Implementation of Genetic Algorithm to generate Backstepping Controller’s gains for MPPT of Partially Shaded Photovoltaic Panels

Abstract

The ability for the solar panel to operate at its maximum power point makes tracking the maximum power point of the photovoltaic model more important nowadays. The MPP tracking becomes more difficult and challenging during changes in weather conditions, which have a significant impact on PV power. In this paper a new method has been used to track the (GMPP) that combines (GA) with (BSC). The GA is used to generate the reference voltage that harmonize to the GMPP and the optimum BSC gains (and ). While the BSC operates the SEPIC duty cycle in order to follow the reference voltage found by the GA. The proposed method is compared to other methods INC-BSC, P&O-BSC, GA-BSC and PSO-BSC using different BSC gains. According to the results, the suggested method succeeded in tracking the GMPP, while in some instances, the other MPPT methods tend to focus on the LMPP, which causes power losses. Additionally, using GA to generate the optimal BSC gains help to reduce oscillations around the GMPP. It helps minimize the voltage error by four times than the standard GA-BSC method and lead to fast tracking, two times faster than the standard GA-BSC method, and four times faster than PSO-BSC method. Furthermore, the findings showed that under the partial shadow condition, the performance criteria of the recommended technique outperform those of the PSO-BSC technique tracking.