The modular multilevel converter (MMC) has become one of the most promising topologies for high- and medium-voltage applications. The steady-state analysis of MMCs provides guidance for system design and performance evaluation. The existing steady-state analysis models developed for three-phase three-wire MMC systems can only deal with balanced ac-side circuits and symmetric arm conditions. And they fail to provide accurate analysis considering unbalanced ac-side circuits or when the components parameters in different arms of the MMC are different. To address this problem, a generic steady-state analysis model is presented in this paper to precisely characterize the steady-state harmonic performance of the three-phase three-wire MMC under various operation conditions. An industrial-level MMC simulation system is built to demonstrate the feasibility and validity of the proposed model.

This paper presents the arm energy model and submodule capacitance sizing of the asymmetric alternate arm converter (AAAC) topology that has been recently proposed for high voltage DC (HVDC) applications. A step-by-step derivation of the converter arm energy model is presented to arrive at a final arm energy expression that aids to determine the maximum arm energy deviation of the converter that is required to determine the minimum submodule capacitance of the converter. A simplified mathematical expression is also derived to help determine the required submodule capacitance for the converter from the maximum arm energy deviation, the number of submodules per converter arm and the maximum allowed submodule capacitor voltage deviation (ripple factor). The derived arm energy expression is validated using both simulation and experimental results. The calculated, the simulated, and the experimentally measured converter arm energy values have a good match with each other verifying the accuracy of the derived converter arm energy model. In addition, comparison of the energy storage requirements of the AAAC topology with other similar converter topologies such as the conventional (symmetric) alternate arm converter topology and the modular multilevel converter topology is provided to further highlight the significance of the derived converter arm energy and submodule capacitance sizing expressions.

The scale of photovoltaic (PV) system tends to be large but the ability of power transmission has not been fully improved. When the power generated by PV array is imbalance with the power output by inverter, system becomes unstable. The instability caused by power imbalance in large PV system is rarely discussed but causes big loss once it happens. Even energy storage and reactive power compensation can help to balance the power, there is a lack of a reliable control strategy for solving this problem. This paper analyzes the stability problem caused by power imbalance in large-scale PV system, and aims to propose an optimization control to improve system stability. The output ability of large PV system is studied, and the influence of grid impedance and power factor on inverter output power is analyzed. One method based on real-time inverter control is investigated to calculate the maximum output power of inverter system, and the optimization control is proposed and implemented based on maximum power point tracking (MPPT). Finally, the correctness of the theories is validated by simulations and experiments.

The wired power supply method used in the coal mine safety monitoring system (CMSMS) sensor has potential safety hazards, and an alternative power supply method is required. In order to ensure the reliability of the power supply system, a new type of compact PCB resonator is proposed in this paper. The idea of this work is, First, the compensation inductor in the inductive power transfer (IPT) system using LCL-LCL topology is integrated with the power coil. The introduction of the DDQ coil structure to construct a double-coupled LCL topology improves the energy transfer density; Secondly, a four-layer PCB resonator that does not require additional compensation capacitors is constructed, which reduces the system volume and ensures reliability; Finally, the proposed work gives the design and optimization method of the coil parameters, and build the experimental prototype, which owns the 1.08MHz working frequency. The charging current and voltage are 350mA and 24.5V, respectively. Simulation and experiments verify the feasibility of the proposed PCB resonator for sensor battery charging.

Inrush current during start-up transient is a special challenge for half-bridge LLC resonant converters, especially when bulk capacitors are connected to the output of the converter. For the half-bridge LLC resonant converter, we propose a fundamental harmonic amplitude-frequency hybrid modulation strategy, so that the output voltage can be regulated continuously from zero to the normal output voltage. By adopting the proposed bipolar and unipolar pulse width modulation strategies, the amplitude of the fundamental harmonic voltage of the primary side of the converter is regulated, allowing the normalized gain of the converter to be continuously adjusted from 0 to 1. In combination with the pulse frequency modulation, the output voltage can be regulated over a wide range. The fundamental harmonic amplitude-frequency hybrid modulation strategy for half-bridge LLC resonant converters is analyzed, including its principle, characteristics, and implementation. The feasibility and effectiveness of the proposed method has been verified based on the experimental results.

