Saeed Yousofi Darmian’s research while affiliated with University of Sistan and Baluchestan and other places

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.

The modular multilevel converter (MMC) is a favored topology in the industry, but its reliability is at risk with an increase in the number of sub-modules (SMs) due to a rise in switching components. The essential need for maintaining capacitor voltage balance in each arm leads to increased complexity and cost, as numerous voltage sensors are required. This study introduces an innovative approach to minimize the number of voltage sensors by employing an enhanced algorithm for open-circuit fault detection in switches. The proposed scheme organizes each arm into groups, each containing two SMs and one voltage sensor, aiming to reduce the overall sensor count. A novel fault detection mechanism is presented, identifying open-circuit faults by comparing group output voltages in healthy and defective conditions. The capacitor voltage estimation algorithm in the sensor reduction scheme is noted for its simplicity compared to other methods. The effectiveness of these methods is validated through simulations and experimental implementations across diverse scenarios, affirming their reliability.