Multi-condition Parameter Solution And Output Characteristics Modeling Of Photovoltaic Cells

In recent years,the depletion of fossil fuels and the resulting energy crisis in human society have prompted the rapid development of renewable energy sources,including solar,wind,nuclear,tidal,and geothermal energy,to address the issue of traditional energy source exhaustion.Among these renewable energy sources,photovoltaic(PV)power generation holds a significant position in long-term energy strategies due to its relative universality,absolute safety,sufficient cleanliness,abundant resources,and potential economy.To fully utilize and effectively control PV power generation,it is crucial to establish accurate mathematical models of its output characteristics.However,since the physical parameters of PV modules are largely dependent on environmental conditions,such as solar irradiance and temperature,accurately and reliably establishing models of physical parameters and environmental conditions poses a significant challenge.Unlike traditional power generation technologies,the output characteristics of PV cells are closely related to irradiance and temperature,and accurately establishing the relationship between model parameters and irradiance and temperature is crucial.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.The compatibility of model parameters with conversion equations under reference conditions is a crucial factor that affects the accuracy of other condition models.Additionally,the value of irradiance used for converting electrical energy differs from the value measured by the irradiance sensor.Furthermore,the internal temperature of the battery panel cannot be measured directly by the temperature sensor.Therefore,there is an urgent need for research on methods for measuring irradiance and temperature correction.This paper focuses on these 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 presents a method for measuring the correction of irradiance and the backsurface temperature of the battery panel.The difference in the spectral response range between the irradiance sensor and the PV cell can cause inconsistency between the measured irradiance value and the irradiance converted into electrical energy.Firstly,the paper analyzes the daily and annual variations of the solar spectrum to establish the mapping relationship between effective irradiance and various spectral influencing factors.Secondly,it establishes the relationship between the internal temperature of the PV panel and the back-surface temperature of the battery panel,mainly related to irradiance.Then,an optimization algorithm is used to extract the parameters in the correction formula.Finally,the paper validates the effectiveness of the correction formula 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 presents a novel approach for extracting physical parameters under reference conditions using particle swarm optimization.The proposed method involves correcting the model parameter values under reference conditions using other conditions to enhance the adaptability of the reference condition parameters to the conversion equation.To ensure the accuracy of the reference condition error,the maximum allowable error of the I-V characteristic curve under reference conditions is discussed as an inequality constraint.Furthermore,new algorithms are proposed to extract the correction values for the reference conditions.By fully considering other working conditions,the proposed method maintains high accuracy when obtaining parameter values under other working conditions through the conversion equation.The effectiveness of the proposed method is demonstrated through its application in a single and dual diode model,as well as in short-term and long-term power output prediction.Overall,the proposed method is a promising alternative for extracting PV model parameters and is worthy of further investigation.(3)This paper presents a novel method that utilizes the power law model(PLM)to predict the I-V characteristics and output power of photovoltaic(PV)modules under varying working conditions.The study establishes a relationship between the parameters in the PLM and the manufacturer’s data sheet information.The research also investigates the irradiance and temperature dependence of the shape parameters in the PLM in-depth.The proposed method offers a straightforward and clear expression of the PLM,which eliminates the need for any iterative process,thereby reducing the complexity of the calculation.Furthermore,the study explores the mapping relationship between the shape parameters in the PLM and the irradiance and temperature.The research eliminates 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.The PLM’s advantages make the proposed method applicable to any type of PV module,and the I-V characteristics can be expressed without using Lambert W function or iterative solutions.The proposed method’s effectiveness and accuracy are validated and tested on a large dataset of 18 PV cells in three locations.The results show that the proposed method outperforms existing methods in estimating the I-V characteristics and maximum power under different weather conditions.In conclusion,the proposed method based on the PLM offers a simple and effective approach to predict the I-V characteristics and output power of PV modules.The study’s findings provide valuable insights into the relationship between the PLM parameters and the irradiance and temperature.The proposed method’s accuracy and effectiveness make it a promising tool for predicting the performance of PV modules under various working conditions.