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<strong>Machine Learning Based Maximum Power Prediction for Photovoltaic System</strong>

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Authentication Code dc
 
Title Statement <strong>Machine Learning Based Maximum Power Prediction for Photovoltaic System</strong>
 
Added Entry - Uncontrolled Name Agarwal, Anshul ; National Institute of Technology Delhi
Kumar, Nitesh ; National Institute of Technology Delhi
Dubey, Pawan ; Madhav Institute of Technology & Science Institute in Gwalior, Madhya Pradesh
NA
 
Uncontrolled Index Term DC-DC Converter; Machine Learning; Renewable Energy
Supervised machine learning; Data driven modeling; Boost converter; MPPT (Maximum power point tracking)
 
Summary, etc. 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.
 
Publication, Distribution, Etc. Indian Journal of Pure & Applied Physics (IJPAP)
2022-10-07 16:30:27
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/IJPAP/article/view/62197
 
Data Source Entry Indian Journal of Pure & Applied Physics (IJPAP); ##issue.vol## 60, ##issue.no## 10 (2022): Indian Journal of Pure & Applied Physics
 
Language Note en
 
Nonspecific Relationship Entry http://op.niscair.res.in/index.php/IJPAP/article/download/62197/465605846
http://op.niscair.res.in/index.php/IJPAP/article/download/62197/465605848
http://op.niscair.res.in/index.php/IJPAP/article/download/62197/465605851
 
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