Application of DE-ELM Algorithm in SF6 Circuit Breaker Contact Ablation Evaluation

Abstract

When assessing the ablation status of circuit breaker contacts, the average dynamic contact resistance of the arc contacts of high-voltage SF6 circuit breakers is crucial. The average dynamic contact resistance of arc contacts on circuit breakers under various current levels may be accurately predicted using a method based on a differential evolution algorithm and extreme learning machine (DE-ELM), which is suggested in this research. Combining optimization algorithms yields the ideal input weight and hidden layer bias of the ELM method. The DE-ELM approach has outstanding anticipation performance for anticipation data under various current levels when compared to other anticipation methods. Finally, an expert system for assessing the ablation state of contacts based on the DE-ELM algorithm is created using the average dynamic contact resistance data of arc contacts predicted by DE-ELM. As a guide for the upkeep of high-voltage SF6 circuit breakers, the ablation status of contacts is categorized into four grades: A-level ablation, B-level ablation, C-level ablation, and D-level ablation.