Neural network based modeling of a large steam turbine-generator rotor body parameters from on-line disturbance data

被引：12

作者：

Karayaka, HB ^{[1
]}

Keyhani, A

Heydt, GT

Agrawal, BL

Selin, DA

机构：

[1] Ohio State Univ, Columbus, OH 43210 USA

[2] Arizona State Univ, Tempe, AZ USA

[3] Arizona Publ Serv Co, Phoenix, AZ USA

来源：

IEEE TRANSACTIONS ON ENERGY CONVERSION | 2001年 / 16卷 / 04期

关键词：

artificial neural networks; large utility generators; parameter identification; rotor body parameters;

D O I：

10.1109/60.969468

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

A novel technique to estimate and model rotor-body parameters of a large steam turbine-generator from real time disturbance data is presented. For each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an Output Error Method (OEM). Artificial neural network (ANN) based estimators are later used to model the nonlinearities in the estimated parameters based on the generator operating conditions. The developed ANN models are then validated with measurements not used in the training procedure. The performance of estimated parameters is also validated with extensive simulations and compared against the manufacturer values.

引用

页码：305 / 311

页数：7

共 50 条

[21] Decision Network Model for Vibration Fault Diagnosis of Steam Turbine-generator Set Based on Rough Set Theory
Zhang Aiping
Cao Liming
Yang Yang
He Xiangying
2009 INTERNATIONAL CONFERENCE ON SUSTAINABLE POWER GENERATION AND SUPPLY, VOLS 1-4, 2009, : 2245 - +
[22] Design and realization on distributed network system for on-line monitoring and fault diagnosis in the steam turbine generator sets
Tian, ZG
Meng, XY
Zhang, HF
ICEMI 2005: Conference Proceedings of the Seventh International Conference on Electronic Measurement & Instruments, Vol 4, 2005, : 263 - 268
[23] On-Line Determination of Salient-Pole Hydro Generator Parameters by Neural Network Estimator Using Operating Data (PEANN)
Shariati, O.
Aghamohammadi, M. R.
Potter, B.
IEEE ACCESS, 2021, 9 : 134638 - 134648
[24] Application of integrated neural network based on information combination for fault diagnosis in steam turbine generator
Zhang, Enping
Zhang, Huifen
Xue, Bicui
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS, 2007, : 1293 - 1297
[25] Application of Neural Network Observer for On-line Estimation of Solid-Rotor Synchronous Generators' Dynamic Parameters Using the Operating Data
Shariati, O.
Zin, A. A. Mohd
Khairuddin, A.
Pesaran, M. H. A.
Aghamohammadi, M. R.
2012 IEEE INTERNATIONAL CONFERENCE ON POWER AND ENERGY (PECON), 2012, : 493 - 498
[26] Development and implementation of neural network observers to estimate the state vector of a synchronous generator from on-line operating data
Pillutla, S
Keyhani, A
IEEE TRANSACTIONS ON ENERGY CONVERSION, 1999, 14 (04) : 1081 - 1087
[27] Research on Detection Method of Steam Turbine Rotor Unbalance and Misalignment Fault Based on Convolution Neural Network
Wang C.
Zhen Z.
Liu T.
Xie Y.
Zhang D.
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2021, 41 (07): : 2417 - 2426
[28] Dynamic modeling and simulation of drum-steam pressure based on lots of on-line data
Zhang, Xiao-Tao
Ni, Wei-Dou
Li, Zheng
Zheng, Song
Dongli Gongcheng/Power Engineering, 2004, 24 (03):
[29] The Modeling of Steam Turbine Speed Control System Based on Radial Basis Function Neural Network
Ren, Yong
Yang, Tao
Gao, Wei
Li, Yanghai
MANUFACTURING ENGINEERING AND AUTOMATION I, PTS 1-3, 2011, 139-141 : 1822 - 1826
[30] Neural network based on on-line parameters tracking scheme for adaptive AC drive system
Tongji Univ, Shanghai, China
Zidonghua Xuebao, 4 (543-546):

← 1 2 3 4 5 →