研究目的
To propose and validate an improved wind driven optimization (IWDO) algorithm for identifying the nine unknown parameters in a triple-diode photovoltaic cell model, enhancing the accuracy and convergence speed of parameter identification.
研究成果
The IWDO algorithm significantly improves the accuracy and convergence speed of parameter identification in triple-diode photovoltaic cell models, outperforming existing algorithms in terms of nRMSE, MAPE, R2, and convergence speed. It effectively balances exploration and exploitation, making it suitable for practical applications under various operating conditions.
研究不足
The study focuses on three specific photovoltaic technologies and uses data from a single location, which may limit the generalizability of the findings to other technologies or geographical areas.
1:Experimental Design and Method Selection:
The IWDO algorithm combines the mutation strategy of the differential evolution algorithm with the covariance matrix adaptation evolution strategy of the wind driven optimization algorithm to improve exploration and exploitation balance.
2:Sample Selection and Data Sources:
Actual recorded data based on 15-minute intervals for 3 years from three commercial photovoltaic technologies (mono-crystalline, poly-crystalline, and thin-film) were used.
3:List of Experimental Equipment and Materials:
Standard PC with a 3.4 GHz Intel(R) Core (TM) i7-6700 CPU and 16 GB of RAM, MATLAB 2019b environment.
4:4 GHz Intel(R) Core (TM) i7-6700 CPU and 16 GB of RAM, MATLAB 2019b environment.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: The IWDO algorithm was applied to identify the unknown parameters from each I-V curve, with results validated against actual data.
5:Data Analysis Methods:
Performance was evaluated using root-mean-square-error (RMSE), normalized root-mean-square error (nRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2).
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