Impedance identification is an important tool for the estimation of the equivalent impedance of the grid or of a grid-connected equipment allows monitoring the correct operation of a network [4]–[6], detecting grid reconfigurations [7], [8], and potentially adapting grid-connected converters during operation [9], [10]. The impedance estimation is based on the injection of controlled small-signal perturbations in the system, and on the analysis of the corresponding small-signal response. The perturbations should be small to avoid altering the steady-state operation of the system, while also possess adequate magnitude to be detectable by the sensing equipment [11]. These perturbations can be generated by ad-hoc perturbation injection converters (PICs) [12], [13], allowing precise shaping of the perturbation injected, of the desired injection time, magnitude and frequency content. However, installing dedicated hardware for perturbation injection purposes might be inconvenient or impractical, especially when cost or space constraints within the network are taken into account [14], [15]. Therefore, it would be convenient to use the same existing grid-connected converters as perturbation injection sources [3], [16]. In this case, by superimposing a small-signal perturbation on top of the steady-state operating waveforms of an existing converter, the impedance identification could be achieved without any additional hardware. Nevertheless, as of today, the majority of grid-connected converters are not inherently designed for perturbation injection purposes, which results in inherent limitations and challenges. One of these challenges comes from the restricted bandwidth of grid-connected converters. This aspect, which has been thoughtfully addressed in [17], imposes an upper limit on the bandwidth of the injected perturbation signals, which is primarily determined by the switching frequency of the semiconductor devices and by the sampling frequency of the adopted controller platforms. Nevertheless, considering the ongoing advancements in the semiconductor devices technology (and particularly in wide-bandgap devices) and the increasing availability of faster control platforms (including DSP and FPGA boards), this limitation is expected to become progressively less restrictive in the upcoming years. Another major challenge in using existing grid-connected converters for perturbation injection purposes comes from a limited voltage reserve, which refers to the difference between the maximum voltage that the converter is capable of applying and the voltage of its specific operating condition. Indeed, most existing grid-connected converters are designed to operate with a limited DC-bus voltage and at high modulation indexes

An improved non-isolated resonant converter with integrated magnetics is proposed to achieve high step-down voltage conversion ratio for 48 V data center applications. The non-isolated resonant converter is composed of autotransformer cell and LLC resonant cell. The outputs of the two cells are in parallel to achieve higher output current. By adopting a symmetrical half-bridge circuit structure, the risk of DC bias in high-frequency transformers is addressed, and balanced current stress on synchronous rectifier switches is achieved. Magnetic integration of the autotransformer and high frequency transformer is proposed to achieve higher power density and higher efficiency. Topology, operation principle, integrated magnetic design and implementation of the proposed non-isolated resonant converter are analyzed in detail. Experimental prototype with 8:1 conversion ratio is developed for the front-end bus converter of 48 V data-center power systems. The power density of the prototype is 1.734 kW/in3, and the maximum efficiency is 98.2%.

Grid-forming inverters (GFIs), which can mimic the behaviors of conventional synchronous generators to provide the frequency and voltage support for the electricity grids, face the challenge of overcurrent during grid faults due to the voltage-source output characteristics. The power semiconductors of inverters are incapable to withstand fault current and easily destroyed. To tackle the overcurrent dilemma encountered by GFIs during voltage drops, this paper proposes an adaptive virtual impedance-based fault current limiting strategy. This adaptive strategy can adjust dynamically the virtual impedance value in real-time based on the magnitude of fault currents, and thereby suppress fault currents effectively. To analyze the impacts of the adaptive impedance on the stability of GFIs, an impedance model composed of the adaptive impedance, grid and voltage control loops, is established in the dq reference frame. The influence of the adaptive virtual impedance control parameters on the stability of the grid-forming inverter system is evaluated through the generalized Nyquist criterion. The efficacy of the proposed adaptive virtual impedance strategy in fault current limitation and the accuracy of the stability analysis are validated through the comprehensive simulation results carried out in Matlab/Simulink and OPAL-RT semi-physical platform.

Constant common mode voltage (CCMV) is critical in Solar Photovoltaic (SPV) systems. Maintaining CCMV further eliminates the voltage transitions across the parasitic capacitance of the PV panel, making it a low-frequency output voltage waveform. This further reduces the CMC and leakage current in the SPV systems. This paper presents a CCMV switched capacitor PV inverter configuration that maintains a low-frequency terminal voltage while considering the effect of switch device junction capacitance (SDJC). Thus, the proposed configuration eliminates the transitions in the common mode and terminal voltage due to the SDJC. The two switched capacitors employed in the proposed configuration are charged and discharged within each switching period, supporting the self-charge balance feature apart from low voltage and RMS current rating, reducing its size and value. Further, the proposed system effectively utilizes the PV source as switch capacitors and the DC-bus buffer capacitor across the PV source comes in parallel in each switching period. The proposed configuration can also operate at lower modulation indices, generating three levels with CCMV. A new mathematical analysis considering SDJC is given for the derivation of expression for CMV and terminal voltage, which is further verified using simulation and experimental results.

