbalance caused by irrelevant attributes, this study employs missing value elimination (Drop method) on the fault record database. The selected dataset is then normalized according to the input feature matrix. Secondly, conventional risk factors are extracted using traditional association rule mining techniques. To improve the accuracy of these rules, the filtering thresholds and association metrics are refined based on seasonal distribution and the importance of time periods. This allows for the identification of spatiotemporally non-stationary factors that are strongly correlated with circuit breaker failures in low-probability seasonal conditions. Finally, a quantitative weighting method is developed for analyzing branch-trunk rules to accurately assess the impact of various factors on the overall stability of the circuit breaker. The DFP-Growth algorithm is applied to enhance the computational efficiency of the model. The case study results demonstrate that the proposed method achieves exceptional accuracy (95.78%) and precision (97.22%) and significantly improves the predictive performance of SF6 high-voltage circuit breaker operational condition assessments.