我希望你将下面的中文摘要翻译为英文要求为SCI科技论文的语言风格：近年来因为人类社会化石燃料短缺能源匮乏的现状愈发严重可再生能源得到迅速发展以应对传统能源枯竭等关键问题。在目前流行的可再生能源中如太阳能、风能、核能、潮汐能和地热能光伏发电由于其相对的通用性、绝对的安全性、足够的清洁性、资源的充足性和潜在的经济性在长期的能源战略中具有重要地位。为了改善光伏发电的充分利用和有效控制对其输出特性建立准确

In recent years, the shortage of fossil fuels and the serious energy shortage in human society have led to the rapid development of renewable energy, such as solar energy, wind energy, nuclear energy, tidal energy, and geothermal energy, to address key issues such as the exhaustion of traditional energy sources. Among the popular renewable energy sources, photovoltaic (PV) power generation has an important position in long-term energy strategies due to its relative universality, absolute safety, sufficient cleanliness, abundant resources, and potential economy. To improve the full utilization and effective control of PV power generation, it is particularly important to establish accurate mathematical models of its output characteristics. However, since the physical parameters of PV modules largely depend on environmental conditions, such as solar irradiance and temperature, accurately and reliably establishing models of physical parameters and environmental conditions is a huge challenge. Compared with traditional power generation technologies, the output characteristics of PV cells are closely related to irradiance and temperature, and it is crucial to accurately establish the relationship between model parameters and irradiance and temperature. However, the current working conditions for solving PV cell model parameters mainly rely on a set of conversion equations to convert the model parameters under reference conditions to any working conditions, thereby obtaining parameter values under these working conditions. Whether the model parameters under reference conditions are compatible with the conversion equations is an important factor affecting the accuracy of other condition models. In addition, the irradiance used for converting electrical energy is different from the value measured by the irradiance sensor, and the internal temperature of the battery panel cannot be directly measured by the temperature sensor. Therefore, research on methods for measuring irradiance and temperature correction is urgently needed. This paper focuses on the above two issues and conducts research from two aspects: measurement of illumination temperature correction and multi-condition parameter solving. The specific research contents are as follows: (1) This paper proposes a method for measuring the correction of irradiance and the back surface temperature of the battery panel. The difference in the spectral response range between the irradiance sensor and the PV cell will cause the measured irradiance value to be inconsistent with the irradiance converted into electrical energy. First, the daily and annual variations of solar spectrum are analyzed to establish the mapping relationship between effective irradiance and various spectral influencing factors. Secondly, the relationship between the internal temperature of the PV panel and the back surface temperature of the battery panel is established, mainly related to irradiance. Then, a optimization algorithm is used to extract the parameters in the correction formula. Finally, the effectiveness of the correction formula is validated using experimental data. The proposed correction method can be used for any PV model to improve the accuracy of the modeling process and reduce errors caused by measurement problems. (2) This paper proposes a new method for extracting physical parameters under reference conditions based on particle swarm optimization. In the proposed method, the model parameter values under reference conditions are corrected using other conditions to improve the adaptability of the reference condition parameters to the conversion equation. The maximum allowable error of the I-V characteristic curve under reference conditions is discussed as an inequality constraint for the reference condition error. In addition, new algorithms are proposed to extract the correction values for the reference conditions. Since the proposed method fully considers other working conditions, it still has high accuracy when obtaining parameter values under other working conditions through the conversion equation. The proposed method is validated in a single and dual diode model and in short-term and long-term power output prediction, and is considered an effective alternative method for extracting PV model parameters. (3) This paper proposes a new method based on the power law model (PLM) to predict the I-V characteristics and output power of PV modules under different working conditions. The relationship between the parameters in the PLM and the manufacturer's data sheet information has been established. The irradiance and temperature dependence of the shape parameters in the PLM have been obtained and studied in depth. Due to the inherent simplicity and clear expression of the PLM, the proposed method can predict the I-V characteristics and output power without using any iterative process, thereby reducing the complexity of the calculation. In addition, this paper further studied the mapping relationship between the shape parameters in the PLM and the irradiance and temperature and eliminated the influence of selecting reference conditions by modifying a new set of conversion equations. The parameters of the new conversion equation can be extracted from experimental data using optimization algorithms. Due to the advantages of the PLM, the proposed method is applicable to any type of PV module, and the I-V characteristics can be clearly expressed without using Lambert W function or iterative solutions. The effectiveness and accuracy of the proposed method are validated and tested on a large dataset of 18 PV cells in three locations. Compared with existing methods, the proposed method shows higher accuracy and better performance in estimating the I-V characteristics and maximum power under different weather conditions. Keywords: photovoltaic cells; diode model; shape parameters; parameter solving.