Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Higgs boson. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Data-mining-based intelligent anti-islanding protection relay for distributed generations

Abstract: A data-mining-based intelligent anti-islanding detection scheme for distributed generation (DG) protection has been presented. The process starts at deriving highly involved features using discrete Fourier transform-based pre-processor at the DG end. The features derived include both positive and negative sequence quantities and related features. Once the features are retrieved, the decision tree is trained to build a data-mining model for identifying the islanding events from non-islanding situations, including disturbances close to islanding conditions. The proposed anti-islanding relay is extensively tested on simulation model and provides encouraging results considering wide variations in operating conditions. Further, the validation is extended on real-time digital simulator module to test the efficacy of the proposed anti-islanding relay. The test results indicate that the proposed anti-islanding relay can reliably detect islanding conditions while meeting the speed criteria.

Improved prediction of severe thunderstorms over the Indian Monsoon region using high-resolution soil moisture and temperature initialization.

Abstract: The hypothesis that realistic land conditions such as soil moisture/soil temperature (SM/ST) can significantly improve the modeling of mesoscale deep convection is tested over the Indian monsoon region (IMR). A high resolution (3 km foot print) SM/ST dataset prepared from a land data assimilation system, as part of a national monsoon mission project, showed close agreement with observations. Experiments are conducted with (LDAS) and without (CNTL) initialization of SM/ST dataset. Results highlight the significance of realistic land surface conditions on numerical prediction of initiation, movement and timing of severe thunderstorms as compared to that currently being initialized by climatological fields in CNTL run. Realistic land conditions improved mass flux, convective updrafts and diabatic heating in the boundary layer that contributed to low level positive potential vorticity. The LDAS run reproduced reflectivity echoes and associated rainfall bands more efficiently. Improper representation of surface conditions in CNTL run limit the evolution boundary layer processes and thereby failed to simulate convection at right time and place. These findings thus provide strong support to the role land conditions play in impacting the deep convection over the IMR. These findings also have direct implications for improving heavy rain forecasting over the IMR, by developing realistic land conditions.