The solid-state transformer is the enabling technology for the future of electric power systems. The increasing relevance of this equipment demands higher standards for efficiency and losses reduction. The dual active bridge (DAB) topology is the most usual DC-DC converter used in the solid-state transformer, and is responsible for part of its switching losses. The traditional phase-shift modulation used on DAB converters presents significant switching losses during the operation with reduced loads. The alternative Triangular and Trapezoidal Modulations have been proposed in recent literature; however, there are limitations on the maximum power these techniques can deal with. This paper presents an adaptive model predictive control to select among these three techniques, according to the converter model, the one that minimizes the switching losses and allows the current demanded by the load. Moreover, an alternative cost function is proposed, including the output voltage and current. Through real-time simulation, using a 1000 V/600 V 12 kW DAB converter, it is shown that the proposed control is able to reduce the losses on the converter. Furthermore, the proposed control presents fast and accurate response, and precise transition between the modulation techniques.

This paper have studied the dynamic of a 2.0 MW Doubly Fed Induction Generator (DFIG) during a severe voltage sag. Using the dynamic model of a DFIG, it was possible to determine the current, Electromagnetic Force and flux behavior during three-phase symmetrical voltage dip. Among the technologies of wind turbines the DFIG is widely employed; however, this machine is extremely susceptible to disturbances from the grid. In order to improve DFIG Low Voltage Ride-Through (LVRT), it is proposed a novel solution, using Superconducting Current Limiter (SCL) in two arrangements: one, the SCL is placed between the machine rotor and the rotor side converter (RSC), and another placed in the RSC DC-link. The proposal is validated through simulation using PSCAD™/EMTDC™ and according to requirements of specific regulations. The analysis ensure that both SCL arrangements behave likewise, and are effective in decrement the rotor currents during the disturbance.

The growing number of renewable energy plants connected to the power system through static converters have been pushing the development of new strategies to ensure transient stability of these systems. The virtual synchronous generator (VSG) emerged as a way to contribute to the system stabilization by emulating the behavior of traditional synchronous machines in the power converters operation. This paper proposes a modification in the VSG implementation to improve its contribution to the power system transient stability. The proposal is based on the virtualization of the resistive superconducting fault current limiters’ (SFCL) behavior through an adaptive control that performs the VSG armature resistance change in short-circuit situations. A theoretical analysis of the problem is done based on the equal-area criterion, simulation results are obtained using PSCAD, and experimental results are obtained in a Hardware-In-the-Loop (HIL) test bench to corroborate the proposal. Results show an increase in the system transient stability margin, with an increase in the fault critical clearing time (CCT) for all virtual resistance values added by the adaptive control to the VSG operation during the short-circuit.

This paper presents a comprehensive study of parameter estimation for three-phase induction motors (IMs) using hybrid optimization methods and a comparative evaluation of static and dynamic modeling approaches. A hybrid metaheuristic combining the Sine Cosine Algorithm (SCA) and Particle Swarm Optimization (PSO) is developed to identify optimal motor parameters efficiently. The approach utilizes a static model for rapid estimation, with final parameter values validated against a dynamic model to ensure accuracy in operational predictions. Results confirm that the static model provides robust parameter estimates for key performance metrics, including torque, power factor, and current, aligning well with experimental results from real-motor no-load tests. Parameters estimated by the proposed method demonstrate a high adherence with the motor real measurements. Comparisons also reveal the limitations of static models in scenarios requiring state-space accuracy, such as observer-based control applications. This study concludes by recommending further exploration of alternative motor modeling structures and the hybrid optimization algorithm for parameter estimation.