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Performance assessment of photovoltaic modules based on daily energy generation estimation

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  • Wang, Jing-Yi
  • Qian, Zheng
  • Zareipour, Hamidreza
  • Wood, David

Abstract

Performance assessment can improve photovoltaic (PV) plant economics by identifying the need for timely corrective actions. Performance assessment of PV plants is usually based on the comparison between measured and modeled outputs of PV plants, i.e., an alarm occurs for abnormal operation when a significant difference is detected. For such methods, it is critical to estimate the potential electricity generation of PV plants in normal operation. However, unpredictable conditions affecting solar modules pose challenges to develop reliable PV models. In this paper, a practical approach to improve estimating daily energy generation of PV plants for performance assessment is proposed, which includes two main components: (i) a data preprocessing method; (ii) sub-models in different weather conditions. The proposed data preprocessing method detects outliers by comparing normalized outputs of adjacent inverters instantaneously. It is robust against erroneous measurements in normal operation. Sub-models in different weather conditions are developed using a Principal Component Analysis and Support Vector Machine method for better representation of PV plant outputs. Results show that the proposed method can detect deviations between the estimated and the measured daily energy generation of 10%. Moreover, false alarms, i.e., an abnormal point identified while the system is operating normally, are significantly reduced.

Suggested Citation

  • Wang, Jing-Yi & Qian, Zheng & Zareipour, Hamidreza & Wood, David, 2018. "Performance assessment of photovoltaic modules based on daily energy generation estimation," Energy, Elsevier, vol. 165(PB), pages 1160-1172.
    • Handle: RePEc:eee:energy:v:165:y:2018:i:pb:p:1160-1172
    DOI: 10.1016/j.energy.2018.10.047
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    1. Jun-Hyun Shin & Jin-O Kim, 2020. "On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant," Energies, MDPI, vol. 13(17), pages 1-12, September.
    2. Jun-Hyeok Kim & Jong-Man Joung & Byung-Sung Lee, 2022. "A Study on the Preprocessing Method for Power System Applications Based on Polynomial and Standard Patterns," Energies, MDPI, vol. 15(4), pages 1-12, February.
    3. Qu, Jiaqi & Qian, Zheng & Pei, Yan & Wei, Lu & Zareipour, Hamidreza & Sun, Qiang, 2022. "An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection," Applied Energy, Elsevier, vol. 319(C).
    4. Tosin Waidi Olofin & Omowunmi Mary Longe & Tien-Chien Jen, 2023. "Analysis of Performance Yield Parameters for Selected Polycrystalline Solar Panel Brands in South Africa," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
    5. Jingyue Wang & Zheng Qian & Jingyi Wang & Yan Pei, 2020. "Hour-Ahead Photovoltaic Power Forecasting Using an Analog Plus Neural Network Ensemble Method," Energies, MDPI, vol. 13(12), pages 1-17, June.
    6. Eduardo Quiles & Carlos Roldán-Blay & Guillermo Escrivá-Escrivá & Carlos Roldán-Porta, 2020. "Accurate Sizing of Residential Stand-Alone Photovoltaic Systems Considering System Reliability," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    7. Carlos Roldán-Porta & Carlos Roldán-Blay & Guillermo Escrivá-Escrivá & Eduardo Quiles, 2019. "Improving the Sustainability of Self-Consumption with Cooperative DC Microgrids," Sustainability, MDPI, vol. 11(19), pages 1-22, October.

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