Optimization of Bidirectional Converters and BLDC Motor Control in Microgrids Using Priority Adaptive Particle Optimization Driven Boosting Model

Abstract

With renewable energy sources integrated seamlessly into modern power grids, the control strategies should be proficient to optimize energy flow and ensure system stability. Thus, grid-connected microgrids play an important role in this transition by balancing generation and consumption using sophisticated optimization techniques. This paper presents a Priority Adaptive Particle Optimization (PAPO) framework with Gradient Boosting Decision Trees (GBDT) to efficiently control bidirectional power converters in microgrids. The PAPO algorithm dynamically adjusts the real-time control parameters to optimize the efficiency of Brushless DC (BLDC) motors in response to variations in loads and environmental conditions. Furthermore, high-gain converters are used for better integration of RESs such as solar and wind with less switching losses and improved energy conversion efficiency. Energy management, considering the synergy of PAPO-GBDT, accurately models both demand on the load side and generation on the supply side. The proposed framework was implemented in MATLAB/Simulink using conventional optimization techniques for validation. The results presented better performance in terms of minimized power losses, energy efficiency, and stable operation of the microgrid. The PAPO-GBDT framework showed a 15% decrease in energy losses and a 10% improvement in overall energy conversion efficiency compared to traditional optimization methods. The system also ensured stable microgrid operation with a 98% reliability rate under changing environmental conditions.