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Probabilistic Feed foreword Neural Network Based Power System Stabilizer For Excitation Control System Of Synchronous Generator (Record no. 56058)

MARC details
000 -LEADER
fixed length control field 01816nam a22001337a 4500
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Jamshed, Ahmed
-- 13MPE13
-- Supervisor Dr. Aslam Pervez Memon
245 ## - TITLE STATEMENT
Title Probabilistic Feed foreword Neural Network Based Power System Stabilizer For Excitation Control System Of Synchronous Generator
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Nawabshsh:
Name of publisher QUEST,
Year of publication 2015.
300 ## - PHYSICAL DESCRIPTION
Number of Pages 58p, :
500 ## - GENERAL NOTE
General note ABSTRACT<br/><br/>An economical and reliable power system is responsible to generate and deliver Eletric power in an efficient way by controlling terminal voltage and load frequency within permissible limits. An excitation system plays major role in the stability of power system. The high gain and fast action of an automatic voltage regulator (AVR) produces negative damping oscillation in the power system. To reduce these oscillations power system PSS) is connected in conjunction with exitation system. The PSS must be tuned to cope with the changing load conditions.<br/>For this purpose, probabilistic feedforward neural network (PFFNN) based power system stabilizer is proposed in this research work. The conventional PSS is designed and developed in Matlab and the data of frequency deviation and terminal voltage (Vt) is stored as input in order to train the probabilistic neural network (PNN) as PSS.<br/>The simulation results of terminal voltage (Vt) and load frequency with conventional PSS and PNN-PSS are compared and discussed. The simulation results, when compared with conventional PSS, show that the proposed PSS has good control on the oscillations. It is also observed that PNN-PSS can enhance the dynamic and the transient stability of power system more easily and efficiently during the wide range of operating conditions.<br/>
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Department of Electrical Engineering
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://tinyurl.com/22ujkkj7
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis and Dissertation
Holdings
Withdrawn status Lost status Home library Current library Date acquired Accession Number Koha item type
    Research Section Research Section 24/11/2016 MP/09-91 Thesis and Dissertation
    Research Section Research Section 25/09/2018 MP/24-248 Thesis and Dissertation

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