Comparative Analysis of GA and PSO Optimization for Power System Stability Parameters
Abstract: The designing of power transmission network is a difficult task due to the complexity of power system. Due to the complexity of the power system, there is always a loss of the stability due to the fault. Whenever a fault is intercepted in the system, the whole system goes to severe transients. These transients cause oscillation in phase angle which leads poor power quality. The nature of oscillation is increasing instead of being sustained, which leads system failure in form of generator damage. To reduce and eliminate the unstable oscillations one needs to use a stabilizer which can generate a perfect compensatory signal in order to minimize the harmonics generated due to instability. This paper presents a Power System stabilizer to reduce oscillations due to small signal disturbance. We also applied Genetic algorithm (GA) and Particle swarm optimization (PSO) for the parameter tuning of the stabilizer. The reason behind the use of GA and PSO instead of conventional methods is that it searches the parameter heuristically, which leads better results. The effectiveness of proposed stabilizers for suppressing oscillation due to change in mechanical input and excitation is examined by investigating their change in rotor angle and power angle deviation in the SMIB system.

Authors: Atul M. Gajare, Dr. R. P. Singh

File Name: Atul_50600-17-104.pdf
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SVD Based Signal Detection using Cultural Algorithm for Spectrum Sensing
Abstract: Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. Recent research studied spectrum sensing using energy detection and network cooperation via modeling and simulations. However, there is a lack of experimental study that shows the feasibility and practical performance limits of this approach under real noise and interference sources in wireless channels. This paper presents the development of efficient and reliable spectrum sensing algorithm for cognitive radio network with the help of soft computing techniques. A Cultural Algorithm (CA) optimized model for conventional SVD based spectrum sensing algorithm has been presented.

Authors: Pallavi Garg, Shiva Bhatnagar

File Name: Pallavi_51100-17-103.pdf
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Relay based Cooperative Spectrum Sensing in Cognitive Radio Network using Coherence based Detector
Abstract: Several exemplary techniques include energy detectors, feature detectors, and cooperative sensing. In these schemes, either one or multiple secondary users (SUs) perform sensing on a single and the same channel during each sensing period. This strategy on simultaneously sensing a single channel by several SUs may limit the sensing efficiency to a large extent. This paper proposes a relay based cooperative spectrum sensing framework. Simulation of proposed work is carried out on MATLAB 2014a. The impact of signal to noise ratio, probability of detection and throughput has been evaluated on proposed algorithm.

Authors: Bharti Chouhan, Pankaj Rathi

File Name: Bharti_50900-17-104.pdf
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PAPR Reduction using PTS, Companding and Cultural Algorithm
Abstract:

Authors: Komal Choudhary, Deepak Sawle

File Name: Komal_51100-17-104.pdf
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Cognitive Radio Engine Design using Adaptive Multi-Objective GA and PSO
Abstract: The radio frequency spectrum is a scarce natural resource, so it is very important to use the spectrum efficiently. Cognitive radio, which can greatly improve the spectrum utilization, is able to adjust the transmit parameters according to the environment. In this paper, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are proposed to design the cognitive radio engine. In order to improve the performance of the algorithm, adaptive mutation mechanism and the elite strategy is introduced into the proposed algorithm. Moreover, the proposed algorithm is used to design a cognitive radio engine in a multi-carrier system with ten sub-carriers. The simulation results show that the proposed algorithm is feasible and effective

Authors: Salina Mansuri, Pratibha Nagaich

File Name: Salina_51100-17-108.pdf
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