PSO-IGWO 优化混合 KELM 的变压器故障诊断方法.

王 享; 黄新波; 朱永灿

Intelligent fault diagnosis of transformer is the main link to promote the development of the smart grid. However, the traditional single intelligent diagnosis algorithm can not deal with a large amount of incomplete information of transformer, resulting in low accuracy of fault diagnosis. Therefore, a transformer fault diagnosis method based on improved gray wolf optimization algorithm (PSO-IGWO) with optimized hybrid kernel extreme learning machine (KELM) was proposed. The fault diagnosis model was established by the hybrid KELM, and the structural parameters of hybrid KELM were optimized by particle swarm optimization algorithm. The simulated experiment was made, combined with the dissolved gas analysis(DGA)sample data. The results show that compared with BPNN and ELM, the diagnostic accuracy of this algorithm is improved by 16.24% and 5.71% respectively, which can provide decision support for the safe and stable operation of transformers.

Journal of Xi'an Polytechnic University, 2019, Vol 33, Issue 2, p154

1674-649X

Academic Journal

10.13338/j.issn.1674-649x.2019.02.007