Grid-tied T-type inverters are widely used in photovoltaic, electric vehicle charging piles, and other grid-tied applications. However, the transistor fault seriously harms the reliability of the inverter. This paper proposed a simple and novel open-circuit fault diagnosis method for the grid-tied T-type inverter based only on the three-phase currents. At first, the Hausdorff distances among the three-phase normalized current accumulations are obtained to perform the fault detection. The process is tolerant of inverter transient conditions. Then, the fundamental frequency values of the three-phase currents are calculated to locate the faulty phase. At last, the faulty transistor is located by some intermediate conditions. In particular, the method is suitable for modulation index regulation. Experiments verify the effectiveness of the proposed method.

Discrete space vector modulation (DSVM) technique is commonly adopted in model predictive control (MPC) to mitigate current harmonics and torque ripples. Nevertheless, the employment of DSVM typically leads to heavy computational burden and high switching frequency (SF). To solve these problems, a novel model predictive current control (MPCC) scheme based on DSVM is proposed in this paper for permanent magnet synchronous motor (PMSM) drives. Firstly, a simple voltage vectors (VVs) pre-selection strategy based on the stator flux increment is introduced to eliminate the redundant virtual VVs generated by DSVM for the purpose of lower computational burden. Then, a hierarchical search strategy is designed to generate the candidate VVs online, which can further simplify the DSVM technique. In addition, an efficient optimal switching sequence (OSS) method is also employed to reduce the switching losses without weakening the control performance. Compared to the existing strategies, the proposed scheme possesses lower complexity and SF as well as superior performance. The effectiveness of the proposed scheme is supported by comparative experimental results on a PMSM platform.

To address the issue of unknown False Data Injection (FDI) attacks on controllers in DC microgrids, a distributed secondary controller for DC microgrids based on a linear extended state observer is presented to ensure that the system can achieve the control objectives of voltage regulation and current sharing even when subjected to FDI attacks. Firstly, the secondary control problem of multi-bus DC microgrids with FDI attacks is transformed into a first-order Multi-Agent System (MAS) fault-tolerant consistency problem, and the impact of FDI attacks on the microgrid system is analyzed. Then, a Linear Extended State Observer (LESO) is employed to detect the estimated injected FDI attack signals. Furthermore, a fault-tolerant secondary controller is designed to eliminate the adverse effects of FDI attacks on the system. The stability of the proposed method is verified using a Lyapunov function. Finally, the effectiveness and superiority of the proposed control method are experimentally demonstrated.

In a multi-terminal modular multilevel converter-based high voltage direct current (MMC-HVDC) grid, due to the coupling between converter stations, it is difficult to obtain an accurate analytical solution on the DC fault current through the traditional equivalent resistance-inductance-capacitance circuit model (E-RLCM). In this paper, a data-driven parameter inversion method is proposed to derive the accurate equivalent parameters in the E-RLCM by combining the electromagnetic transient simulation data with the backpropagation neural network, and the polynomial regression. In this way, the accurate analytic calculation expression of the DC fault current for a multi-terminal MMC-HVDC grid with a pole-to-pole fault (PTPF) is obtained. To verify the effectiveness of the proposed method, simulations are performed for a four-terminal MMC-HVDC grid with a PTPF by PSCAD. The results show that the average calculation error of the DC fault current using the inversion parameters is significantly reduced from over 10% to 2.84%.

Multi-level inverters (MLIs) are becoming increasingly popular in high-speed motor drive systems for modern electric aircraft applications. However, two significant limitations are associated with current MLIs technology: (1) the high switching losses due to the high carrier switching frequency and (2) the complex modulation schemes required to maximize the DC source utilization. Consequently, the development of new topologies to mitigate these limitations is imperative for the rapid advancement of future electric aircraft systems. This paper introduces a six-level twelve-step inverter (TSI) that utilizes twelve switches and three planar high-frequency transformers. Implementing the proposed configuration ensures maximum DC source utilization, with a peak phase voltage of 5Vdc/3. The proposed solution presents less semiconductor losses than the conventional MLIs, surpassing conventional MLIs, associated with neutral point clamped (NPC), flying capacitor (FC), and cascaded H-bridge (CHB). Experimental results demonstrate the TSI’s operation under static and dynamic conditions and its capability to function in three different modes: three-step, six-step, and twelve-step operations. The paper also offers a comprehensive design of the proposed planar transformer, supported by theoretical analysis, finite element analysis (FEA), and experimental validation.

Partial shading (PS) presents a significant concern in PV arrays due to its substantial impact on system performance, causing reduced power output, distorted PV characteristics, and power loss. Therefore, this paper introduces a novel accelerated linear convergence factor-based spotted hyena optimization for dynamic PV array reconfiguration (DPVAR). The key aim of the accelerated linear convergence factor is to reduce row difference in each tier while minimizing the need to relocate modules, within a significantly reduced iteration count. The algorithm pursues two primary objectives: enhancing power generation under PS and efficiently finding the global maximum power within a minimum period while minimizing steady-state oscillations. Furthermore, the proposed algorithm is adaptable to dynamic PS conditions and applicable for symmetric and asymmetric PV arrays of any size. The effectiveness of the technique is evaluated through simulation and experiments. In addition, the performance of the proposed technique is compared to the particle swarm optimization (PSO) based reconfiguration method. The investigation reveals that the proposed reconfiguration technique demonstrates an average power enhancement of 17.57% compared to the before-reconfiguration method and 7.88% compared to the PSO reconfiguration method.