Transformer Fault Detection Based on SSA-BP Neural Network

The study of transformer defect detection is critical to the safe and dependable operation of the electrical system. This paper introduces the theory of neural networks and combines Salp swarm algorithm with BP neural network to enhance the performance of transformer fault detection, improve the accuracy and convergence rate of detection methods, and solve the main issues present in the practical application of transformer fault detection technology. A method based on BP neural network optimization using the Salp swarm algorithm is proposed in order to detect transformer defects during operation. The final simulation outcomes demonstrate that the enhanced method put forward in this research has strong global optimization capability, stability, and application value in detecting transformer faults.