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Machine Learning Based Maximum Power Prediction for Photovoltaic System

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Field Value
 
Title Machine Learning Based Maximum Power Prediction for Photovoltaic System
 
Creator Agarwala, Anshul
Kumar, Nitish
Dubey, Pawan
 
Subject Supervised machine learning
Data driven modeling
Boost converter
MPPT (Maximum power point tracking)
 
Description 892-898
This manuscript proposes a data-driven machine learning algorithm to track maximum power for PV (photovoltaic)
panel systems. Data from the PV panel system connected to a boost converter has been collected. PV Voltage, current,
temperature, irradiance, PI and power value have been collected for the supervised machine learning-based modeling.
Where PV Voltage, PV current, temperature, and irradiance are the predictors, and PI (proportional integral) is the response
of the machine learning-based model. The proposed system becomes more efficient with time while existing MPPT
(maximum power point tracking) work on a specific logic for whole life. The model efficacy has been analyzed based on
accuracy, scattering plot, and ROC (receiver operating characteristics) curve.
 
Date 2022-10-07T11:43:55Z
2022-10-07T11:43:55Z
2022-10
 
Type Article
 
Identifier 0975-0959 (Online); 0301-1208 (Print)
http://nopr.niscpr.res.in/handle/123456789/60662
https://doi.org/10.56042/ijpap.v60i10.62197
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source IJPAP Vol.60(10) [Oct 2022]