Switchgear plays a crucial role as a fundamental element within the power system, its stable operation is essential to guarantee the dependability and security of the entire power grid. Due to the specificity and complexity of the working environment, switchgear is prone to various faults. To guarantee the consistent functioning of the switchgear, a switchgear fault diagnosis model based on the improved Bald Eagle Search Algorithm (BES) optimized Support Vector Machine (SVM) is proposed. Twelve sets of state data on switchgear operation were collected to create a fault diagnosis dataset, the bald eagle search algorithm is improved by adopting the opposition-based learning strategy and the adaptive weight adjustment strategy to enhance the algorithm's optimization ability for the penalty factor c and the kernel function parameter g of the support vector machine. Case analysis shows that for the four types of switchgear operating states, the improved model achieves diagnosis accuracy of 97.44%, which realizes the accurate identification of switchgear fault diagnosis, and providing essential technical support for technicians to conduct switchgear maintenance.