Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model

Jiannan Kang; Jiakui Shi; Yansong Gao; Junfeng Fu; Libo Li; Fengliang Wang; Wei Wang; Jie Wan

doi:10.1109/ICPDS47662.2019.9017184

Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model

Jiannan Kang
, Jiakui Shi
, Yansong Gao
, Junfeng Fu^*
, Libo Li
, Fengliang Wang
, Wei Wang
, Jie Wan

^*Corresponding author for this work

Datang Northeast Electric Power Test and Research Institute Co. Ltd
Harbin Institute of Technology
Northeast Electric Power University
North China Electric Power University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The performance of the unit is in a state of realtime degradation, and the traditional proprietary test method cannot accurately monitor this performance degradation in time. Aiming at the above problems, a real-time monitoring system and optimization scheme for steam turbine performance based on data driven model is proposed. Firstly, a steam turbine performance prediction model based on pattern recognition and prediction function is established, which can realize medium and long-term prediction of turbine operating economy and provide early warning for performance degradation. Then, performance analysis is performed for units performance degradation, respectively in thermal system and communication. The corresponding optimization scheme is proposed in the flow design. Finally, the 600MW supercritical unit is taken as the research case. The results show that the above method is effective and feasible in practice.

Original language	English
Title of host publication	2019 IEEE International Conference on Power Data Science, ICPDS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	28-31
Number of pages	4
ISBN (Electronic)	9781728137759
DOIs	https://doi.org/10.1109/ICPDS47662.2019.9017184
State	Published - Nov 2019
Externally published	Yes
Event	2019 IEEE International Conference on Power Data Science, ICPDS 2019 - Taizhou, China Duration: 22 Nov 2019 → 24 Nov 2019

Publication series

Name	2019 IEEE International Conference on Power Data Science, ICPDS 2019

Conference

Conference	2019 IEEE International Conference on Power Data Science, ICPDS 2019
Country/Territory	China
City	Taizhou
Period	22/11/19 → 24/11/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

performance monitoring
steam turbine
system optimization
thermal economy

Access to Document

10.1109/ICPDS47662.2019.9017184

Cite this

Kang, J., Shi, J., Gao, Y., Fu, J., Li, L., Wang, F., Wang, W., & Wan, J. (2019). Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model. In 2019 IEEE International Conference on Power Data Science, ICPDS 2019 (pp. 28-31). Article 9017184 (2019 IEEE International Conference on Power Data Science, ICPDS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPDS47662.2019.9017184

@inproceedings{9830909ee8c3419494b7b9c42ac11aec,

title = "Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model",

abstract = "The performance of the unit is in a state of realtime degradation, and the traditional proprietary test method cannot accurately monitor this performance degradation in time. Aiming at the above problems, a real-time monitoring system and optimization scheme for steam turbine performance based on data driven model is proposed. Firstly, a steam turbine performance prediction model based on pattern recognition and prediction function is established, which can realize medium and long-term prediction of turbine operating economy and provide early warning for performance degradation. Then, performance analysis is performed for units performance degradation, respectively in thermal system and communication. The corresponding optimization scheme is proposed in the flow design. Finally, the 600MW supercritical unit is taken as the research case. The results show that the above method is effective and feasible in practice.",

keywords = "performance monitoring, steam turbine, system optimization, thermal economy",

author = "Jiannan Kang and Jiakui Shi and Yansong Gao and Junfeng Fu and Libo Li and Fengliang Wang and Wei Wang and Jie Wan",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Power Data Science, ICPDS 2019 ; Conference date: 22-11-2019 Through 24-11-2019",

year = "2019",

month = nov,

doi = "10.1109/ICPDS47662.2019.9017184",

language = "英语",

series = "2019 IEEE International Conference on Power Data Science, ICPDS 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "28--31",

booktitle = "2019 IEEE International Conference on Power Data Science, ICPDS 2019",

address = "美国",

}

Kang, J, Shi, J, Gao, Y, Fu, J, Li, L, Wang, F, Wang, W & Wan, J 2019, Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model. in 2019 IEEE International Conference on Power Data Science, ICPDS 2019., 9017184, 2019 IEEE International Conference on Power Data Science, ICPDS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 28-31, 2019 IEEE International Conference on Power Data Science, ICPDS 2019, Taizhou, China, 22/11/19. https://doi.org/10.1109/ICPDS47662.2019.9017184

Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model. / Kang, Jiannan; Shi, Jiakui; Gao, Yansong et al.
2019 IEEE International Conference on Power Data Science, ICPDS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 28-31 9017184 (2019 IEEE International Conference on Power Data Science, ICPDS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model

AU - Kang, Jiannan

AU - Shi, Jiakui

AU - Gao, Yansong

AU - Fu, Junfeng

AU - Li, Libo

AU - Wang, Fengliang

AU - Wang, Wei

AU - Wan, Jie

PY - 2019/11

Y1 - 2019/11

N2 - The performance of the unit is in a state of realtime degradation, and the traditional proprietary test method cannot accurately monitor this performance degradation in time. Aiming at the above problems, a real-time monitoring system and optimization scheme for steam turbine performance based on data driven model is proposed. Firstly, a steam turbine performance prediction model based on pattern recognition and prediction function is established, which can realize medium and long-term prediction of turbine operating economy and provide early warning for performance degradation. Then, performance analysis is performed for units performance degradation, respectively in thermal system and communication. The corresponding optimization scheme is proposed in the flow design. Finally, the 600MW supercritical unit is taken as the research case. The results show that the above method is effective and feasible in practice.

AB - The performance of the unit is in a state of realtime degradation, and the traditional proprietary test method cannot accurately monitor this performance degradation in time. Aiming at the above problems, a real-time monitoring system and optimization scheme for steam turbine performance based on data driven model is proposed. Firstly, a steam turbine performance prediction model based on pattern recognition and prediction function is established, which can realize medium and long-term prediction of turbine operating economy and provide early warning for performance degradation. Then, performance analysis is performed for units performance degradation, respectively in thermal system and communication. The corresponding optimization scheme is proposed in the flow design. Finally, the 600MW supercritical unit is taken as the research case. The results show that the above method is effective and feasible in practice.

KW - performance monitoring

KW - steam turbine

KW - system optimization

KW - thermal economy

UR - https://www.scopus.com/pages/publications/85082475802

U2 - 10.1109/ICPDS47662.2019.9017184

DO - 10.1109/ICPDS47662.2019.9017184

M3 - 会议稿件

AN - SCOPUS:85082475802

T3 - 2019 IEEE International Conference on Power Data Science, ICPDS 2019

SP - 28

EP - 31

BT - 2019 IEEE International Conference on Power Data Science, ICPDS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Power Data Science, ICPDS 2019

Y2 - 22 November 2019 through 24 November 2019

ER -

Kang J, Shi J, Gao Y, Fu J, Li L, Wang F et al. Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model. In 2019 IEEE International Conference on Power Data Science, ICPDS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 28-31. 9017184. (2019 IEEE International Conference on Power Data Science, ICPDS 2019). doi: 10.1109/ICPDS47662.2019.9017184

Real-time Monitoring and Optimization Modification for Turbine Performance Based on Data Driven Model

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this