Enhanced maximum power point estimation algorithm using quantum particle swarm optimization for solar photovoltaic micro inverter systems

Photovoltaic systems are increasingly integrated into distributed energy networks, demanding compact and efficient inverter solutions that can maintain stable operation under variable environmental conditions. To address this, a single-stage micro-inverter architecture is developed using a quantum-behaved particle swarm optimization algorithm for enhanced maximum power point tracking. The motivation lies in improving the convergence speed and tracking accuracy over conventional methods while minimizing system complexity. The hypothesis is that embedding a probabilistic optimization algorithm within the control loop can improve adaptability to irradiance fluctuations and achieve better regulatory compliance. The proposed design employs a flyback converter topology that combines voltage step-up, galvanic isolation, and direct current to alternating current inversion into a unified platform. The control strategy integrates a quantum-behaved particle swarm optimization-based maximum power point estimation mechanism and a proportional-integral controller, both validated through simulation and real-time hardware testing using a programmable controller and solar simulator. The quantitative results show the system achieves maximum power tracking efficiency of 95–98 percent, with voltage and current total harmonic distortion levels of 3.6 percent and 4.9 percent, respectively, meeting grid compliance requirements. The proposed solution ensures system stability with a phase margin of 58.7 degrees and responds well under dynamically varying irradiance conditions. This work demonstrates that integrating intelligent control with a compact flyback-based architecture results in a reliable, efficient, and grid-compliant solar micro-inverter suitable for low-power residential and standalone solar applications.