National Institute of Technology, Meghalaya

About: National Institute of Technology, Meghalaya is a education organization based out in Shillong, India. It is known for research contribution in the topics: Control theory & Computer science. The organization has 503 authors who have published 1062 publications receiving 6818 citations. The organization is also known as: NIT Meghalaya & NITM.

A Special Protection Scheme for Transmission Lines based on Wide Area Monitoring System

Abstract: The utilization of phasor measurement units (PMUs) with protective relays is rapidly increasing in the existing power transmission networks for reliable monitoring, control and protection of complex power system. Thus, it is feasible to develop an effective primary and backup protection scheme for transmission lines by utilizing the information from PMU-cum-Relays. Regarding the conventional relays, it is observed that its operation is severely affected with the presence of events like power swings, load encroachment etc. In this paper, authors propose to utilize wide area monitoring system for backup protection of transmission lines using proposed phasor data concentrator (PDC) index, which is calculated at PDC using the measured frequencies from different PMUs. As the backup protection scheme is centrally executed at PDC, the proposed scheme do not require the coordination time, but do involve communication delays associated with the network. The effectiveness of proposed special protection scheme has been tested and validated on reduced NEREB 29-bus Indian power system with distributed generation sources using SINCAL and MATLAB softwares. The simulation results reveal that the proposed special protection scheme can enhance the protection strategy of transmission lines in smart electric grid.

Thermal Performance Study of Helically Grooved Absorber Tubes for Parabolic Trough Solar Collector

Abstract: In this work, a computational study on four different types of helically grooved absorber tubes namely, semi-circular, rectangular, trapezoidal, and triangular has been carried out for their possible application in parabolic trough solar collector. In order to conduct the work, absorber tube of 2 m length with 19 mm inner and 25 mm outer diameter is selected. Flow velocities have been calculated by fixing the Reynolds number of the flow as 4000 i.e., turbulent flow. A constant heat flux of 818.5 W/m2 is provided at the lower surface of the absorber tube, facing the reflector. The simulation is performed using the finite volume based tool ANSYS FLUENT 17.1. The standard k-ε RNG turbulence model is used for simulation. The values of friction factors for semi-circular, rectangular, trapezoidal, and triangular absorber tube are 0.0511, 0.0889, 0.0929, and 0.0352, respectively. Nusselt numbers for these tubes are calculated as 68.91, 65.69, 72.05, and 85.49. Hence, it can be concluded from the present study that the thermal performance of the absorber tube with triangular groove is superior to the other groove types. The pressure drop for the same tube is also lowest.

A comparative analysis between two DPFC models in a grid connected Hybrid Solar- Wind Generation system

Abstract: In this paper a detailed performance analysis between two different models of Distributed Power Flow Controller (DPFC) is performed. The first type being the normal Distributed Power Flow Controller using batteries and the second type obtained by utilizing an extra three phase converter in place of the batteries. The system in study is a Hybrid Solar-Wind Generation system integrated with the Grid. The effect of the two models on the Hybrid System is studied in detail using results obtained from MATLAB/Simulink platform.

An Approach to Emotion Identification Using Human Gait

Abstract: Human gait data have abundant information for recognition of actions, intentions, emotions, and gender. The paper presents an approach toward classification of human emotions using gait data into three classes: happy, angry, and normal. Data of human gait for 3 emotional expressions (happy, angry, and neutral) of 25 individuals were collected. The silhouette was divided into 9 segments in order to analyze motion in various body parts moving with different frequency. The features such as centroid, aspect ratio, and orientation were extracted from different segments using geometric and Krawtchouk moments, respectively. A train model was generated from testing data using support vector machines (SVM), and hence, new feature vector was classified into three classes. The results show that polynomial kernel using geometric moment features has the maximum recognition rate of 83.06 %.