Due to the unpredictable and fluctuating nature of solar photovoltaic (PV), energy storage systems (ESS), such as batteries, are always integrated with PV systems to smooth the power supply. In this article, a multiport dc–ac converter (MPC) with differential power processing dc–dc converter (DPPC) is proposed for battery ESS integrated PV systems. The MPC is capable of regulating most of active power among PV, battery and ac grid, and only the differential power (partial power) needs to be handled by the dc-dc converter. Hence, the major merits of higher integration, higher efficiency, and lower cost can be achieved by the proposed configuration. A new cooperative control scheme for the MPC and DPPC is investigated, aiming at realizing flexible active power flow. Besides, a modified space vector pulsewidth modulation is developed for the MPC, taking into the consideration of the voltage variation of both PV and battery. The active power controllability of the MPC and the power rating of the DPPC are quantitatively analyzed. The effectiveness of the proposed configuration, control and modulation schemes is validated by experimental results.

As is well known, the additional low-order harmonic currents and the huge computation amount of the cost function value with all available control sets are the two special issues of finite-control-set model predictive current control (FCS-MPCC) for multiphase inverters. Thus, this paper proposes a FCS-MPCC scheme with duty-ratio optimization for five-phase inverters. First, a group of the reconstructed virtual voltage vectors (V ³ ) with low-order harmonic elimination is defined and employed as the control sets in FCS-MPCC scheme. Thus, it leads to no requirement for harmonic items and weighting factors existing in the cost function. Then, a combination of zero vectors and the selected V ³ during each control horizon is adopted to rearrange the pulse sequence, which can realize constant switching frequency. In addition, the durations of the selected V ³ and zero vectors during each control horizon are also estimated to minimize the steady-state current ripples in different subspaces based on the cost function optimization. Finally, a comparison of the proposed V ³ -FCS-MPCC scheme and the other FCS-MPCCs are presented in simulation and experimental test. Simulation and experimental results verify that the proposed V ³ -FCS-MPCC scheme can remain the simplified structure, fast dynamic performance, constant switching frequency, and achieve minimum current ripples in different subspaces.

The installment of LC filter between the voltage source inverter (VSI) and motor terminal can reduce the value of dv/dt considerably, and further mitigate the surge voltage stress on the motor windings during the long-distance transmission. However, the sophisticated controller needs to be designed to solve the additional resonance problems caused by the filter. In this article, the finite-control-set model predictive control (FCS-MPC) is adopted to realize the q-axis current reference tracking for motor and suppress the filter resonance simultaneously. Different from the transfer-function-based AD scheme, the mechanism of resonance generation is revealed in the time-domain, where the cost function of FCS-MPC is designed to directly regulate the multivariable trajectory of the system. Therefore, the proposed FCS-MPC controller expresses the intuitive design principle, a simple structure, and fewer tuning parameters, which could be an alternative to the existing AD scheme. To reduce the number of sensors, the Kalman-filter based state estimation is used by considering the one-step delay of the control input. The cost function is further optimized to obtain a lower computational burden. The experimental results prove that the proposed FCS-MPC scheme can ensure the motor-friendly waveform and overcome the resonance problem effectively.

Graph theory within power electronics, developed over a 50-year span, is continually evolving, necessitating ongoing research endeavors. Facing with the never-been-seen explosion of graph-structured data, the state-of-the-art deep learning technique-Graph Neural Networks (GNNs), becomes the leading trend in machine learning within just recent five years and demonstrated surprisingly broad and prominent benefits covering from new drug discovery to better IC design. However, its promising applications in Power Electronics are still rarely discussed and its full potential remains unexplored. Addressing this gap, this review paper is the first to outline GNNs’ general workflow in power electronics, laying the groundwork and examining current GNN methodologies within the field. To bridge the gap in the sparse GNN literature within this domain, we also provide extended discussions on leveraging insights from GNN-aided circuit design to enrich power electronics research. Our work includes in-depth GNN-based case studies that demonstrate promising applications from converters to system-level power electronics, showcasing GNNs’ unique benefits and untapped possibilities (e.g., accurate component design, voltage predictions on IEEE-13 bus and 118 bus systems). Additionally, we provide a comprehensive survey of GNNs’ latest and successful applications, emphasizing their impact on energy-centric sectors, such as transportation electrification, smart grids. Considering the interdisciplinary nature of power electronics in modern energy systems, our review highlights the potential of GNNs emerge as a promising tool to decode the intricate behavior and dynamics of power electronics systems, and we hope such synergies between advanced AI methodologies like GNNs with the ever-evolving graph theory can lead to more powerful tools, novel methodologies, and advancements in the power electronics community.

This paper proposes a unified model predictive control (MPC) scheme for the integrated photovoltaic (PV) and battery storage system, where both of them are directly connected to the utility grid with high conversion efficiency through a multi-level neutral-point-clamped (NPC) inverter based multi-port interface. In such a system, the individual/unequal input voltage from each DC port raises control challenges, resulting in asymmetric voltage vector distribution and increased modulation complexity in the AC side. In this case, the finite-control-set MPC (FCS-MPC) scheme is proposed to make the power management be liberated from the modulator design thanks to the natural advantage, i.e., the direct control property without using a modulator. The multivariable-based cost function is designed for the AC side to regulate the injected grid current well to meet the IEEE 519-2014 standard. On the other hand, to proceed with the normal DC-side power flow, the capacitor voltage of each port is modeled, predicted, and also regulated through the unified cost function in the MPC framework. As a result, each PV array can work at the individual maximum power point (MPP) and the battery can be automatically charged/discharged to compensate for the power difference. The five-level four-port inverter-based simulation and three-level dual-port inverter-based experiment are conducted to verify the multi-mode operation of the integrated system and the advantages of the proposed controller.

The inductive-capacitive-inductive (LCL)-filtered grid-connected inverter features a high-order plant, complex parameter design, and vulnerability under disturbance factors. The transfer-function-based active damping (TFAD) scheme is commonly adopted to mitigate the resonance problem but with cascaded control loops and high design complexity. On the contrary, multiobjective optimization can be easily realized in model predictive control (MPC) via a predefined cost function. As the coupling dynamic among state variables, a hybrid cost function, which contains the grid current, inverter-side current, and capacitor voltage tracking, is designed to guarantee system stability. The optimized control law is generated according to the minimization of the designed cost function, which is further transformed into the driving pulse by the modulator stage. In this process, the weighting factors are exactly arranged to place the desired closed-loop poles. The control law can ensure zero error tracking of grid current reference, which is better than the state feedback way. The hybrid cost function with modified reference is also proposed to reject the harmonics caused by the unhealthy grid condition. The control delay is compensated by using the forecast reference and the predictive state variable estimated from the observer. The proposed scheme performs the intuitive concept, reduced tuning complexity, and fast dynamic response. Simulated results and experiments are presented to verify the proposed control strategy.