The increasing adoption of renewable energy sources (RES), such as solar photovoltaics and wind turbines, is transforming electricity generation. However, integrating RES within DC microgrids (DCM) for applications such as fast DC charging in electric vehicles (EVs) presents challenges, including low inertia, power fluctuations, and voltage instability. This study addresses these challenges with novel control strategies and optimization algorithms. A hybrid Firefly Algorithm-Particle Swarm Optimization (FA-PSO) approach is used to tune Takagi-Sugeno Fuzzy Inference Systems (TSFIS), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Fractional Order Proportional-Integral-Derivative (FO-PID) controllers. This strategy optimizes power management within the DCM, ensuring faster convergence, superior accuracy, and reduced topological constraints. In addition, a comprehensive Small Signal Stability Analysis (SSSA) evaluates the impact of the proposed hybrid optimization techniques on DC microgrid stability. Crucially, a hardware prototype validates these strategies under real-world uncertainties, such as varying wind speed and solar insolation, demonstrating their effectiveness and feasibility for practical DC microgrid applications with integrated EV charging.

The integration of Renewable Energy Resources (RERs) into electrical grids introduces significant challenges concerning the reliability and stability of the grid. This paper focuses on these challenges, particularly the issues of real-time load forecasting and adaptive inertia control in renewable integrated grids. A data-driven, deep learning-based approach is proposed to dynamically forecast real-time load and renewable energy generation, using the New England IEEE 39-Bus Power System as a case study. To enhance the dynamic performance of the microgrid, the paper introduces an enhanced fractional extended state observer-based linear active disturbance rejection control mechanism coupled with a feedback architecture. This control scheme aims to provide adaptive inertia to the system, thus improving its ability to handle fluctuations and intermittencies inherent in RERs. The effectiveness of the proposed controller is rigorously compared with existing approaches through simulation studies, validating its superior performance for the IEEE 39-Bus Power System under examination. To further substantiate the findings, a hardware-in-loop real-time experimental analysis is conducted using OPAL-RT hardware. This hardware-based analysis serves as a functional validation of the proposed data-driven forecasting algorithm confirming its viability to improve the grid reliability.

This paper introduces a novel switched-capacitor-based 9-level inverter topology to meet IEEE standards for low total harmonic distortion (THD) in grid-connected inverters. The new design addresses the trade-off between increasing output voltage levels to reduce harmonics and the consequent rise in device count. The proposed topology is streamlined, consisting of 12 switches, 3 capacitors, 2 diodes, and a single DC source. Comparative analysis with similar topologies confirms the advantages of the new design. The experimental results show that the proposed inverter achieves a THD of 13.58% in its output voltage. The topology is validated through its application in a single-stage, three-phase photovoltaic system connected to the grid. Simulations are conducted using MATLAB/Simulink to test the system's performance. Furthermore, hardware-in-the-loop experiments are performed using OPAL-RT 5700 real-time simulators to further substantiate the efficacy of the proposed topology.

The multi-phase machine (MM) drives are best suitable for safety-critical applications due to its inherent fault-tolerant capability. The fault-tolerant operation of MM drives is challenging and necessary for applications such as off-shore ship propulsion, high-way electric vehicles, and more electric aircraft. A two-level voltage source inverter (VSI) fed five-phase induction motor (FPIM) drive has been considered for the post-fault performance assessment. In this paper, an improved fault-tolerant direct torque control (DTC) technique is proposed for the FPIM drive based on virtual vector (VV)s} assessment under the open-phase fault condition. The theoretical analysis is carried out to investigate the impact of VVs on the change in torque as well as flux response at different speeds and loading conditions. Based on this analysis, a modified lookup table, flux, and torque hysteresis bands are designed to improve the functioning of the fault-tolerant DTC of FPIM drive. The FPIM drive performance is analyzed and tested experimentally with an open-phase fault. Studies have confirmed the reliability and superiority of the proposed DTC controller over existing post-fault solutions. In addition, the robustness of the FPIM drive at steady-state and dynamic operating conditions is evaluated from the given results, including the transition from pre-to-post fault operation.

Communication delays and false data injection attacks pose significant threats to the frequency control of automatic generation systems. This paper presents a Golden Jackal Optimizer (GJO)-enhanced frequency-shifted internal model control (FIMC) scheme aimed at addressing these challenges in a dual-area thermal power system. The FIMC approach employs a pole and zero shifting variable that acts as a system robustness indicator. The paper determines an analytical search range for this variable using the Routh-Hurwitz criteria, which were later utilized by GJO. The robustness and performance of the GJO-tuned FIMC are tested against random and step load disturbances, as well as system nonlinearities. The paper models various false data injection threats, assessing the effectiveness of the GJO-tuned FIMC in neutralizing these threats under inherent communication delays. Finally, the proposed strategy is verified in real-time through hardware, employing the OPAL-RT platform. The results are compared with a recent strategy, underscoring the advanced efficacy of the proposed approach.

This article presents a new open-end winding induction motor (OEWIM)-based dual-motor differential four-wheel drive (D4WD) for the electric vehicle (EV). Constant speed operation through cruise control is achieved using direct torque control (DTC) algorithm. The redundant vectors are used in the switching vector selection of the DTC algorithm to achieve balanced battery currents. Fault-tolerant operation (FTO) of the drive is demonstrated, where the EV will work with full torque even if one of the inverters in rear motor drive or front motor drive or both fail. The dynamic model of the proposed drive is presented. The proposed drive and FTO are verified through simulation as per the FTP-75 driving cycle. An experimental prototype of the proposed drive is developed, and the abovementioned algorithms are verified experimentally as per the FTP-75 and HFET driving cycles. Both the simulation and experimental results are presented, and these results agree with the theoretical observations. Stable operation of EV for the entire test cycle under normal operation, as well as inverter fault conditions, is demonstrated.