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.

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.

Ensuring the reliability of multilevel inverters (MLIs) is a significant focal point in numerous sensitive industrial applications. In a conventional MLI, an abnormal situation arises when a fault occurs within any of the switches. Consequently, the other components may suffer severe damage. In such a situation, the faulty inverter must be removed. Thus, this work presents a new fault tolerant MLI (FT MLI) topology, which incorporates more redundant switching states in comparison to normal MLIs. As a result, the proposed FT MLI topology is resilient to open-circuit faults in the switches. An explanation of the operation of the proposed topology is provided, accompanied by a description of the FT capabilities for symmetric mode as well as asymmetric mode. The proposed topology has per unit total standing voltage of 6.33 and 6.5 for symmetric and asymmetric modes of operation, respectively. Simulation results using MATLAB/Simulink and real-time results using Typhoon hardware-in-the-loop (HIL)-402 device are presented to demonstrate the FT MLI topology’s effectiveness. This paper compares the proposed topology with other FT MLI topologies, emphasizing the advantages of the proposed topology.

A novel seven-level switched-capacitor inverter (7L-SCI) topology has been proposed in this article, which is being applied to a grid-connected photovoltaic (PV) system with DC superconducting cable. The proposed topology has the additional benefit of inherent fault tolerance to the switch faults. A comparative analysis is provided between the proposed topology and different seven-level MLI topologies. Additionally, DC superconducting cable is utilized for its energy storage properties to smooth out fluctuations in PV power. A superconducting cable can conduct high-speed charges and discharges, allowing the cable to deal with output PV power fluctuations that are difficult to control using conventional technologies. The application of the superconducting cable reduces the spikes in inverter’s source current which is generally a drawback of using a switched capacitor multilevel inverter. Through the use of a laboratory prototype, the performance of the presented topology is examined. Finally, simulation results of MATLAB/Simulink^® are presented to show the grid-connected system’s performance with and without superconducting cables.

This study employs artificial neural networks (ANN) to predict the dielectric properties of nanofluids formulated with mineral oils, synthetic esters, and natural esters. Individual ANN models are developed for various nanoparticle–oil combinations using the Levenberg–Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization backpropagation (BRG) algorithms implemented in MATLAB^®. The models focus on regression analysis of key electrical parameters, including AC breakdown voltage (ACBDV), dielectric dissipation factor (DDF), and relative permittivity (RP) of transformer insulating oils and their corresponding nanofluids. Comparative evaluation across different samples reveals distinct performance trade-offs among the algorithms. LM achieves faster convergence with strong DDF prediction, SCG exhibits inconsistent performance across properties, and BRG consistently delivers superior prediction and robustness for RP and DDF, as indicated by higher R² and lower mean squared error values. Overall, BRG emerges as the most reliable and accurate approach, while LM is preferable when faster convergence is prioritized with acceptable accuracy.