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<p>This paper investigates the effectiveness of extrapolation and interpolation methods for evaluating photovoltaic (PV) module performance under real-world outdoor conditions, with particular emphasis on Procedure 3 of the IEC 60891:2021 standard. It aims to address the gap between measured and actual PV module performance by examining the correlation between these measurements. A novel methodology is proposed to determine suitable irradiance differences, using two legacy PV modules with maximum powers of 150 Wp and 260 Wp under standard test conditions (STC). The approach integrates optimization techniques with probabilistic neural networks (PNN).</p><p>Experimental results reveal an average uncertainty of ± 2.23% and ± 2.39% for the legacy PV modules, with Root Mean Square Errors (RMSEs) ranging from 0 to 1%, confirming the effectiveness of extrapolation and interpolation methods in predicting PV module performance and degradation across varying environmental conditions. The paper provides a detailed analysis of solar irradiance selection during current–voltage (I-V) curve translation and offers a comprehensive difference analysis between the methods. Clear justifications for the methodologies applied enhance the paper’s utility for researchers and practitioners in the PV systems field. Additionally, the advantages, limitations, opportunities, and challenges associated with the proposed methodology are discussed, offering valuable insights for future research and practical applications.</p>

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Optimizing Legacy PV Module Performance: Advanced Correction Techniques for Efficiency Enhancement Under IEC 60891:2021 Procedure 3

  • 期刊论文

  • 发表日期：

    2025年11月7日

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  • Amina Chahtou,
  • El Amin Kouadri Boudjelthia,
  • Nasreddine Belhaouas,
  • Hichem Hafdaoui,
  • Djohra Saheb-Koussa

摘要

This paper investigates the effectiveness of extrapolation and interpolation methods for evaluating photovoltaic (PV) module performance under real-world outdoor conditions, with particular emphasis on Procedure 3 of the IEC 60891:2021 standard. It aims to address the gap between measured and actual PV module performance by examining the correlation between these measurements. A novel methodology is proposed to determine suitable irradiance differences, using two legacy PV modules with maximum powers of 150 Wp and 260 Wp under standard test conditions (STC). The approach integrates optimization techniques with probabilistic neural networks (PNN).

Experimental results reveal an average uncertainty of ± 2.23% and ± 2.39% for the legacy PV modules, with Root Mean Square Errors (RMSEs) ranging from 0 to 1%, confirming the effectiveness of extrapolation and interpolation methods in predicting PV module performance and degradation across varying environmental conditions. The paper provides a detailed analysis of solar irradiance selection during current–voltage (I-V) curve translation and offers a comprehensive difference analysis between the methods. Clear justifications for the methodologies applied enhance the paper’s utility for researchers and practitioners in the PV systems field. Additionally, the advantages, limitations, opportunities, and challenges associated with the proposed methodology are discussed, offering valuable insights for future research and practical applications.