Fault diagnosis for open-circuit faults in NPC inverter based on knowledge-driven and data-driven approaches

TL;DR

This paper introduces a novel fault diagnosis method for NPC inverter IGBTs combining knowledge-driven Concordia transform and data-driven random forests, improving accuracy and robustness in open-circuit fault detection across varying loads.

Contribution

The study proposes a hybrid fault diagnosis approach that enhances robustness and accuracy by integrating Concordia transform with random forests for NPC inverter faults.

Findings

Improved fault classification accuracy and robustness.

Effective fault location under different load conditions.

Reduced dependence on fault data through Concordia transform.

Abstract

In this study, the open-circuit faults diagnosis and location issue of the neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis approach based on knowledge driven and data driven was presented for the open-circuit faults in insulated-gate bipolar transistors (IGBTs) of NPC inverter, and Concordia transform (knowledge driven) and random forests (RFs) technique (data driven) are employed to improve the robustness performance of the fault diagnosis classifier. First, the fault feature data of AC in either normal state or open-circuit faults states of NPC inverter are analysed and extracted. Second, the Concordia transform is used to process the fault samples, and it has been verified that the slopes of current trajectories are not affected by different loads in this study, which can help the proposed method to reduce overdependence on fault data. Moreover, then the transformed fault samples are adopted to train the RFs fault diagnosis classifier, and the fault diagnosis results show that the classification accuracy and robustness performance of the fault diagnosis classifier are improved. Finally, the diagnosis results of online fault diagnosis experiments show that the proposed classifier can locate the open-circuit fault of IGBTs in NPC inverter under the conditions of different loads.