A Modified Hybrid Maximum Power Point Tracking Method for Photovoltaic Arrays Under Partially Shading Condition

• Published in: IEEE access Vol. 7; pp. 160091 - 160100
• Main Authors: Zhang, Wei, Zhou, Guopeng, Ni, Hao, Sun, Yunlian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: ANN; Arrays; Artificial neural networks; Electric potential; Illumination; Lighting; Maximum power point trackers; Maximum power tracking; MP&O; MPPT; Photovoltaic cells; Photovoltaic systems; PSC; PV arrays; Sensor arrays; Sensors; Shading; Training; Voltage
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2950375

To ensure the photovoltaic (PV) arrays under partial shading condition(PSC) could still output maximum power quickly and efficiently, this work presents a modified hybrid maximum power point tracking (MPPT) method, which applies artificial neural network (ANN) to the modified perturb and observe (MP&O). Instead of using expensive illumination intensity sensors directly, the illumination intensity on each module in the PV array can be obtained indirectly by sampling the specific points of their own cheaper voltage-current sensors. ANN uses indirect illumination intensity to predict the optimal voltage areas of the global maximum power point (GMPP). Based on the areas, MP&O adopts a adaptive step size strategy to obtain GMPP. By modeling and simulation in Matlab/Simulink, it is shown that the tracking time and efficiency of the proposed method in this work can reach 0.026s and 99.87% respectively. Compared with other methods, the method has faster speed, higher efficiency, smaller fluctuation and lower complexity.