Jingang Han

Marine current turbines (MCTs) are frequently affected by variations in marine current velocity, swell disturbances, and load torque fluctuations. These disturbances can cause rapid changes in the operating state of the permanent magnet synchronous generator (PMSG). Therefore, fast rotor position estimation is crucial for the sensorless control of PMSG‐based marine current power generation systems. Finite‐position‐set phase‐locked loop (FPS‐PLL) has attracted increasing attention in such systems due to its excellent dynamic response. However, it suffers from a high computational burden. To address this problem, an inverse quadratic interpolation FPS‐PLL (IQI‐FPS‐PLL) is proposed in this paper. In this method, a half‐angle octant method is developed. The rotor position domain is divided into eight sectors. Then, accurate rotor position estimation can be achieved with only one IQI calculation. Finally, the proposed IQI‐FPS‐PLL is implemented and validated on both a MATLAB/Simulink simulation platform and an 11‐kW PMSG experimental platform. The results verify the effectiveness of the proposed method.

The asymmetric cascaded H‐bridge (ACHB) inverter is capable of generating a greater number of output voltage levels under the same number of modules compared to conventional topologies. However, when applied to such multilevel systems, conventional finite control set model predictive control (FCS‐MPC) tends to exhibit undesirable harmonic components and chaotic switching frequency variation. Aiming at the inherent defects of excessive computational burden and poor steady‐state accuracy faced by FCS‐MPC in real‐time applications, this paper proposes a virtual voltage vector MPC strategy based on a sampled‐data model. By replacing the exponential matrix in the discretized sampled‐data model with a polynomial approximation, it enables more efficient real‐time execution. Finally, the proposed control strategy is implemented to a single‐phase nine‐level ACHB inverter. Both simulation and experimental results demonstrate the superior performance of the proposed algorithm under steady and dynamic states.

The hybrid energy storage system (HESS) significantly improves the dynamic response and energy utilization efficiency of the propulsion system in fuel cell vessels while maintaining the stability of the power grid. To address the issue of inaccurate power allocation and unreasonable capacity configuration caused by modal aliasing during power decomposition, this article innovatively proposes a power distribution method for hybrid energy storage systems. First, the Multi-Verse Optimizer (MVO) is used to optimize Variational Mode Decomposition (VMD) in order to address the issue of VMD being highly dependent on parameter selection. Then, power is decomposed twice to resolve the modal aliasing problem associated with single decomposition, achieving a more accurate power breakdown and providing a more stable power output. Finally, the decomposed powers are cross-allocated: low frequencies are assigned to lithium batteries that can provide long-term stable energy supply, while high frequencies are allocated to supercapacitors capable of delivering short-term efficient energy supply. The simulation results indicate that the MVO-CVMD method proposed in this paper effectively addresses the modal aliasing problem, enhances the accuracy of power decomposition, and reduces the cost of capacity configuration.

The modular multilevel converter (MMC) has attracted significant attention in the fields of high and medium‐voltage applications in recent years. Traditional indirect finite control set model predictive control (IFCS‐MPC) strategies for MMC require voltage measurements from each submodule (SM) for the voltage control loop. As a result, a considerable number of sensors is required to achieve energy balancing, leading to increased circuit complexity and higher hardware costs. This paper proposes an IFCS‐MPC method for MMC with a reduced number of sensors approach. In the approach, an SM capacitor voltage observer is designed based on the discrete‐time model of capacitor voltage, utilizing the measured arm current and SM switching state. Only one SM voltage sensor is needed per arm, which is used to correct the observed voltage errors caused by the discrete‐time model and the accuracy of the parameters. Subsequently, the observed values of the SM capacitor voltages are substituted for the measured values in the voltage sorting algorithm of IFCS‐MPC to reduce voltage sensors. The simulation and experiment results are carried out to demonstrate the effectiveness of the proposed control system and the feasibility of reducing the number of sensors.

The model predictive control (MPC) method is widely applied in modular multilevel converter (MMC) control due to its multiple‐object inclusion and fast response. But there still some challenges that exist such as high computational burden caused by the large number of submodule (SM), weighting factors tuning for various control object in the cost function and requirement of short sampling times. To solve these challenges, a virtual voltage vector modulated model predictive control (VSVV‐M2PC) scheme for the control of the MMC is proposed. This scheme enables the MMC to output dual voltage levels, which improves the current tracking accuracy. Additional circulating current suppression (CCS) method and sorting‐based modulator are designed, which reduce the computational burden, achieve fixed switching frequency, and avoid the weighting factor tuning process. Finally, the proposed scheme is validated by experimental results.