Photovoltaic Output Power Modeling And Study Based On Shape Parameters

Due to the global energy shortage,the advantages of clean,safe and renewable photovoltaic power generation,and the growing interest in renewable energy,the scale of installed photovoltaic(PV)has increased significantly in recent years.As the basic component module of a large-scale photovoltaic system,photovoltaic modules,its output modeling and power output under different operating conditions are crucial to the design,manufacturing,and evaluation of photovoltaic power generation systems.However,the electrical parameters of the modules provided by the module manufacturer,which are under a specific condition,Standard Testing Condition(STC),are not sufficient to accurately model the electrical characteristics and power output of photovoltaic module under a wide range of actual solar irradiance and temperature conditions.Therefore,this dissertation selects the output power and current-voltage(Ⅰ-Ⅴ)characteristics of photovoltaic modules under different working conditions and establishing the relationship between parameters and environmental factors in the output characteristic equation as the main research topic,to achieve rapid and reliable photovoltaic modeling,which is very important for the tracking of the maximum power point of the photovoltaic module and the grid dispatching after photovoltaic grid connection.This dissertation studies the relationship between the output characteristics with the change of solar irradiance and module temperature.At present,the commonly used models of PV module output Ⅰ-Ⅴ characteristics include single-diode model(SDM)and mathematical power law model(PLM).The physical meaning of the parameters in SDM is clear,but the implicit characteristics of the Ⅰ-Ⅴ equation bring various calculation inconveniences when the parameters are obtained and the output power of the photovoltaic module is modeled.The PLM has the characteristics of explicit expression,which effectively reduces the computational complexity of parameter calculation,and provides a new prospect for establishing the relationship between the parameters in the output characteristic equation and the environmental parameters.The analysis of the relationship between the parameters in the two models and the environmental conditions,and the specific work are summarized as follows:(1)Based on the analytical relationship between the shape parameters in the PLM and the one in physical model,the dependence of the shape parameters in PLM on solar irradiance and temperature is derived and studied in detail,and a new analytical modeling of PV output power and electrical characteristics under different solar irradiance and temperature operating conditions is obtained.It has been verified that this model has high accuracy in modeling the output power of photovoltaic modules with different material types.(2)Combining the characteristics of artificial neural network(ANN)with accurate,reliable,and no human intervention in data modeling,this dissertation proposes a new artificial intelligence modeling of PV output power and electrical characteristics based on ANN and PLM.This model only needs inputting two parameters,namely actual solar irradiance and module temperature into the well-trained ANN to obtain shape parameter values of the PV module under the corresponding conditions,and then obtain the Ⅰ-Ⅴ curve and PV output power.In addition,considering the influence of two other external environmental factors,solar zenith angle and azimuth angle on PV modules,a four-input artificial neural network model was proposed based on the above-mentioned two-input artificial neural network model.The modeling effects of these two ANN models are compared.(3)This dissertation studies the change of shape parameters in the PLM model with solar irradiance and temperature for the first time.The analytical expression of shape parameters with solar irradiance and temperature are derived in the analytical model of PV module output power.In the study of the output power model based on artificial neural network,the changing of each shape parameter with solar irradiance and temperature is obtained more accurately,and the obtained results are compared with the one from the analytical model.(4)Based on the analysis of the power and Ⅰ-Ⅴ characteristics of the PV module under different solar irradiance and temperature condition,and the artificial neural network model proposed in this dissertation,the programming simulation based on MATLAB is completed,and the experimental data from National Renewable Energy Laboratory(NREL)is used.A large number of measured data from different material types of PV modules provided by NREL verifies the above-mentioned models in multiple directions,and the analysis of the accuracy and applicability of the models is completed.