The utilization of renewable energy sources (RESs) has become significant throughout the world, especially over the last two decades. Although high-level RESs penetration reduces negative environmental impact compared to conventional fossil fuel-based energy generation, control issues become more complex as the system inertia is significantly decreased due to the absence of conventional synchronous generators. Some other technical issues, high uncertainties, low fault ride through capability, high fault current, low generation reserve, and low power quality, arise due to RESs integration. Renewable energy like solar and wind are highly uncertain due to the intermittent nature of wind and sunlight. Cutting edge technologies including different control strategies, optimization techniques, energy storage devices, and fault current limiters are employed to handle those issues. This paper summarizes several challenges in the integration process of high-level RESs to the existing grid. The respective solutions to each challenge are presented and discussed. A comprehensive list of challenges and solutions, for both wind and solar energy integration cases, are well documented. Finally, the future recommendations are provided to solve the several problems of renewable energy integration which could be key research areas for the industry personnel and researchers.

Model predictive control (MPC) is the result of the latest advances in power electronics and modem control. It is regarded as one of the best techniques when it comes to handling of nonlinearities in the intrinsic model of induction motor (IM). Conventional MPC utilizes weighting factors in the objective function that are tuned after rigorous experimental work which can be improved by utilizing the more mature intelligent optimization techniques like NSGA-II etc. In this study, the weighting factor optimization for the conventional MPC control of IM based on NSGA-II with TOPSIS decision-making criteria is studied. A control algorithm is designed, and an experimental test setup is made to obtain the results of this intelligent MPC which are compared with conventional MPC based on some performance indices like torque and flux ripple, switching frequency loss etc.

This paper introduces advanced power-electronic converter topologies for Electric Vehicles (EVs) using a four-phase DC/DC interleaved boost converter (FP-IBC) and a five-level T-type DC/AC multilevel converter. A comparison between the proposed topologies and other converter topologies is performed and discussed. The simulation results are analysed to evaluate the converters based on power loss calculations and harmonic analysis. The converters are studied at different switching frequencies and various loading conditions to reflect their effects on the converter losses. The results highlight the proposed converters' higher efficiency compared to other studied converter topologies in electric vehicle applications.

The effect of multilevel inverter supply on power losses in magnetic cores and electrical machines is studied. A dynamic numerical model for the hysteresis, eddy current, and excess losses in a core lamination is first developed. By both measurements and simulations for a ring-core inductor, we demonstrate how increasing the number of inverter voltage levels decreases the iron losses when compared with traditional two-level supply. Although the switching frequency has a significant impact on the iron losses in the case of a traditional two-level inverter, using three or five voltage levels makes the losses almost independent of the switching. Finally, finite-element simulations show that similar reductions are also possible for the core losses of 150-kVA and 12.5-MW wound-field synchronous machines, in which rather low switching frequencies are typically used. Calorimetric loss measurements are also presented for the 150-kVA machine in order to confirm the significant effect of switching frequency on the core losses with two-level inverter supply.

This article proposes an intensive study for common-mode voltage (CMV) elimination/reduction in a five-level dual T-type multilevel converter (MLC) drive. An advanced and fast model predictive control (MPC) approach is developed for CMV reduction (CMVR) and CMV elimination based on the preselection of the switching states (SS). In addition, the proposed approach considers the torque control and the capacitor balancing of the proposed drive as essential aspects of MLC drives. A detailed study of the impact of the converter SS on the CMV is presented. The main features of the proposed MPC approach are reducing the complexity of the cost-function, the tuning burden of the weighting factors, and the computation time by more than 80% compared to conventional MPC techniques. A hardware implementation based on Silicon Carbide T-type MLC is built. The experimental results demonstrate the effectiveness of the proposed MPC approach to mitigate/eliminate the CMV along with achieving the torque command and balancing the dc-link capacitors. Unlike conventional CMVR techniques, the proposed approach reduces the CMV by 75% of its normal values without jeopardizing the harmonic contents, drive efficiency, and flux as well as torque ripples.