The fault current in a modular multilevel converter-based high voltage direct current grid (MMC-HVDC-Grid) will rise rapidly when DC short-circuit fault (DCSCF) occurs, which poses a great challenge to the prompt and accurate fault protection detection technology. Aiming to solve the issue of (DCSCF) location detection in MMC-HVDC-Grid, a statistical feature-based improved deep belief network (IDBN) method is proposed in this paper. By the proposed method, the three features, such as the standard deviation, information entropy, and kurtosis are extracted from the fault current training samples within a single terminal of MMC-HVDC-Grid and fused as the inputs of IDBN. The IDBN is trained by using the pre-training of contrastive divergence (CD) algorithm and the back fine-tuning of the supervised learning algorithm, and then the DCSCF detection is accomplished with the SOFTMAX as the output layer. In order to improve the detection accuracy and reduce the adverse effect on the converter station, the exponential decay learning rate is used to optimize the network, which can speed up the rate of convergence for the training process. Taking four-terminal MMC-HVDC-Grid as an example, the proposed method is tested and compared with other fault detection methods based on traditional neural networks, such as back propagation, radial basis function, stacked autoencoder (SAE), and support vector machine. The simulation results show that the performances of the overall classification accuracy, kappa coefficient, Jaccard distance, and detection speed by the proposed method are all superior to the other traditional neural network fault detection methods mentioned above. In addition, the online DC fault current data are continuously extracted into the trained IDBN model, and the performance of the proposed method is verified through the real-time digital simulation platform.

Shading of photovoltaic panels is one of the most prevalent causes of power decrease. A partial shading condition (PSC) creates multiple distinctive peaks in the power-voltage (P-V) curve. Reconfiguration distributes partial shade on a complete array. It reduces the mismatch power losses and boosts the power generation of the solar photovoltaic (SPV) array. This paper proposes a novel static PV array reconfiguration method named the novel shade distribution (NSD). The performance of the proposed method has been evaluated and compared with the best conventional total-cross-tide (TCT) method and available reconfiguration(AR) methods, sudoku (SK), optimal sudoku (OSK), and zig-zag (ZZ). The performance has been investigated under eight partial shading conditions with five performance evaluation parameters. This paper validates the performance of the proposed method through MATLAB/Simulink and hardware experimentation.

Facing the fast growth of grid-tied installation and the intermittent nature of solar radiation, the power ramp rate control (PRRC) has become an essential function in PV power systems. Traditionally, the system heavily relies on a battery energy storage system (BESS) to mitigate the intermittency and maintain grid resilience. However, it is a high-cost solution considering the battery management system (BMS), lifetime, maintenance, and recycling. Active power reserve control recently draws research attention to remove or minimize the usage of BESS. This paper proposed a novel PRRC scheme, free of the utilization of BESS or solar irradiance forecasting. The development is based on the in-depth analysis of PV output characteristics and real-world solar irradiance data. It shows an improvement regarding energy harvest efficiency, ramp rate control success rate and transient performance in comparison to the prior works. The advantages are clearly demonstrated by both the simulation and experiment test.

In this article, a dual deep neural network (DDNN) based cyber-attack detection and correction method for direct current microgrids (DCMG) are proposed. DCMG are prone to cyber-attacks through their sensors and communication links. The injection of false data packets in the cyber layer can disrupt the control objectives, leading to voltage instability and load sharing patterns. Therefore, detection and correction of malicious data is essential for the DC microgrid stability. In this article, a DDNN is designed with prediction and correction networks. The prediction network composed with one input layer, two hidden layers and one output layer. This network predicts the converter’s duty by considering the input features as DC bus voltage and the reference voltage. The correction network also composed with one input layer, two hidden layers and one output layer. This network provides the duty corresponding to the attack by considering the input features as DC bus voltage, battery voltage and reference voltage. The output from the prediction and correction network are implanted to detect and correct the false data injection (FDI) attacks. However, for the training purpose, the data is collected by performing the various attack scenarios who is able to inject the false data and disrupt the stable operation of the system. The data is then used to train a neural network to detect a larger set of FDI attacks. The proposed scheme’s effectiveness is verified by conducting the real-time experiments for various attack scenarios and its results are explored.

Aiming at the optimization of the power density, a parameter design method is proposed for the capacitive power transfer (CPT) system. To reduce the volume of compensation inductors, magnetic-core inductors are used to replace air-core inductors. That is, the constraints of the power density consist of the air breakdown voltage limitation and the magnetic-core loss limitation. To quantify the impact of the magnetic-core loss on the power density, the current stress and quality factor of magnetic-core inductors are discussed. Taking the power density as the main optimization objective, a parameter design method is proposed in this article. Since there exist several design points for power density optimization, efficiency is chosen as the auxiliary optimization objective to realize the parameter design. Thus, the proposed design method can optimize not only the power density but also efficiency. To verify the effectiveness, a 100W output CPT system is built in this article. The experimental results show that the power density of the CPT system can reach 4.08W/in3, and the peak DC-DC efficiency can reach 92.06%.