| Autor: |
Joseph Chakar, Marko Pavlov, Yvan Bonnassieux, Jordi Badosa |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Energy Conversion and Management: X, Vol 15, Iss , Pp 100270- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2590-1745 |
| DOI: |
10.1016/j.ecmx.2022.100270 |
| Popis: |
Practical but accurate methods that can assess the performance of photovoltaic (PV) systems are essential to all stakeholders in the field. This study proposes a simple approach to extract the solar cell parameters and degradation rates of a PV system from commoditized power generation and weather data. Specifically, the teaching-learning-based optimization algorithm was used to estimate the single-diode model parameters of a monocrystalline silicon PV module from a handful of power production data points that capture the operating current and voltage under real working temperatures and irradiance levels. These parameters can reproduce the solar panel’s actual behavior under all operating conditions and provide insights into its underlying degradation mechanisms. The results were validated by site measurements as well as a sensitivity analysis, thus offering exciting possibilities for the future of PV performance analysis, power forecasting, and remote fault detection for real-life applications. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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