No abstract

This paper presents an approach for computing the long duration availability of semitransparent photovoltaic system by steady-state analysis. This method employs the Markov process with constant transition rates and different degradation states for similar components and fixed configuration. In this research, average output power from modules is presented, which is considered in the same state with respect to the constant transient transition rate and variable repair rate. The output energy for 20 years with transition and absorbing time of single semitransparent photovoltaic module has been estimated for steady-state condition. This method includes all possibilities of failures associated with the operation of the semitransparent photovoltaic system.

There is tremendous potential in solar energy to meet future electricity demands. Partial shading (PS) and drift are two major problems that must be addressed simultaneously to achieve the maximum power point (MPP) of a stand-alone PV system, which are discussed in this paper. Both of these factors contribute to the voltage drop due to heavy steady-state oscillation. The partial shading and drift problem are associated with severe rapid changes in the insolation. A modified drift-control perturbation and observation DC-(P&O) approach was investigated using a low-cost programmable hardware solution, i.e., the ARM Cortex M4 32-bit Microcontroller (MC) (STM32F407VGT6), with efficient embedded programming and Waijung block sets for real-time solutions. The experimental setup was accomplished on a 40-watt solar panel. It was found that the proposed method had a significant impact on drift control during abrupt changes in current and voltage caused by shading effects, with the controller conversion efficiency of 80.39% and 94.48% with percentage absolute errors of 7.3 and 7.2 for cases with and without PS and drift, respectively.

Abstract-This paper presents an algorithm to predict output power or performance parameters i.e., open circuit voltage and short circuit current of a glass-glass (G-G)i.e., semitransparentsolar thermal module very close to the experimental values. The predicted performance parameters by the proposed algorithm have been found closer to the experimental values or actual parameters than those computed by the artificial neural network (ANN) and analytical approach alone.The proposed algorithm uses ANN to reduce the root mean square error (RMSE)upto 100% between performance parameters of the prototype solar cell model under study due to ANN and analytical approaches alone. Solar irradiation andsolar-cell temperature are the essential parameters for design, prediction and performance analysis of any photovoltaic solar energy system. Therefore, solar irradiation,and solar cell temperature have been taken as input parameters in the proposed algorithm, ANN model and analytical model.

No abstract