Application of ANN-SCE model on the evaluation of automatic generation control performance

Le Ren Chang-Chien, Chien Sheng Lo, Ko Shien Lee

研究成果: Conference article

摘要

Under open access of transmission grid and unbundling of generation in the deregulated electricity market, frequency regulation is considered as ancillary service to balance minute to minute generation and demand. To ensure the reliability and quality of regulation are well maintained, a fair evaluation of load frequency control (LFC) performance and responsibilities between providers and customers has become a major concern. This paper demonstrates the features of Artificial-Neural-Network based System Control Error (ANN-SCE) model in tracking a single area's AGC dynamics, in gauging various impacts on the performance of AGC, and in identifying system dynamics that may be further used as a control reference in supplementing AGC logic.

原文English
文章編號465-135
頁(從 - 到)121-126
頁數6
期刊Series on Energy and Power Systems
出版狀態Published - 2005 十二月 1
事件IASTED International Conference on Energy and Power Systems, EPS 2005 - Krabi, Thailand
持續時間: 2005 四月 182005 四月 20

指紋

Neural networks
Control systems
Gaging
Dynamical systems
Power markets

All Science Journal Classification (ASJC) codes

  • Engineering(all)

引用此文

@article{c0996185dc4c4796abcccc681e94f02e,
title = "Application of ANN-SCE model on the evaluation of automatic generation control performance",
abstract = "Under open access of transmission grid and unbundling of generation in the deregulated electricity market, frequency regulation is considered as ancillary service to balance minute to minute generation and demand. To ensure the reliability and quality of regulation are well maintained, a fair evaluation of load frequency control (LFC) performance and responsibilities between providers and customers has become a major concern. This paper demonstrates the features of Artificial-Neural-Network based System Control Error (ANN-SCE) model in tracking a single area's AGC dynamics, in gauging various impacts on the performance of AGC, and in identifying system dynamics that may be further used as a control reference in supplementing AGC logic.",
author = "Chang-Chien, {Le Ren} and Lo, {Chien Sheng} and Lee, {Ko Shien}",
year = "2005",
month = "12",
day = "1",
language = "English",
pages = "121--126",
journal = "Series on Energy and Power Systems",
issn = "1482-7891",

}

Application of ANN-SCE model on the evaluation of automatic generation control performance. / Chang-Chien, Le Ren; Lo, Chien Sheng; Lee, Ko Shien.

於: Series on Energy and Power Systems, 01.12.2005, p. 121-126.

研究成果: Conference article

TY - JOUR

T1 - Application of ANN-SCE model on the evaluation of automatic generation control performance

AU - Chang-Chien, Le Ren

AU - Lo, Chien Sheng

AU - Lee, Ko Shien

PY - 2005/12/1

Y1 - 2005/12/1

N2 - Under open access of transmission grid and unbundling of generation in the deregulated electricity market, frequency regulation is considered as ancillary service to balance minute to minute generation and demand. To ensure the reliability and quality of regulation are well maintained, a fair evaluation of load frequency control (LFC) performance and responsibilities between providers and customers has become a major concern. This paper demonstrates the features of Artificial-Neural-Network based System Control Error (ANN-SCE) model in tracking a single area's AGC dynamics, in gauging various impacts on the performance of AGC, and in identifying system dynamics that may be further used as a control reference in supplementing AGC logic.

AB - Under open access of transmission grid and unbundling of generation in the deregulated electricity market, frequency regulation is considered as ancillary service to balance minute to minute generation and demand. To ensure the reliability and quality of regulation are well maintained, a fair evaluation of load frequency control (LFC) performance and responsibilities between providers and customers has become a major concern. This paper demonstrates the features of Artificial-Neural-Network based System Control Error (ANN-SCE) model in tracking a single area's AGC dynamics, in gauging various impacts on the performance of AGC, and in identifying system dynamics that may be further used as a control reference in supplementing AGC logic.

UR - http://www.scopus.com/inward/record.url?scp=30644470159&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=30644470159&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:30644470159

SP - 121

EP - 126

JO - Series on Energy and Power Systems

JF - Series on Energy and Power Systems

SN - 1482-7891

M1 - 465-135

ER -