The Nested Neutral Point Clamped (NNPC) converter, functioning as a voltage source Converter (VSC), provides an effective solution for applications requiring Medium-Voltage and High-Power (MVHP). Earlier implementations of this converter typically required many sensors to maintain capacitor voltages equally in all series Half-Bridge Sub-Modules (HB-SMs). This research introduces two methodologies to minimize sensor requirements for computing capacitor voltages using novel algorithms based on estimation methods. These strategies simplify the converter control procedure by eliminating individual current and voltage sensors specified for HB-SMs. The proposed approaches ensure precise voltage balance with minimal estimation error by continuously adjusting capacitor voltages using estimated values from previous iterations and switching signals. Extensive MATLAB Simulink tests verify the effectiveness of these techniques across diverse practical scenarios. The results highlight significant simplification of sensor complexity while maintaining strong performance in NNPC Converter applications, emphasizing the importance of sensor implementation in the cost and operational effectiveness of VSCs.

A promising solution for operations in the moderate voltage and high-power ranges, the Alternate Arm Multilevel Converter (AAMC) distinguishes itself with a distinctive topology as an AC-DC Voltage Source Converter (VSC). However, traditional implementations require numerous sensors to maintain capacitor voltage balancing across series Full-Bridge Sub-Modules (FB-SBs). Introducing an innovative approach to enhance AAMC efficiency, this study proposes two sensor reduction methods for calculating capacitor voltages with estimation algorithms, demonstrating the current paths within each method. These streamlined approaches simplify the control structure and eliminate the requirement for individual current sensors in each phase and voltage sensors per FB-SBs. By continuously adjusting capacitor voltages based on estimated values used by data from executed last steps and switching signals, the provided methods achieve precise voltage balance with minimal estimation error. The methods’ effectiveness is validated through extensive simulations achieved by MATLAB Simulink software across various operational conditions. The results demonstrate notable reductions in measurement component requirements while maintaining robust performance in AAMC applications. This research highlights the potential of optimizing sensor utilization to improve the performance of voltage source converter technologies in the future.

In this paper, first, a model predictive control (MPC) for a single-phase current source rectifier (CSR) is developed and used in electric vehicle (EV) charger structures. Due to the uncertainties in its model caused by varying points of connection to the power grid, a disturbance estimator is necessary. In addition to the uncertainties, the CSR model's precision is diminished by the rectifier output's unmodeled dynamics. The disturbance estimator with Lagrange extrapolation yields a more precise model of the single-phase CSR for MPC. Finite control set model predictive control (FCS-MPC) is employed to minimize switching losses by providing an optimal control input, eliminating the need for a modulator due to the limited number of allowable switching modes. The proposed control method is simulated in MATLAB software and implemented on a 4-kW laboratory prototype. Simulation and experimental results confirm the validity of the proposed control method.

Accessibility to sustainable and reliable electrical energy for remote area power supply (RAPS) systems under high penetration levels of wind energy requires adopting appropriate technologies and controllers. In recent years, energy storage has gained significant attention for smoothing inherent voltage and power fluctuations of standalone systems. This paper presents a filtration-based fractional-order PI (FOPI) controller optimized by Gases Brownian Motion Optimization (GBMO) algorithm to manage an active battery-supercapacitor hybrid energy storage system (HESS) in a wind-dominated RAPS system. The GBMO algorithm has a high accuracy and convergence rate among optimization algorithms introduced in recent years. Moreover, a fuzzy logic controller (FLC) guarantees the wind turbine generator's maximum power point tracking (MPPT) at any wind condition while mitigating its fluctuating torque. The proposed RAPS system uses the advantages of the FOPI controller, the GBMO optimization algorithm, the FLC-based MPPT algorithm, and a high-pass filter to manage precise coordination between different components of the RAPS system. The high-pass filter decouples high and low-frequency voltage oscillations to modify the HESS performance and relieve battery stress, thereby extending its lifespan. The proposed controller's performance and robustness are verified in comparison with an optimal PI controller based on GBMO under turbulent wind speed and load step changes in a detailed model of the system. The results demonstrate the superiority of the optimal FOPI controller over the optimal PI controller.