A novel gene expression programming-based MPPT technique for PV micro-inverter applications under fast-changing atmospheric conditions

Abstract

The erratic behavior of the atmospheric conditions adversely affects efficient energy harvesting and the stable operation of photovoltaic systems. It is therefore critical to draw maximum power from photovoltaic modules regardless of atmospheric conditions. The maximum power point tracking techniques have crucial impacts on both efficient and stable operation of photovoltaic systems as being the controller part of the power converters. In this paper, a novel gene expression programming-based maximum power point tracking technique is proposed for micro-inverter applications under fast-changing atmospheric conditions. In this context, the main objective of this study is to improve the significant performance indices of maximum power point tracking technique including convergence speed during transients, tracking accuracy, steady-state oscillations, and rate of overshoots for ensuring the stable and efficient operation of the photovoltaic micro-inverter system. The proposed maximum power point tracking technique is integrated to a two-stage grid-connected micro-inverter system and tested in terms of the aforementioned performance parameters. The performance analyses of the developed technique are performed under various scenarios by utilizing the PSCAD/EMTDC platform. The obtained results reveal that the rate of overshoots is decreased by 0.6 A while the convergence speed is accelerated by 1.4 s. In comparison with traditional MPPT techniques, tracking accuracy, steady-state stability, and robustness of the whole system are remarkably improved along with increasing overall system efficiency by 4%. It is also worth pointing out that the complexity level of the control technique is significantly reduced by the equation obtained through the symbolic regression analysis.