DC microgrid cluster (DCMGC) is a dynamic network formed by connecting a group of geographically neighboring DC microgrids (DCMGs) through tie-lines. Each DCMG collaborates with other DCMGs to achieve maximum economic benefits through flexible power flow management within the DCMG and at the system level. Therefore, DCMGCs require communication, computing, and control to manage the power flow. As a result, the DCMGCs are naturally represented as cyber-physical systems (CPSs). However, DCMGCs are of high penetration of distributed energy resources, which creates significant randomness at both resource and load sides. Consequently, these systems will experience large disturbances leading to serious stability problems like high oscillations or even collapse. In this paper, Takagi-Sugeno (T-S) modeling is utilized to reduce the large signal Lyapunov stability of DCMGs to a series of linear matrix inequalities (LMIs). The impact of key circuit parameters, control parameters, communication delay, and cyber-attacks on the large signal stability of DCMGCs is revealed, and the region of attraction (ROA) of the network is estimated as well. Finally, the large signal stability analysis is verified by experimental results. The findings of this work will be instrumental in developing more effective control strategies to enhance the stability and reliability of DCMGCs.

This paper deals with a modular input-independent output-series (IIOS) multiport dc power electronic transformer (DCPET), which can interface with multiple dc units (such as PV array, storage devices and dc loads) to the medium-voltage dc (MVdc) bus directly without extra low-voltage dc (LVdc) converters. Therefore, the number of converters and the system expense are greatly reduced and saved when compared with the conventional dc distributed network. Due to the input power of these dc units being different and the multiple modules being in series connection on MVdc side, which will lead to the output voltage imbalance between submodules (SMs). By inserting a single LC branch between adjacent SMs to transfer the differential power in the proposed topology, SM output voltage mismatched problem can be solved. Moreover, all SM power switches can realize zero voltage switching (ZVS), which further ameliorates the system efficiency. Finally, the above theoretical analysis is verified by simulation and experimental results, and the proposed multiport PET can operate stably in different working states.

To enhance the stability of a DC microgrid, a promising approach is to control the energy storage converter via the virtual DC machine control (VDMC), which can improve inertia and damping of the system. However, the conventional VDMC suffers from poor dynamic performance during large disturbances, partially due to its fixed control parameters. To track such problem, this paper proposes a voltage compensation control and parameter adaptive method for the VDMC of microgrid energy storage converters. Firstly, the dynamic process of microgrid bus voltage under disturbance is analyzed, and an armature voltage compensation control loop is then constructed. Subsequently, the influences of inertia, damping and compensation coefficients on the system dynamic characteristics are evaluated, and a parameter adaptive control method for the improved VDMC is proposed. Simulation and experimental results demonstrate that the proposed control strategy can effectively mitigate the DC bus voltage fluctuations, with faster response and smaller overshoot than the conventional VDMC strategy.

To address the problems of high current harmonics, large torque ripples, and heavy computational burden in the finite control set model predictive control (FCS-MPC), this paper proposes an efficient multi-vector model predictive current control (MPCC) scheme for permanent magnet synchronous motor (PMSM) drive. Firstly, a simple pre-selection method based on the trace of the stator current increment is proposed to obtain the candidate optimal voltage vectors. This pre-selection method avoids the heavy computational burden of evaluating all voltage vectors and is easy to implement. Then, to further reduce the torque ripples and current harmonics, the dwelling time of each voltage vector is achieved in inverse proportion to its cost function. Compared to the standard means, the proposed scheme is able to obtain great performance while greatly decreasing the computational burden and complexity. And its effectiveness is experimentally validated through comparative assessments.

This article presents an initial solution based selective harmonic elimination (SHE) method for multilevel inverter (MLI) that aims to solve SHE problem with high accuracy while significantly reducing the number of iterations. Initial SHE solution is defined as a set of initial switching angles that has less accuracy as compared to the final SHE solution. To find initial SHE solution, tunicate swarm algorithm (TSA), grey wolf optimization (GWO) and whale optimization (WO) are used to solve five-dimensional SHE problem. A comparative analysis demonstrates that TSA achieved the desired initial SHE solution within lesser iterations as compared to GWO and WO for a wide range of modulation indices. The initial switching angles found using TSA are further optimized using Newton-Raphson (NR) method being the proposed TSANR method, which exploits the SHE search space with quadratic rate of convergence to find final SHE solution. The proposed TSANR method significantly minimizes the computational time and further improves the accuracy of the SHE solution as compared to the state-of-the-art SHE methods. Detailed simulations, experimental and harmonic analysis under dynamic change in modulation index are presented to show the efficacy of the proposed SHE method in terms of control over fundamental and detrimental harmonics.

