Pandit Deendayal Petroleum University

About: Pandit Deendayal Petroleum University is a education organization based out in Gandhinagar, Gujarat, India. It is known for research contribution in the topics: Welding & Computer science. The organization has 996 authors who have published 1804 publications receiving 16594 citations.

Stochastic impact assessment of PHEV charger levels in a microgrid

Abstract: In this paper, we have investigated the impact of different plug-in hybrid electric vehicle (PHEV) charger topologies on the fuel cost, network loss, and loss allocation of a gridconnected AC microgrid. The presence of distributed generation, both renewable and non-renewable resources, has been considered in the problem formulation. Uncertainties related to PHEV load, electrical load, renewable generation, and grid power price have been modeled using “Hong’s 2m point estimate method”. The dispatchable units and the battery energy storage system are optimally scheduled to minimize the cost of operation. The network loss is allocated to different nodes using an exact loss allocation approach. Impacts of three charger types, namely-level 1, level 2, and level 3 on the system operating cost, loss, and loss allocation are investigated. Simulation results for a thirty-three node system are also presented.

FT-IR and thermal studies of iron–nickel–manganese ternary levo-tartrate crystals

Abstract: Several metallic tartrates deserve special attention due to their various applications. In the present investigation, mixed tartrate crystals of three different metals are reported. Using single diffusion gel growth technique, iron–nickel–manganese (Fe–Ni–Mn) ternary levo-tartrate compound crystals were grown with different concentrations. Spherulitic crystals with different coloration were obtained for various concentrations of metals. The concentrations of metals were determined by EDAX. The presence of functional groups was confirmed using Fourier transform infrared (FT-IR) spectroscopy. The powder XRD study suggested that the crystals belong to the orthorhombic system. The thermal stability of all the samples was studied from room temperature to 900 °C. It was found that the composition of the crystal affects the dehydration and decomposition behaviors. The results are discussed.

A systematic study on shaping the future of solar prosumage using deep learning

Abstract: One of the core necessities for development in the modern world is a clear access to energy. Therefore, places that lack access to energy face serious challenges on their ability to progress effectively. In the bid to progress, nations have utilized conventional energy sources for decades leading to plummeting natural oil levels and increasing global temperatures, not to mention climate change. Energy transitions have begun to catch up but the world is far from creating a decisive decline in global emissions. Next to transportation, electricity generation burns the highest amount of fossil fuels today. The losses incurred during transmission and distribution exacerbate existing problems. One viable solution is to interlink local sustainable energy generation plants to existing grids. As of 2019, 583.5 GW of operation photovoltaic energy supplies our demands. Solar prosumage development promises to boost this rise in PV usage. However, it is difficult as it is to manage conventional grids let alone interlinked smart grids. Unlike classical approaches to developing electrical grid lines, modern systems demand much more in terms of tangible resources, statistical analysis and computational techniques. This level of complexity has inspired engineers to develop Artificial Intelligence techniques that can develop deep neural networks that grow robustly, while maintaining flexibility in handling non-linear complex relationships within large sets of data. In this paper, we provide a systematic study of existing AI solutions for smart solar grids and discuss the possible challenges associated with implementing this technology in near future.