Multilevel inverters (MLIs) are used in a variety of industrial applications in high- and medium-voltage systems. The modularity, high-power output from medium voltages, and low harmonic content are some of the advantages of MLIs. The reliability of MLIs is quite important. The reliability is affected by different kinds of faults occurring in the MLIs. In MLI circuits, switching devices are the most vulnerable components and have a major involvement in all types of faults. As an outcome, it is necessary to take proper corrective action in the event of a fault. This work provides a comprehensive review of different fault tolerant (FT) solutions for MLIs in the event of switch fault. Moreover, various single-phase FT MLI topologies are reviewed, along with their constructional features, merits, and demerits. This work also proposes a comparison approach that integrates novel factors to account for fault tolerance quantitatively. A comparison investigation verifies the effectiveness of the proposed method. The FT operation of an existing five-level FT MLI topology is discussed, simulated, and experimentally verified.
The presence of bypass diodes in photovoltaic (PV) arrays can mitigate the negative effects of partial shading conditions (PSCs), which can cause multiple peak characteristics at the output. However, conventional maximum power point tracking (MPPT) methods can develop errors and detect the local maximum power point (LMPP) instead of the global maximum power point (GMPP) under certain circumstances. To address this issue, several artificial intelligence (AI)‐based methods have been proposed, but they result in complicated and unreliable methodologies. This study introduces the driving training‐based optimization (DTBO) method, which aims to address the partial shading (PS) problem quickly and reliably in maximum power point (MPP) detection for PV systems. DTBO improves tracking speed and reduces fluctuations in the power output during the tracking period. The proposed method is extensively verified using the Typhoon hardware‐in‐the‐loop (HIL) 402 emulator and compared to conventional methods such as particle swarm optimization (PSO), and JAYA, as well as the recently proposed adaptive JAYA (AJAYA) method for MPPT in a PV system under similar conditions.
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