PR controller has been widely researched in various control systems for its robustness and simplicity. However, a traditional PR controller with relatively small integral gain, used for higher-order harmonics to keep stability, will cause increases in magnitudes and decreases in phase around resonant frequency, and jeopardize stability. These all call for a more precise realization of PR controller. This paper proposes a cascade-formed PR controller realization method, which proves to realize PR controller more accurately even with a relatively small integral gain. The method is to decompose a PR controller into multiple independent PR units, and each PR unit is realized by mapping PR controller’s parameters to its pole and zero positions. The distance between a pole-zero pair is found related to frequency characteristic error and is restricted accordingly to limit the error. Comprehensive comparisons of PR controllers realized by cascade form and the traditional parallel form have been conducted theoretically and experimentally, verifying that the cascade realization method is more accurate.

A typical degradation mechanism of insulated gate bipolar transistor (IGBT) modules is the bond wire degradation (BWD), and thus the bond wire aging monitoring (AM) shows much attractiveness for IGBT modules. However, the performance degradation with junction temperature swings and load current dependence in many bond wire AM methods remains an obstacle. To address this, a bond wire AM method based on the back propagation neural networks (BPNN) is proposed in this paper, in which the on-state voltage drop (OVD) is used as the indicator of bond wire AM. In the proposed AM method, a multi-physical field coupling model of the IGBT module is established. Then, with the assistance of the model, the characterization behaviors of the OVD are thoroughly analyzed. According to the analysis, it is known that the junction temperature swings and load current dependence may obviously degrade the performance of the proposed AM method. Afterward, BPNN is adopted to deal with these issues. Finally, the performance of the proposed AM method is explored through extensive experimental tests.

DC microgrid clusters (DCMGCs), as deeply integrated cyber-physical systems, are formed by interconnection of multiple DC microgrids, and use distributed control to achieve power distribution with high reliability and scalability, and further reflect advantages of distributed energy resources-based generations. However, sharing of information among control agents by distributed manner in the DCMGCs renders the systems vulnerable to cyber-attacks. Among various cyber-attacks, false data injection attacks (FDIAs) can be carefully designed as stealth attacks, which can cause errors in the power management of DC-MGCs without manifestation of instability phenomena and even mislead existing detection methods to make incorrect judgments. To address this issue, this paper presents an alternative data-based strategy to detect FDIAs and mitigate the impact of the attacks in cyber network of DCMGCs. The classification conditions of FDIAs are discussed according to the different responses of DC-MGCs to the attacks. Furthermore, the core detection problem is transformed into identifying whether the system outputs match by selecting alternative communication data to circumvent complex modeling. Finally, hardware-in-the-loop experimental results on the dSPACE™ MicroLabBox platform with universal digital signal processing (DSP) controllers validate the proposed strategy.

The rise in demand for energy storage solutions and the widespread adoption of electric vehicles (EVs) have given rise to the creation of vehicle-to-everything (V2X) topologies. V2X technology enables communication and power flow between EVs, the grid, homes, buildings, and other loads. This paper provides an acute review of V2X topologies, including the communication and power flow between EVs and the grid, homes, vehicles, and loads. The different types of V2X communication, including IEEE standards, the 3rd Generation Partnership Project (3GPP), ISO standards, Wi-Fi, and Internet of Things (IoT)-based protocols, are discussed, along with their advantages and disadvantages. Finally, the challenges and opportunities for the adoption of V2X topologies are presented.

Modular multilevel converters (MMCs) are widely utilized in medium voltage grid-connected applications, typically employing carrier phase shift modulation. However, the high switching frequency associated with this modulation scheme often increases power losses and thermal stress on semiconductor devices, negatively impacting their efficiency and reliability. In this paper, we propose an adaptive switching frequency scheme that divides the carrier frequency into several discrete zones based on load conditions. Through analytical evaluation of the carrier frequency, our proposed method optimizes it to meet power quality and capacitor voltage ripple requirements, effectively reducing power losses and thermal stress. A simulation case study based on a 15-MVA MMC demonstrates a remarkable 21% reduction in annual power losses and a 12% reduction in annual damage, thereby improving efficiency and reliability. Additionally, experimental measurements conducted on a 15-kW downscale platform validate around 10% reduction in power losses while fulfilling power quality and capacitor voltage ripple requirements.