Enhanced grid stability in PV systems using an improved Luo converter and neural network-based droop control

Distributed generation systems, particularly photovoltaic (PV) systems, face challenges in effective control due to their intermittent nature. These challenges include ensuring grid stability, maximizing energy production, and adapting to changing environmental conditions. Existing control strategies struggle to address these issues comprehensively. Therefore, there is a need for innovative approaches that enhance the adaptability, stability, and performance of grid-tied PV systems. Adaptability pertains to system’s capability to efficiently adjust to disparities in environmental conditions and load variations, while stability focuses on maintaining consistent and reliable system performance despite these variations. This study proposes a novel approach employing a neural network decoupler for droop control, aiming to enhance system stability and adaptability while optimizing grid voltage synchronization. This approach integrates the neural network decoupler-based droop control, improved Luo converter, and recurrent neural network-based maximum power point tracking to ensure utility-level voltage attainment in PV systems. These technologies enhance system stability and adaptability, facilitating optimal grid voltage synchronization. Comprehensive MATLAB simulation and practical testing with successful implementation of laboratory prototype validates the efficacy of presented droop controller. Analysis of the attained outcomes reveals the effectualness of the approach in maintaining a stable grid voltage, even under variable voltage from the PV system and instability caused by the load. Notably, the improved Luo converter achieves an estimated efficiency of approximately 98.6%. These outcomes underscore the practical significance and possible advancement of proposed approach to future developments in distributed generation and grid-tied PV systems.