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Related Experiment Video

Updated: Jan 13, 2026

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for CuIn,GaSe2 Solar Cells
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Photovoltaic Module Degradation Detection Using V-P Curve Derivatives and LSTM-Based Classification.

Chan-Ho Lee1, Sang-Kil Lim1, Sung-Jun Park2

  • 1Department of Electronic Engineering, Chosun University, Gwangju 61452, Republic of Korea.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using voltage-power curve analysis and an AI model to detect degradation in solar modules. It enables early identification of faulty solar panels and their degradation levels for improved monitoring.

Keywords:
AI-based diagnostic modelPV module degradationdegradation detectionderivativelong short-term memory (LSTM)photovoltaic systemsvoltage–power curve

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Engineering
  • Materials Science for Energy

Background:

  • Photovoltaic (PV) systems are crucial for sustainable energy but suffer performance degradation due to environmental exposure.
  • Current diagnostic methods for solar module aging lack real-time monitoring, quantitative assessment, and scalability for large power plants.
  • Degradation leads to power loss and operational issues, necessitating advanced detection techniques.

Purpose of the Study:

  • To develop a novel, real-time method for detecting and quantifying degradation in solar modules.
  • To enable early identification of the number and severity of degraded solar modules within a string.
  • To overcome the limitations of existing PV diagnostic tools.

Main Methods:

  • Utilizing the first-order derivative of the voltage-power curve to extract key degradation features.
  • Developing an AI model based on long short-term memory (LSTM) for classifying normal/abnormal states and predicting aging.
  • Designing a shallow LSTM network optimized for PV time-series data to prevent overfitting and gradient vanishing.

Main Results:

  • The proposed method effectively extracts features indicative of solar module degradation.
  • The LSTM model accurately classifies system states and predicts aging status, demonstrating learning and convergence.
  • MATLAB simulations validated the model's effectiveness and stability in training.

Conclusions:

  • The novel voltage-power curve derivative method combined with LSTM AI offers a robust solution for solar module degradation detection.
  • This approach facilitates early and accurate diagnosis of PV system health, improving operational efficiency.
  • The study provides a foundation for advanced, real-time monitoring systems for photovoltaic power plants.