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Machine Learning Photovoltaic String Analyzer.

Sandy Rodrigues1,2, Gerhard Mütter3, Helena Geirinhas Ramos1

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Summary
This summary is machine-generated.

This study introduces a hybrid machine learning methodology to accurately predict photovoltaic (PV) system energy production and detect faults. The proposed method enhances energy prediction accuracy, leading to successful PV string fault detection.

Keywords:
PV faultPV stringensemble methodologyhybrid methodologymachine learning prediction models

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Area of Science:

  • Renewable Energy Systems
  • Machine Learning Applications
  • Data Science

Background:

  • Photovoltaic (PV) system energy production is inherently non-linear due to unpredictable weather conditions.
  • Accurate modeling of PV systems is crucial for effective fault detection and performance optimization.
  • Traditional methods struggle with the non-linear data generated by PV systems.

Purpose of the Study:

  • To accurately predict the DC energy output of six PV strings in a utility-scale PV system.
  • To effectively detect PV string faults by leveraging accurate energy production predictions.
  • To benchmark the performance of various machine learning methodologies for PV system analysis.

Main Methods:

  • Proposed a novel hybrid methodology combining fuzzy systems with a machine learning system.
  • Implemented and benchmarked five machine learning models: regression tree, artificial neural networks, multi-gene genetic programming, Gaussian process, and support vector machines for regression.
  • Utilized data mining and ensemble methodologies for comparison.

Main Results:

  • The proposed hybrid methodology achieved the most accurate predictions of PV string DC energy.
  • The enhanced accuracy in energy prediction directly led to successful PV string fault detection.
  • Benchmarking demonstrated the superiority of the hybrid approach over other methodologies.

Conclusions:

  • The developed hybrid methodology is highly effective for accurate PV system energy prediction.
  • Accurate energy prediction is a key enabler for reliable PV string fault detection.
  • This approach offers a significant advancement in optimizing the performance and reliability of utility-scale PV systems.