03449nas|a2200409 i 4500 20260817163304.0 280200s1999 th uu m rtt 0| a1eng d .b11777576 AIT Thesis no.ET-99-28 Sunphead Chaipunha An artificial neural network approach for digital filtering application in distance relay Bangkok : Asian Institute of Technology, 1999 123 leaves Thesis ; no. ET-99-28 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering. School of Environment Resources and Development Thesis (M.Eng.) - Asian Institute of Technology, 1999 The 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. Neural networks (Computer science) Electric power transmission Electric lines Dhadbanjan, Thukaram, Chairperson Yu, Cun Yi, Examination Committee Surapong Chiraratananon, Examination Committee Electricity Generating Authority of Thailand, Scholarship donor Asian Institute of Technology. Thesis ; no. ET-99-28 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B12791 .b11777576 mnait u 240421 0 000228 m a u 3 mnait mnarc 22 40 Barcode : 30050120731863 CREATED : 2014-03-02 RECORD # : i12754675 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120365381 CREATED : 2016-06-15 RECORD # : i13037316 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 8179 8179 0 0 0 0 MAIN MAIN mnait 2026-08-17 50.00 0 AIT Thesis no.ET-99-28 30050120731863 2026-08-17 16:33:04 3 2026-08-17 22 0 0 0 0 MAIN MAIN mnarc 2026-08-17 0 AIT Thesis no.ET-99-28 30050120365381 2026-08-17 16:33:04 2026-08-17 40