Maximum Power Point Tracking (MPPT) algorithm performs for maximizing the efficiency of solar Photo Voltaic (PV) system. The solar photovoltaic system efficiency reduces due to partial shading and ambient atmospheric condition, which varies with geographic locations. Traditional MPPT systems solve the above problem through different soft computing algorithms such as Perturb and observe (P&O), Flower pollination algorithm (FPA) and Particle swarm optimization (PSO). In P&O, FPA and PSO algorithms, duty cycle of boost converter varies to attain MPPT. The soft computing algorithms in MPPT perform less during the partial shading effect or rapid insolation, fluctuation condition of solar energy. The performance of MPPT with traditional algorithms is reduced due to slow convergence speed and oscillations in tracking by computing algorithms. In this paper, Regression controller based MPPT achieve maximum peak voltage during partial shading effect is developed. The regression controller predicts the duty cycle for boost converter based on stored dataset of PV system output voltage and load, during partial shading effect or rapid isolation for that particular geographic location. The regression based duty cycle prediction controller is programmed in MATLAB R2018a Simulink. Furthermore, Regression controller is implemented in PV system test bed. The simulation and hardware results of Regression controller based MPPT perform more of about 20%, 16.96% and 15% in efficiency respectively than PSO, FPA and P&O algorithms during partial shading condition in PV.

最大功率点跟踪（MPPT）算法可以最大程度地提高太阳能光伏（PV）系统的效率。太阳能光电系统的效率由于部分阴影和周围大气条件而降低，这随地理位置而变化。传统的MPPT系统通过不同的软计算算法（例如，扰动和观测（P＆O），花授粉算法（FPA）和粒子群优化（PSO））解决了上述问题。在P＆O，FPA和PSO算法中，升压转换器的占空比变化以达到MPPT。MPPT中的软计算算法在部分阴影效应或太阳能的快速日晒，波动条件下表现不佳。由于收敛速度慢和计算算法在跟踪中的振荡，传统算法的MPPT性能降低。在本文中，基于MPPT的回归控制器在部分阴影效应期间实现了最大峰值电压。回归控制器基于所存储的PV系统输出电压和负载的数据集，在特定阴影区域的局部阴影效应或快速隔离期间，预测升压转换器的占空比。基于回归的占空比预测控制器在MATLAB R2018a Simulink中编程。此外，回归控制器在光伏系统测试台中实现。在PV的部分阴影条件下，基于回归控制器的MPPT的仿真和硬件结果分别比PSO，FPA和P＆O算法的效率高出约20％，16.96％和15％。回归控制器基于所存储的PV系统输出电压和负载的数据集，在特定阴影区域的局部阴影效应或快速隔离期间，预测升压转换器的占空比。基于回归的占空比预测控制器在MATLAB R2018a Simulink中编程。此外，回归控制器在光伏系统测试台中实现。在PV的部分阴影条件下，基于回归控制器的MPPT的仿真和硬件结果分别比PSO，FPA和P＆O算法的效率高出约20％，16.96％和15％。回归控制器基于所存储的PV系统输出电压和负载的数据集，在特定阴影区域的局部阴影效应或快速隔离期间，预测升压转换器的占空比。基于回归的占空比预测控制器在MATLAB R2018a Simulink中编程。此外，回归控制器在光伏系统测试台中实现。在PV的部分阴影条件下，基于回归控制器的MPPT的仿真和硬件结果分别比PSO，FPA和P＆O算法的效率高出约20％，16.96％和15％。基于回归的占空比预测控制器在MATLAB R2018a Simulink中编程。此外，回归控制器在光伏系统测试台中实现。在PV的部分阴影条件下，基于回归控制器的MPPT的仿真和硬件结果分别比PSO，FPA和P＆O算法的效率高出约20％，16.96％和15％。基于回归的占空比预测控制器在MATLAB R2018a Simulink中编程。此外，回归控制器在光伏系统测试台中实现。在PV的部分阴影条件下，基于回归控制器的MPPT的仿真和硬件结果分别比PSO，FPA和P＆O算法的效率高出约20％，16.96％和15％。