Selective harmonic elimination (SHE) technique is used in power inverters to eliminate specific lower-order harmonics by determining optimum switching angles that are used to generate Pulse Width Modulation (PWM) signals for multilevel inverter (MLI) switches. Various optimization algorithms have been developed to determine the optimum switching angles. However, these techniques are still trapped in local optima. This study proposes an opposition-based quantum bat algorithm (OQBA) to determine these optimum switching angles. This algorithm is formulated by utilizing habitual characteristics of bats. It has advanced learning ability that can effectively remove lower-order harmonics from the output voltage of MLI. It can eventually increase the quality of the output voltage along with the efficiency of the MLI. The performance of the algorithm is evaluated with three different case studies involving 7, 11, and 17-level three-phase MLIs. The results are verified using both simulation and experimental studies. The results showed substantial improvement and superiority compared to other available algorithms both in terms of the harmonics reduction of harmonics and finding the correct solutions.

High-gain DC–DC converters are being drastically utilized in renewable energy generation systems, such as photovoltaic (PV) and fuel cells (FC). Renewable energy sources (RES) persist with low-level output voltage; therefore, high-gain DC–DC converters are essentially integrated with RES for satisfactory performance. This paper proposes a non-isolated high-gain non-inverting interleaved DC–DC boost converter. The proposed DC–DC converter topology is comprised of two inductors and these are charging and discharging in series and parallel circuit configurations. The voltage multiplier technique is being utilized to produce high gain. The proposed topology is designed to operate in three modes of operation. Three switches are operated utilizing two distinct duty ratios to avoid the extreme duty ratio while having high voltage gain. Owing to its intelligent design, the voltage stress on the switches is also significantly reduced where the maximum stress is only 50% of the output voltage. The proposed converter’s steady-state analysis with two distinct duty ratios is thoroughly explored. Furthermore, a 160 W 20/400 V prototype is developed for performance analysis and validation. The converter topology can generate output voltage with a very high voltage gain of 20, which is verified by the prototype. Moreover, a high efficiency of 93.2% is attained by the proposed converter’s hardware prototype.

Fuel cells have drawn a lot of interest in recent years as one of the most promising alternative green power sources in microgrid systems. The operating conditions and the integrated components greatly impact the quality of the fuel cell’s voltage. Energy management techniques are required in this regard to regulate the fuel cell’s power in a microgrid. The active/reactive power in the microgrid should be adjusted in line with US Energy Star’s regulations whereas the grid current needs to follow the standard set by IEEE 519 2014 to enhance the power quality of the electrical energy injected into the microgrid. Uncontrolled energy injection from the fuel cell can have serious impacts including superfluous energy demand, overloading, and power losses, especially in high power and medium voltage systems. Although fuel cells have many advantages, they cannot yet produce high voltages individually to compensate for the demand of a microgrid system. Due to these reasons, the fuel cell must be interfaced with a DC-DC converter. This research proposes a novel high voltage gain converter integrated 1.26 kW fuel cell for microgrid power management that can boost the fuel cell’s voltage up to 20 times. Due to this high voltage gain, the voltage and current ripple of the fuel cell is also reduced substantially. According to the analysis, the proposed converter demonstrated optimal performance when compared to the other converters due to its high voltage gain and extremely low voltage ripple. As a result, the harmonic profile of the microgrid current persists with a reduced THD of 3.22% and a very low voltage ripple of 4 V. To validate the converter’s performance, along with extensive simulation, a hardware prototype was also built. The voltage of the fuel cell is regulated using a simplified proportional integral controller. The operating principle of the converter integrated fuel cell along with its application in microgrid power management is demonstrated. A comparative analysis is also shown to verify how the proposed converter is improving the system’s performance when compared against other converters.

This study introduces a novel two-stage hybrid optimization framework aimed at enhancing the sustainability of 90-degree slot milling operations on AZ31 magnesium alloy. The framework systematically combines the Taguchi method for experimental design, stepwise regression for predictive modeling, nondominated sorting genetic algorithm II for multiobjective optimization, analytic hierarchy process for weighting sustainability indicators, and gray relational analysis for final solution selection. In the first stage, economic and environmental objectives are optimized independently. In the second stage, these objectives are consolidated using weights derived through the analytic hierarchy process and reoptimized using the nondominated sorting genetic algorithm II. Gray relational analysis is employed to identify the most balanced solution from the Pareto optimal set. The developed regression models demonstrate high predictive accuracy, and experimental validation confirms the practical effectiveness of the framework. Results show a 78% improvement in economic and a 90% improvement in environmental sustainability compared to baseline conditions. The framework’s modular structure and adaptability to other machining processes make it a valuable tool for promoting sustainable manufacturing practices.