Work

Fault Diagnosis Method of Four-Level Converter Based on Improved Dual-Kernel Extreme Learning Machine

Year: 2025

Type: article

Abstract: To ensure the reliable operation of power converters and prevent catastrophic failures, this paper proposes a novel online fault diagnosis strategy for a four-level converter. The core of this strategy is an optimized multi-kernel extreme learning machine model. Specifically, the model extracts multi-scale features from three-phase output currents by combining Gaussian and polynomial kernels and employs particle swarm optimization to determine the optimal kernel fusion scheme. Experimental validation was performed on an online diagnosis platform for a four-level converter. The results show that the proposed method achieves a high diagnostic accuracy of 99.35% for open-circuit faults. Compared to conventional methods, this strategy significantly enhances diagnostic speed and accuracy through its optimized multi-kernel mechanism.

Source: World Electric Vehicle Journal

DOI: 10.3390/wevj16110617

Authors: Ning Xie, Duotong Yang, Xiaohui Cao, Zhenglei Wang

Institution: China Southern Power Grid (China)

Language: English

Topic: Multilevel Inverters and Converters

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,689