An allusive realization on the performance analysis of glass to glass, glass to tedlar and aluminum base flexible photovoltaic (PV) module

The photovoltaic effect plays a significant role in power generation by using solar PV modules. The two most popular types of PV panels majorly available in the market: Glass-to-Tedlar (G-T) and Glass-to-Glass (G-G) type. Both G-T and G-G type of modules are rigid in structure due to the involvement of transparent glass cover on the top of the module. In order to have non rig-id or flexible type of module, a module structure with aluminum as base and transparent plastic sheet at the top has been taken which is known as flexible type of PV module (F-Al). The use of plastic and aluminum makes these modules more flexible and lighter in weight as compared with G-T and G-G type of PV modules. The advantage of using F-Al PV module is that it can easily be modified into desired shapes. Initially, the mathematical modeling of F-Al PV module has been developed on Simulink. The different set of data for these three types of modules has been taken for analyzing the performance of these modules. The performance analysis of G-T type, G-G type and F-Al type of PV modules have been compared in terms of efficiency, cell temperature, daily energy production. The maximum electrical efficiency has been observed with G-G PV module. It is also observed that being with flexible nature, efficiencies of F-Al PV modules are almost comparable with G-T modules. The cell temperature of F-Al PV module and G-T PV module is found to be larger than G-G module. The daily electrical energy produc-tion was found to be more with G-G module in comparison to G-T and F-Al PV modules for a typical day in the month of April, 2019. It is also observed that due to flexible nature of F-Al module, efficiency is also converging faster towards regression line in comparison to G-G and G-T module. Further, efficiency and daily energy consumption of all three modules of PV panel are realized through artificial neural network (ANN). It is observed that least mean square error (MSE) is obtained with F-Al in comparison to G-G and G-T under the implication of ANN. Therefore, additional feature of least MSE for F-Al makes its preferable over others.