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 standalone microgrids with renewable energy resources (RERs) such as a photovoltaic (PV) system and fast changing loads face major challenges in terms of reliability and power management due to a lack of inherent inertial support from RERs and their intermittent nature. Thus, energy storage technologies such as battery energy storage (BES) are typically used to mitigate the power fluctuations and maintain a power balance in the system. This paper presents a model predictive control (MPC) based power management strategy (PMS) for such standalone PV/battery systems. The proposed method is equipped with an autoregressive integrated moving average (ARIMA) prediction method to forecast the load and environmental parameters. The proposed controller has the capabilities of (1) effective power management, (2) minimization of transients during disturbances, and (3) automatic switching of the operation of the PV between the maximum power point tracking (MPPT) mode and power-curtailed mode that prevents the overcharging of the battery and at the same time maximize the PV utilization. The effectiveness of the proposed method has been verified through a comprehensive simulation-based analysis.

Deep learning and digital image technologies have combined to create a potentially effective tool for identifying partial discharge (PD) patterns precisely. However, it is necessary to investigate which algorithm guarantees the best performance. The more common tools are restricted by a lack of training data and an advanced model in itself. Therefore, the main goal of this paper is to develop an efficient hybrid network comprising two deep networks, long short-term memory (LSTM), and convolutional neural network (CNN), for identifying the form of PD. A total of 8186×525 (non-PD×PD) images were applied to assess the proposed methods. The size of the PD type was increased to 3675 images using data augmentation techniques. The results indicated that the integration of CNN and LSTM networks can provide a more robust implementation for PD detection. The integrated CNN-LSTM deep network based on data augmentation outperformed features derived from a single deep network. The recall, F-measure, and classification precision have 99.9% as a validation accuracy with a 99.8% intersection over union and a loss of 0.004.

The Microgrid concept can solve major problems in distribution power systems. Moreover, it has been used for the interconnection in distributed energy resources (DERs) which are connected to the power system at the distribution voltage level. Furthermore, voltage stability considered as one of the main problems in power utilities. Reactive power needed to be controlled to maintain the voltage stable in the power system. This paper discusses the effect of DERs on voltage stability when the MG system operates under the normal condition and after different types of faults occur in the system. Simulation results provided to validate the robustness of the system to maintain the voltage stable at the desired value.

The flyback converter is widely used in modern power conversion systems due to its simple topology, single switch, low component count, and inherent galvanic isolation. This paper investigates the performance characteristics of flyback converters by analyzing their operation in Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM), with particular emphasis on voltage spike behavior. The main contribution of this study is the development of a mathematical model that correlates the operating mode (CCM or DCM) with the voltage spike across the power switch. In addition, the study examines the impact of duty cycle variations on voltage spike behavior. The results show that, for comparable operating conditions and moderate duty cycles, CCM operation generally results in lower voltage spikes than DCM due to reduced peak magnetizing current, while at higher duty cycles and load levels, CCM may exhibit increased voltage stress. The proposed approach was validated through MATLAB/Simulink simulations and a scaled-down experimental prototype. A proportional integral (PI) controller is implemented in simulation to ensure regulated output voltage under practical operating conditions. The results demonstrate that optimizing the duty cycle and accounting for the influence of conduction mode can enhance the reliability of flyback converters, making them suitable for applications requiring galvanic isolation and cost-effective solutions.