Probabilistic Modeling and Forecasting OF Wind Power Systems In the recent past, a need has arisen for establishment of Distributed Energy Systems (DES) for generation and distribution of quality energy from different sources. A decision as to whether the distributed energy sources or Micro Grids (MG) as they are often called, be operated as a standalone, localized source, or, be connected, in some way,to a Main Electric Transmission System (METS), needs careful consideration. The components in a micro grid, almost always comprises sources of entirely different nature, such as Wind Energy (WE), Solar, Photo Voltaic (PV), Small Hydro – Generators(SHG), Gas Based Generating Systems (GBGS). In this paper, wind energy system is considered and the modeling aspects are discussed. The wind speed data from a location in Karnataka has been analyzed and shown that the probability distribution of wind speed follows Rayleigh or Gaussian/Normal distribution in comparison with Weibull distribution. Also, it has been emphasized that the power developed follows Weibull distribution. Methods for forecasting short term power accrual in Autoregressive models have been explored.
Application of a Class of Nanofluids to Improve the Loadablity of Power TransformersIt is generally known that addition of conducting or insulating particles to mineral transformer oil, lowers its breakdown strength, Ed. However, if the particulates are of molecular dimensions, or nanoparticles, (NPs), as they are called, the breakdown strength is seen to increase considerably. Recent experiments by the authors on oil cooled power equipment such as transformers showed that, nanofluids comprising NPs of selected oxides of iron, such as Fe3O4, called magnetite, added to transformer oil increased the breakdown voltage of the virgin oil and more importantly a remarkable enhancement in the thermal conductivity and the viscosity and hence an increased loadability of the transformer for a given top oil temperature (TOT).