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.

Wavelet singular entropy-based symmetrical fault-detection and out-of-step protection during power swing

Abstract: Fault-detection and out-of-step protection functions are two important requirements in distance relays while dealing with power-swing conditions. In the proposed research, the first part focuses on developing wavelet entropy-based out-of-step blocking function during stable power swing and tripping function during unstable power swing. The process starts at retrieving the current signal samples during power swing and process it through wavelet transform to derive singular values, used to find out Shannon entropy, called wavelet singular entropy (WSE). Further, the power swing indicators are computed to distinguish stable power swing from unstable ones. The second part computes WSE-based indicator to distinguish faults from power swing. The WSE blocks the relay during power swing and issue the tripping signal during fault conditions based on a set threshold. The proposed technique is extensively tested for different stable and unstable power swing conditions providing improved response time for fault detection during power swing and distinguishing stable power swings from unstable ones.

A Particle-Swarm-Optimization-Based Decentralized Nonlinear Active Noise Control System

Abstract: This paper proposes a functional-link-artificial-neural-network-based (FLANN) multichannel nonlinear active noise control (ANC) system trained using a particle swarm optimization (PSO) algorithm suitable for nonlinear noise processes. The use of PSO algorithm in a multichannel ANC environment not only reduces the local minima problem but also removes the requirement of computationally expensive modeling of the secondary-path transfer functions. A decentralized version of a multichannel nonlinear ANC is also developed, which facilitates scaling up of an existing ANC setup without rederiving the learning rules. This is possible as the controller module of each channel is independent of others. Simulation study of the two new multichannel ANC systems demonstrates comparable mitigation performance. However, the decentralized one is preferred to as it possesses the added advantage of scalability.