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

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 curren...

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Bibliographic Details
Published in:Sustainability 2024-01, Vol.16 (3), p.1021
Main Authors: Dawahdeh, Ahmad, Sharadga, Hussein, Kumar, Sunil
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Electric power production
Energy consumption
Fuzzy logic
Neural networks
Optimization
Power
Radiation
Solar energy industry
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su16031021