Keywords
modular multilevel converter; open circuit fault of submodule; Sand Cat swarm optimization; extreme learning machine; fault diagnosis
Abstract
To enhance the fault diagnosis of the switch tube of the modular multilevel converter (MMC) submodule, a Sand Cat swarm optimization (SCSO) algorithm is improved. This improved SCSO (ISCSO) algorithm is employed to optimize the fault diagnosis of an extreme learning machine (ELM). Cubic chaotic mapping, a spiral search method, and a sparrow alert mechanism are used to improve the three stages of sand cat search, so as to enhance the convergence speed and search capability of the algorithm. An MMC model is developed on the MATLAB/SIMULINK platform, where the bridge arm circulation is used as the input when a fault occurs in the submodule. By comparing the fault diagnosis performance of ISSO-ELM against ELM optimized by other algorithms, the results show that the proposed method can effectively identify submodule faults. It shows feasibility and superiority in MMC fault diagnosis, offering better fault diagnosis performance.
DOI
10.19781/j.issn.1673-9140.2025.01.026
First Page
245
Last Page
255
Recommended Citation
ZHANG, Bide; HE, Hengzhi; SHAO, Shuai; QIU, Jie; MA, Junmei; and CHEN, Guang
(2025)
"Improved Sand Cat swarm optimization‑based extreme learning machine method for MMC submodule fault diagnosis,"
Journal of Electric Power Science and Technology: Vol. 40:
Iss.
1, Article 26.
DOI: 10.19781/j.issn.1673-9140.2025.01.026
Available at:
https://jepst.researchcommons.org/journal/vol40/iss1/26