Multi-Physics Coupling Simulation and Service Life Prediction for High-Reliability Surge Arresters

Introduction

Surge arresters are critical for overvoltage protection in power systems. Conventional design methods struggle to quantify failure mechanisms under complex operating conditions. This study integrates electro-thermal-mechanical multi-physics simulation with machine learning-driven life prediction to achieve full lifecycle reliability assessment.

Technical Challenges  

1. Nonlinear Material Properties: Strong nonlinear voltage-current characteristics of ZnO varistors under lightning/switching surges  

2. Multi-field Coupling Effects: Electric field distribution affects thermal field, temperature gradients induce mechanical stress  

3. Operational Complexity: Coastal salt fog, high-altitude low temperature, and industrial pollution accelerate aging

 Key Innovations

1. Multi-scale Modeling

  - Macro-scale: 3D EM-Fluid-Thermal coupling (COMSOL/ANSYS)  

  - Micro-scale: Molecular dynamics simulation of ZnO grain boundary degradation  

2. Accelerated Aging Tests

 Industrial Validation  

Case study at ±800kV UHVDC converter station:  

- Fault warning accuracy: 92.3% (47% improvement)  

- Life prediction error: <±8% (IEC standard: ±20%)  

- Maintenance cost reduction: $340k/year per station  

Conclusion  

This technology enables the transition from scheduled replacement to condition-based maintenance. Future work will extend applications to offshore wind farms and other extreme environments.