4.5 Article

Asymmetric Multilevel Inverter Topology and Its Fault Management Strategy for High-Reliability Applications

Journal

ENERGIES
Volume 14, Issue 14, Pages -

Publisher

MDPI
DOI: 10.3390/en14144302

Keywords

multilevel inverters; power electronics; fault tolerance; fault detection

Categories

Funding

  1. Taif University Researchers Supporting Project, Taif University, Taif, Saudi Arabia [TURSP-2020/121]
  2. Capability Systems Centre, UNSW-Canberra at ADFA [CRGS/MOHD TARIQ/01]

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The paper presents a fault-tolerant asymmetric reduced device count multilevel inverter topology capable of producing an 11-level output under healthy conditions and operating after open-circuit faults. Nearest-level control based Pulse width modulation is used, and reliability analysis is carried out. Additionally, an AI-based fault detection method with decision trees is proposed for fault location detection with low computational requirements and high accuracy.
As the applications of power electronic converters increase across multiple domains, so do the associated challenges. With multilevel inverters (MLIs) being one of the key technologies used in renewable systems and electrification, their reliability and fault ride-through capabilities are highly desirable. While using a large number of semiconductor components that are the leading cause of failures in power electronics systems, fault tolerance against switch open-circuit faults is necessary, especially in remote applications with substantial maintenance penalties or safety-critical operation. In this paper, a fault-tolerant asymmetric reduced device count multilevel inverter topology producing an 11-level output under healthy conditions and capable of operating after open-circuit fault in any switch is presented. Nearest-level control (NLC) based Pulse width modulation is implemented and is updated post-fault to continue operation at an acceptable power quality. Reliability analysis of the structure is carried out to assess the benefits of fault tolerance. The topology is compared with various fault-tolerant topologies discussed in the recent literature. Moreover, an artificial intelligence (AI)-based fault detection method is proposed as a machine learning classification problem using decision trees. The fault detection method is successful in detecting fault location with low computational requirements and desirable accuracy.

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