000			03449nas|a2200409 i 4500
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008			280200s1999 th uu m rtt 0| a1eng d
035			_a.b11777576
099		9	_aAIT Thesis no.ET-99-28
100	0		_aSunphead Chaipunha
245	1	3	_aAn artificial neural network approach for digital filtering application in distance relay
260			_aBangkok : _bAsian Institute of Technology, _c1999
300			_a123 leaves
490	1		_aThesis ; _vno. ET-99-28
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering. School of Environment Resources and Development
502			_aThesis (M.Eng.) - Asian Institute of Technology, 1999
520			_aThe main objective of the digital relaying of transmission line is to determine the phasor repi"esentations of the voltage and current signals from their sample values and thereafter to calculate the apparent impedance of faulty line from the relay location to the fault point. Then determine whether the fault lies within the relay's protective zone or not. Since impedance of linear system is defined in terms of the fundamental frequency voltage and current sinusoidal waves, it is necessary to extract the fundamental frequency components of voltage and current signals from the complex post fault voltage and current signals. This work presents an adaptive neural network approach for the estimation of fundamental components from the complex post fault voltage and current signals. The neural estimator is based on the use of an adaptive perceptron consisting of Adaptive Linear Neuron network called ADALINE. The learning parameters in the proposed algorithm are adjusted to force the actual and desired outputs to satisfy a stable difference error equation, rather than to minimize an error function. Three numerical tests have been conducted for adaptive estimation of fault impedance. The first is the simulated signals, the second is signals from transient analysis program EMTP and the third is signals from EGAT system captured by fault recorder. The estimator tracks accurately the fundamental components of the signals data corrupted with harmonics and decaying de component during transient period. The performance of the proposed algorithm is found superior to the recursive DFT based algoritlm1s.
650		0	_aNeural networks (Computer science)
650		0	_aElectric power transmission
650		0	_aElectric lines
700	1		_aDhadbanjan, Thukaram, _eChairperson
700	1		_aYu, Cun Yi, _eExamination Committee
700	0		_aSurapong Chiraratananon, _eExamination Committee
710	2		_aElectricity Generating Authority of Thailand, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. ET-99-28
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B12791
907			_a.b11777576 _bmnait _cu
902			_a240421
998			_b0 _c000228 _dm _ea _fu _g3
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909			_aBarcode : 30050120731863 _bCREATED : 2014-03-02 _cRECORD # : i12754675 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
909			_aBarcode : 30050120365381 _bCREATED : 2016-06-15 _cRECORD # : i13037316 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
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