An intelligent dc current minimization method for transformerless grid-connected photovoltaic inverters.

Abstract Due to the scaling and zero-drift of current sensor errors, unbalanced grid voltages, tolerance of power switching devices, and asymmetry of PWM gate driving pulses, transformerless grid-connected inverters usually have certain amount of dc components injected to the ac grid. Therefore, power quality of the grid is degraded. Many efforts, such as using blocking capacitors, a dc current feedback control method, and a voltage dc component feedback control method, etc., have been introduced to minimize the dc injection. This paper proposes an intelligent control strategy of dc current injection suppression to the grid by utilizing adaptive-back-propagation (ABP) neural network PID controller. The performance of the proposed scheme is evaluated and compared with the traditional and existing method. Finally, the control scheme is verified on a 2-kW three-phase grid-connected inverter in the laboratory. Highlights • Analyze the sources that may lead to dc current in a grid-connected inverter. • Adaptive BP neural work based PID control is proposed to minimize the dc current. • Learning coefficients of adaptive BP-PID controller are on-line regulated. • Propose sliding window double integration method for dc current extraction. [ABSTRACT FROM AUTHOR]