Fault mechanisms and diagnosis methods for typical load mutation problems of turbo-generator sets

被引:1
|
作者
Yao, Kun [1 ]
Wang, Ying [2 ]
Fan, Shuangshuang [3 ]
Wan, Jie [3 ]
Wu, Henggang [4 ]
Cao, Yong [1 ]
机构
[1] Harbin Inst Technol, Shenzhen, Peoples R China
[2] Harbin Engn Univ, Harbin, Peoples R China
[3] Harbin Inst Technol, Harbin, Peoples R China
[4] Zhejiang Energy Technol Res Inst Co Ltd, Hangzhou, Peoples R China
来源
基金
国家重点研发计划; 中国博士后科学基金;
关键词
steam turbine; load mutation; load oscillation; governing valve; electrical interference; actuator hardware failure; STEAM-TURBINE; VALVE; MODE; OSCILLATION; SYSTEM;
D O I
10.3389/fenrg.2022.981598
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
Since flexible peak shaving has been implemented in a growing number of high-power turbo-generator sets in the power grid owing to increasing demand, the load control performance of steam turbines directly affects the safety and efficiency of the unit operation. Load-following issues, especially load mutation, weaken the frequency control performance of the unit and cause load fluctuation faults, threatening power grid safety and stability. However, the definition, classification, characterization, generation mechanism, and diagnostic methods for load mutation problems have not been systematically researched. Based on the operational data of various turbo-generator set cases, this study systematically assessed three typical load mutation problems; namely, the common fault of unreasonable parameter settings of the control system as well as new-found faults in the actuator hardware and electrical interference. Subsequently, the fault mechanisms and characterization parameters of the different set capacities were analyzed and extracted. Furthermore, a diagnosis method was designed according to the actual problem, based on which fault type was identified. Case analysis of typical sets demonstrated that this method can quickly test and diagnose faults when in actual real-world scenarios and effectively determine the cause of the fault. This method can also detect the initial fault features, which is convenient for daily maintenance and avoids fault aggravation.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Some problems of the founding of the powerful turbo-generator sets
    Taranov, V. G.
    Shvetz, N. S.
    Shvetz, V. B.
    [J]. Proceedings of the 16th International Conference on Soil Mechanics and Geotechnical Engineering, Vols 1-5: GEOTECHNOLOGY IN HARMONY WITH THE GLOBAL ENVIRONMENT, 2005, : 1567 - 1570
  • [2] Diagnosis of interturn fault in stator winding of turbo-generator
    Faiz, Jawad
    Mahmoodi, Ali
    Keravand, Mehran
    Davarpanah, Mahdi
    [J]. INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2019, 29 (12):
  • [3] On Model Updating of Turbo-Generator Sets
    Bachschmid, N.
    Pennacchi, P.
    Chatterton, S.
    Ricci, R.
    [J]. JOURNAL OF VIBROENGINEERING, 2009, 11 (03) : 379 - 391
  • [4] Fault diagnosis of turbo-generator based on RBF Neural Networks
    Li, RX
    Wang, DF
    Han, P
    Zhang, J
    [J]. 2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, : 3125 - 3129
  • [5] A fault diagnosis system for turbo-generator set by data mining
    Ping, Yang
    Wei, Ren
    [J]. 2006 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, PTS 1 AND 2, PROCEEDINGS, 2006, : 801 - 804
  • [6] Fuzzy vibration fault diagnosis system of steam turbo-generator rotor
    Xie, DM
    Song, X
    Zhou, HL
    Guo, MW
    [J]. 2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, : 411 - 415
  • [7] A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise
    Tichun WANG
    Jiayun WANG
    Yong WU
    Xin SHENG
    [J]. Chinese Journal of Aeronautics, 2020, (10) : 2757 - 2769
  • [8] A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise
    Wang, Tichun
    Wang, Jiayun
    Wu, Yong
    Sheng, Xin
    [J]. CHINESE JOURNAL OF AERONAUTICS, 2020, 33 (10) : 2757 - 2769
  • [9] Fault Diagnosis and Knowledge Management of Turbo-generator based on Support Vector Machine
    Cai, Zhong-jian
    Lu, Sheng
    Zhang, Fengchuan
    [J]. 2009 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, VOL 1, 2009, : 532 - +
  • [10] Application of wavelet neural network on turbo-generator set fault diagnosis system
    Liu Lin
    Shen Songhua
    Guan Miao
    Li Chunlong
    [J]. 2006 CHINESE CONTROL CONFERENCE, VOLS 1-5, 2006, : 354 - +