We discuss the analytical temporal response of a microring resonator excited through a bus waveguide by an optical rectangular pulse. Finite difference time domain (FDTD) simulations illustrate the analytical solution and help in understanding the meaning of the different coupling regimes. In addition, we show that the temporal dynamics allows determing the coupling regime while the commonly used spectral characterization in the stationary regime does not. We also take advantage of the simulation to highlight the phase shift between the input and the output signals in the different coupling regimes. Finally, measurements on a Si3N4 microring resonator are performed and analyzed in the case of under-coupling regime to illustrate how the time response study leads to the Q-factor determination.

With the increasing integration of renewable energy sources into electrical grids, energy storage systems have become crucial for stability and regulation. Thus, dedicated two-stage AC-DC converters are essential for integrating batteries into the grid infrastructure. These converters typically employ either single-phase or three-phase inverters paired with a Dual Active Bridge (DAB) in a back-to-back configuration, ensuring high efficiency and galvanic isolation. The conventional control scheme for such converters involves two separate PI-based control loops, one for each stage. This paper proposes a novel control strategy based on a Modulated Model Predictive Control (MMPC) algorithm for a two-stage AC-DC converter. The proposed approach utilizes a single cost-function optimization routine to simultaneously control both the grid-side and the battery-side stages, simplifying the control architecture and enhancing performance. Finally, the proposed MMPC is implemented on a DSP and FPGA-based board and the experimental results show that the proposed solution achieves faster dynamics than conventional control with no overshoots or undershoots.

This paper presents an advanced control strategy for a Synchronous Reluctance Machine (SyRel) in an Open-End Winding (OEW) configuration. The goal is to enhance performance and control flexibility in dual-inverter systems, which present strong multivariable interactions and nonlinear dynamics. To address these challenges, a Finite-Control-Set Model-Predictive Control (FCS-MPC) method is proposed, as it can directly handle multiple control objectives and system constraints within a unified framework. A single cost function is used to regulate motor torque, the auxiliary converter Floating Capacitor (FC) voltage, and the power factor at the main side. This enables coordinated control of both converters without cascaded loops. The predictive controller uses an experimentally identified magnetic model of the SyRel machine, which accounts for magnetic nonlinearities, improving prediction accuracy under dynamic response. Experimental validation confirms the effectiveness of the proposed strategy. The system shows fast dynamic response, robust regulation of control objectives, and consistent unity power factor at the main converter. A comparison with conventional PI-based control highlights the advantages of the proposed method in terms of power factor regulation and dynamic performance, particularly under strong nonlinearities introduced by the FC voltage control. These results confirm that the proposed cost function formulation, with its specific control terms, effectively enables regulation of the overall system.

This paper proposes a Tree-Based Finite-Control-Set Model Predictive Control (TBFCS-MPC) strategy for a Cascaded H-Bridge (CHB) inverter, integrated with an adaptive online Look-Up Table (LUT) for real-time battery voltage balancing. The proposed TBFCS-MPC reduces computational complexity by limiting the number of evaluated voltage vectors to a maximum of 22, compared to 127 in conventional FCS-MPC approaches. This reduction enables real-time execution on embedded hardware, achieving a cost function evaluation time of only $\bf {4.2\ \mu s}$, compared to $\bf {24.1\ \mu s}$ with state-of-the-art solutions. The LUT dynamically selects switching states based on instantaneous battery voltages, ensuring active balancing. Experimental results validate the method's fast current reference tracking, effective balancing, and significantly reduced computational burden, making it a practical solution for high-performance, battery-fed multilevel inverter applications.

With the increasing integration of renewable energy sources (RESs) into power systems, batteries are playing a critical role in ensuring grid reliability and flexibility. Among them, vanadium redox flow batteries (VRFBs) have emerged as a promising solution for large-scale storage due to their long cycle life, scalability, and deep discharge capability. However, achieving optimal control and system-level integration of VRFBs requires accurate, real-time modeling and parameter estimation, challenging tasks given the multi-physics nature and time-varying dynamics of such systems. This paper presents a lightweight physics-informed neural network (PINN) framework tailored for VRFBs, which directly embeds the discrete-time state-space dynamics into the network architecture. The model simultaneously predicts terminal voltage and estimates five discrete-time physical parameters associated with RC dynamics and internal resistance, while avoiding hidden layers to enhance interpretability and computational efficiency. The resulting PINN model is integrated into a modulated model predictive control (MMPC) scheme for a dual-stage DC-AC converter interfacing the VRFB with low-voltage AC grids. Simulation and hardware-in-the-loop results demonstrate that adaptive tuning of the PINN-estimated parameters enables precise tracking of battery parameter variations, thereby improving the robustness and performance of the MMPC controller under varying operating conditions.