Stability Enhancement by Comparative Analysis of AI Techniques in Power System Stablizer Integrated With Hybrid System
Keywords:Hybrid system, Stabilizer, Particle swarm optimization, neural network, Hysteresis control
Hybrid system has been modeled in MATLAB/SIMULINK environment which is then integrated with two generators based power system. The work has done over analysis of THD level in voltage output from the hybrid system with various controls being proposed for the power system stabilizer. Various controls like PI-Hysteresis, particle swarm optimization (PSO) and PSO with neural network (NN) have been implemented for comparative study. It was found that the distortion level in voltage output waveform was least in stabilizer having PSO-NN control which is 3.36%. Also the active power enhancement reached a whooping value of 9.4 KW from the hybrid system.
 S. Bhattacharyya, A. Khasnobish, S. Chatterjee, A. Konar and D. N. Tibarewala, “Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data”, 2010 International Conference on Systems in Medicine and Biology, Kharagpur, 2010, pp. 126-131.
 Tang, Zhichuan & Li, Chao & Sun, Shouqian, “Single-trial EEG classification of motor imagery using deep convolutional neural networks”, Optik - International Journal for Light and Electron Optics, 2016.
 Alexandre Barachant, Stephane Bonnet, Marco Congedo, Christian Jut- ´ ten, “Multiclass BrainComputer Interface Classification by Riemannian Geometry”, IEEE Transactions on Biomedical Engineering, 2012.
 Barachant, Alexandre & Bonnet, Stephane & Congedo, Marco & Jutten, ´ Christian, “Classification of covariance matrices using a Riemannianbased kernel for BCI applications”, Neurocomputing, 2013.
 C. Lindig-Leon and L. Bougrain, “A Multi-label Classification Method for Detection of Combined Motor Imageries”, 2015 IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, 2015
 Marquez L, Alejandro, & Munoz. "Analysis & classification of electroencephalographic signals (EEG) to identify arm movements.", Electrical Engineering, Computing Science and Automatic Control (CCE), IEEE, 2013, pp. 138- 143.
 Zhiwei, Li, and Shen Minfen. "Classification of mental task EEG signals using wavelet packet entropy and SYM.",IEEE, 2007, pp.906-909.
 Ting, Wu, Yan Guo-zheng, Yang Banghua, & Sun Hong. "EEG feature extraction based on wavelet packet decomposition for brain computer interface.", Elsevier, 2008,Vol. 41, Issue 6, pp. 618-625.
 Syed Khairul Bashar and Mohammed Imamul Hassan Bhuiyan, “Identification of Arm Movements Using Statistical Features from EEG Signals in Wavelet Packet Domain ”, IEEE, 2015.
 Saugat Bhattacharyya, DebabrotaBasu, Amit Konara, D.N. Tibarewala, “Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm”, Elsevier, 2015.
 Faisal Farooq and PrebenKidmose, “Random Forest classification for p300 based brain computer interface applications,” IEEE, 2013, pp 1-5.
 Prasant Kumar Pattnaik , Jay Sarraf, ” Brain Computer Interface issues on hand movement”, Journal of King Saud University – Computer and Information Sciences (2018) 30, 18–24.
 Muhammed Al-Suify, Walid Al-Atabany, Mohamed.A.A.Eldosoky, “Classification of right and left hand movement using nonlinear analysis” IEEE, 2017.
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Copyright (c) 2019 Harsh Vardhan Singh, Dr. Ranjeeta Khare
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