In this paper the analysis of a single phase fully controlled converter using fractional order (FO) model is being carried out. It has been observed that the use of Integer order model of a lossy element in converters have some drawbacks which can be greatly reduced by analyzing full wave converter using non integer order. However, this theory talks nothing about the elements with frequency dependent losses, like an inductor with core loss. As a result, the theory of classical converters rarely accommodate the load with lossy elements. But in reality, all the elements produce some coreloss. Thus, the single phase full wave converter, in their present format, are unable to take into account such losses. FO models of inductor considers the frequency dependent losses as claimed in literature. This paper, probably for the first time, establishes the theory of single phase fully controlled converter with these lossy load using the FO models is analyzed.
In this paper, presents thermal image analysis on Fault Classification (FDC) of Photovoltaic (PV) Module. The traditional manual approach of PV inspection is generally more time-consuming, more dangerous, and less accurate than the modern approach of PV inspection using Thermography Images (TI). The benefit in using (TI) images is that it can be used to quickly establish problematic areas in PV Module and provide various measurement details. Thermal image analysis conducted in this research will contribute to inspect PV module by providing a more accurate and cost-efficient diagnosis of PV faults. To maintain the long-term reliability of solar modules and maximize the power output, faults in modules need to be diagnosed at an early stage. In this research, thermographic images were used to detect faults in PV Module using traditional methods and Deep learning methods are mainly used to identify and classify the type of faults that can happen in PV Module. This method will present and discuss on the fault classification and its performance parameters. The fault detection stage determined whether the PV module has an abnormal condition. In this research, performance metrics of fault classification using Deep Neural Networks (DNNs) models is analyzed, which offers high accuracy for detecting abnormalities in image classification tasks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.