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Photovoltaic Array Fault Diagnosis and Localization Method Based on Modulated Photocurrent and Machine Learning.

Yebo Tao1, Tingting Yu2, Jiayi Yang3,4

  • 1College of Intelligent Manufacturing, Jiaxing Vocational & Technical College, Jiaxing 314036, China.

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|January 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for diagnosing photovoltaic (PV) array faults using modulated photocurrent and machine learning. It achieves high-speed, accurate fault identification and localization with low-cost equipment.

Keywords:
artificial intelligencefault diagnosisfault locationmachine learningphotovoltaic power systemssolar energy

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Area of Science:

  • Renewable Energy Systems
  • Electrical Engineering
  • Materials Science

Background:

  • Photovoltaic (PV) arrays degrade over time due to outdoor exposure, leading to various faults.
  • Effective fault diagnosis systems are crucial for PV array reliability and performance.
  • Current methods often compromise between diagnostic accuracy and fault localization capabilities.

Purpose of the Study:

  • To develop a fault identification and localization approach for PV arrays.
  • To overcome the limitations of existing methods by achieving both high accuracy and precise localization.
  • To enable rapid and cost-effective PV array fault detection.

Main Methods:

  • Utilizing modulated photocurrent and machine learning for fault diagnosis.
  • Employing frequency-modulated light to separate photocurrent and measure individual panel efficiency.
  • Applying machine learning classification algorithms to analyze current amplitude and frequency for fault identification.

Main Results:

  • Achieved high-speed (5800 obs/s) and high-accuracy (97.8%) fault identification and localization using a neural network algorithm.
  • Demonstrated the method's effectiveness through practical experimentation.
  • Confirmed the ability to identify faults by measuring only the short-circuit current.

Conclusions:

  • The proposed modulated photocurrent and machine learning method offers a superior solution for PV array fault diagnosis.
  • This approach provides a practical, low-cost, high-speed, and highly accurate system for fault identification and localization.
  • The findings support the widespread adoption of this technique for enhancing PV system reliability.