Comparative analysis of different PV technologies under the tropical environments.

In this paper, six different types of solar PV technologies are compared in terms of their performances under tropical conditions, using three years of performance data from a 1.2 MW experimental solar farm. The technologies considered include single-crystalline silicon, polycrystalline silicon, microcrystalline silicon, amorphous silicon, copper indium selenium (CIS), and hetero-junction with intrinsic thin layer (HIT). The field performances of these cells were initially assessed using standard performance indices such as Array Yield, Reference Yield, Capture Loss, Performance Ratio, and Efficiency Ratio. Among the technologies studied, amorphous silicon and HIT-based systems demonstrated better performance, showing higher Performance and Efficiency Ratios, along with lower capture losses. This study also modelled the fluctuations in power production from these panels. Under probabilistic modeling, the ramping behavior of the systems was characterized using the Generalized Logistic Distribution. Based on this analysis, CIS PV systems were found to have minimum power ramps, where as the HIT based systems showed the highest power fluctuations. To predict minute-wise and hourly ramping of the PV systems under varying levels of solar insolation, machine learning methods based on Artificial Neural Networks (ANN), Support Vector Machines (SVM), and k-Nearest Neighbors (kNN) were developed. With a Normalized Root Mean Square Error (NRMSE) of over 96%, these models demonstrated high accuracy in capturing the ramping characteristics of the studied PV systems. The results of this study offer valuable insights into the performance of different PV systems under tropical regions, which can be used in efficiently designing and managing solar PV projects.