Transformer Fault Automatic Diagnosis Technology based on PSO-ELM

Transformer is a very important equipment in power system, its failure may lead to serious power system interruption and loss. Therefore, it is very important to develop an efficient and accurate transformer fault automatic diagnosis technology to ensure the reliable operation of the power system. This paper proposes a transformer fault automatic diagnosis technology based on particle swarm optimization (PSO) and extreme learning machine (ELM). By optimizing feature selection and using ELM algorithm for fault classification and diagnosis, the automatic diagnosis of transformer faults is made more rapid and accurate. The experimental data shows that the automatic diagnosis technology based on PSO-ELM has 99.6% accuracy and is 28.2% faster than the traditional technology, which provides strong support for the reliable operation of the power system. Future research can further explore the application potential of this method in other power equipment fault diagnosis and further improve its performance and reliability.