Novel MPPT Controller Augmented with Neural Network for Use with Photovoltaic Systems Experiencing Rapid Solar Radiation Changes

Ahmad Dawahdeh (), Hussein Sharadga and Sunil Kumar
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Ahmad Dawahdeh: Department of Mechanical Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
Hussein Sharadga: Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA
Sunil Kumar: Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA

Sustainability, 2024, vol. 16, issue 3, 1-22

Abstract: A maximum power point tracking (MPPT) controller optimizes power harvesting in photovoltaic (PV) systems under varying conditions. The perturb and observation (P&O) algorithm is commonly used for MPP tracking, but suffers from slow response, loss of tracking direction, and entrapment. The current research proposes a neural network (NN) integrated with the P&O algorithm to enhance tracking performance during sudden variations in solar irradiance. The proposed neural network updates the duty cycle change when detecting sudden changes. It effectively estimates the duty cycle change even when trained with a small dataset. The integration between the NN and P&O significantly improves tracking performance compared with the conventional P&O algorithm, especially under sudden irradiance changes.

Keywords: MPPT controller; perturb and observation; PV system; neural network; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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