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| Parameter Identification and Output Characteristics Modelling of Photovoltaic Module Based on Effective Irradiance Correction |
| Li Guorong1, Zhang Yunpeng1, Zhou Hai2, Wu Ji2, Guan Yifei3 |
1. School of Electrical Engineering Shandong University Jinan 250061 China;
2. China Electric Power Research Institute Nanjing 210003 China;
3. State Grid Shandong Electric Power Co. Ltd Jinan 250003 China |
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Abstract Parameter identification and output characteristic modelling of photovoltaic (PV) module is a complex task with several significant challenges, including recording and contribution of large amounts of data, data privacy protection anduncertainty of physical parameters of PV modules under varying operating conditions. These poses significant challenges for the efficient implementation of traditional mathematical optimization methods. Recently, many researchers have turned their attention to intelligent optimization algorithm methods, which rely on data-driven principles and exhibit strong adaptability to solve the optimal solution. However, as the prediction accuracy of PV power generation improves, more stringent demands are placed on the accuracy of PV module modeling. The traditional analytical calculation approach alone struggles to meet the heightened requirements for power prediction accuracy, as it involves solving for five parameters under changing operating conditions. To tackle these challenges, this paper introduces a new method for parameter identification and modeling of PV module output characteristics that incorporates effective irradiance correction.
First, the study analyzes environmental factors influencing the output characteristics of photovoltaic modules to establish a correction formula that converts measured irradiance to effective irradiance. This formula accounts for constant, angular, seasonal, and temperature adjustments. Second, the paper employs an analytical method to determine the single-diode model’s physical parameters under standard test conditions, fitting each parameter in the effective irradiance correction formula based on how the physical parameters vary with irradiance and temperature. The fitting process is guided by minimizing the root-mean-square error (RMSE) of the current-voltage (I-V) curve. Then, using the proposed correction method, the actual operating conditions of the PV modules are considered to adjust effective irradiance, which is then used to calculate the modules’ physical parameters and output characteristics. Finally, the method’s efficacy is confirmed using measured data: the corrected results enhance the accuracy of the I-V and power-voltage (P-V) output characteristic computations and maximum power estimation, out performing uncorrected outcomes under varied weather (sunny and cloudy), irradiance levels, and temperature ranges.
The following conclusions can be drawn from the case studies: the results show that the correction value of effective irradiance is negative in low irradiance area, and the correction value changes from negative to positive and increases monotonically with the increase of irradiance. Compared with the irradiance obtained by fitting from the measured I-V data, the effective irradiance is more consistent with fitting irradiance than measured irradiance.In different weather conditions (sunny day, cloudy day), different irradiance, temperature and season intervals, the error RMSE of the proposed effective irradiance method is less than that of the measured irradiance calculation results. And the RMSE of high irradiance (greater than 800 W/m2) and temperature intervals (greater than 50℃) is significantly reduced by 21.6% and 18.6%. The mean RMSE is reduced by 9.78% for all outcomes. Compared with the measured irradiance calculation results, the effective irradiance calculation results is in better agreement with the measured maximum power. The proposed method in this study improves the modelling accuracy of PV modules and the accuracy of PV power prediction. Based on the work in this study, establishing more accurate correction equations for irradiance and temperature is the future work.
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Received: 12 October 2023
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2024, Vol. 39 
