This research appraises comparative analysis between single diode and double diode model of photovoltaic (PV) solar cells to enhance the conversion efficiency of power engendering PV solar systems. Single diode model ...This research appraises comparative analysis between single diode and double diode model of photovoltaic (PV) solar cells to enhance the conversion efficiency of power engendering PV solar systems. Single diode model is simple and easy to implement, whereas double diode model has better accuracy which acquiesces for more precise forecast of PV systems performance. Exploration is done on the basis of simulation results and MATLAB tool is used to serve this purpose. Simulations are performed by varying distinct model parameters such as solar irradiance, temperature, value of parasitic resistances, ideality factor of diode and number of series and parallel connected solar cells used to assemble PV array. Conspicuous demonstration is executed to analyze effects of these specifications on the efficiency curve and power vs. voltage output characteristics of PV cell for specified models.展开更多
Current research focuses on the performance degradation of photovoltaic(PV)modules,examining both crystalline silicon(p-Si and m-Si)and thin-film technologies,including a-Si/μc-Si,HIT,CdTe and CIGS.These modules were...Current research focuses on the performance degradation of photovoltaic(PV)modules,examining both crystalline silicon(p-Si and m-Si)and thin-film technologies,including a-Si/μc-Si,HIT,CdTe and CIGS.These modules were operated outdoors in two distinct climatic zones in the United States(US)over a period of three years.The degradation analysis includes the study of various quantities,such as the decrease in peak power,the reduction in current and voltage,and the variation in the fill factor.The annual degradation rate(DR)of PV modules is obtained by a linear fit of the effective maximum power evolution over time.The results indicate that m-Si and p-Si modules experienced a slight decrease in performance,with DRs of−0.83%and−1.07%,respectively.Subsequently,the HIT module exhibited a DR of−1.75%,while CdTe and CIGS modules demonstrated DRs of−2.03%and−2.45%,respectively.The a-Si/μc-Si module showed the highest DR at−3.26%.Using the Single Diode Model(SDM),we monitored the temporal evolution of physical parameters as well as changes in the shape of the I-V and P-V curves over time.We found that the key points of the I-V curve degrade over time,as do the I-V and P-V characteristics between two days approximately 30 months apart.展开更多
To ensure the safety and stability of power grids with photovoltaic(PV)gen eration integrati on,it is necessary to predict the output perform a nee of PV modules un der varyi ng operating con ditions.In this paper,an ...To ensure the safety and stability of power grids with photovoltaic(PV)gen eration integrati on,it is necessary to predict the output perform a nee of PV modules un der varyi ng operating con ditions.In this paper,an improved artificial neural network(ANN)method is proposed to predict the electrical characteristics of a PV module by combining several neural networks under different environmental conditions.To study the dependenee of the output performance on the solar irradianee and temperature,the proposed neural network model is composed of four neural networks,it called multineural network(MANN).Each neural network consists of three layers,in which the input is solar radiation,and the module temperature and output are five physical parameters of the single diode model.The experimental data were divided into four groups and used for training the neural networks.The electrical properties of PV modules,including l-V curves,PV curves,and normalized root mean square error,were obtained and discussed.The effectiveness and accuracy of this method is verified by the experimental data for d iff ere nt types of PV modules.Compared with the traditional single-ANN(SANN)method,the proposed method shows be社er accuracy under different operating conditions.展开更多
文摘This research appraises comparative analysis between single diode and double diode model of photovoltaic (PV) solar cells to enhance the conversion efficiency of power engendering PV solar systems. Single diode model is simple and easy to implement, whereas double diode model has better accuracy which acquiesces for more precise forecast of PV systems performance. Exploration is done on the basis of simulation results and MATLAB tool is used to serve this purpose. Simulations are performed by varying distinct model parameters such as solar irradiance, temperature, value of parasitic resistances, ideality factor of diode and number of series and parallel connected solar cells used to assemble PV array. Conspicuous demonstration is executed to analyze effects of these specifications on the efficiency curve and power vs. voltage output characteristics of PV cell for specified models.
文摘Current research focuses on the performance degradation of photovoltaic(PV)modules,examining both crystalline silicon(p-Si and m-Si)and thin-film technologies,including a-Si/μc-Si,HIT,CdTe and CIGS.These modules were operated outdoors in two distinct climatic zones in the United States(US)over a period of three years.The degradation analysis includes the study of various quantities,such as the decrease in peak power,the reduction in current and voltage,and the variation in the fill factor.The annual degradation rate(DR)of PV modules is obtained by a linear fit of the effective maximum power evolution over time.The results indicate that m-Si and p-Si modules experienced a slight decrease in performance,with DRs of−0.83%and−1.07%,respectively.Subsequently,the HIT module exhibited a DR of−1.75%,while CdTe and CIGS modules demonstrated DRs of−2.03%and−2.45%,respectively.The a-Si/μc-Si module showed the highest DR at−3.26%.Using the Single Diode Model(SDM),we monitored the temporal evolution of physical parameters as well as changes in the shape of the I-V and P-V curves over time.We found that the key points of the I-V curve degrade over time,as do the I-V and P-V characteristics between two days approximately 30 months apart.
基金the National Key Research and Development Program of China(Grant No.2018YFB0904200).
文摘To ensure the safety and stability of power grids with photovoltaic(PV)gen eration integrati on,it is necessary to predict the output perform a nee of PV modules un der varyi ng operating con ditions.In this paper,an improved artificial neural network(ANN)method is proposed to predict the electrical characteristics of a PV module by combining several neural networks under different environmental conditions.To study the dependenee of the output performance on the solar irradianee and temperature,the proposed neural network model is composed of four neural networks,it called multineural network(MANN).Each neural network consists of three layers,in which the input is solar radiation,and the module temperature and output are five physical parameters of the single diode model.The experimental data were divided into four groups and used for training the neural networks.The electrical properties of PV modules,including l-V curves,PV curves,and normalized root mean square error,were obtained and discussed.The effectiveness and accuracy of this method is verified by the experimental data for d iff ere nt types of PV modules.Compared with the traditional single-ANN(SANN)method,the proposed method shows be社er accuracy under different operating conditions.
