A New Approach of Modeling an Ultra-Super-Critical Power Plant for Performance Improvement

被引:10
作者
Hou, Guolian [1 ]
Yang, Yu [1 ]
Jiang, Zhuo [1 ]
Li, Quan [2 ]
Zhang, Jianhua [1 ]
机构
[1] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China
[2] State Grid Zhejiang Elect Power Res Inst, Hangzhou 310014, Zhejiang, Peoples R China
来源
ENERGIES | 2016年 / 9卷 / 05期
关键词
modeling; ultra super-critical power plant; coordinated control system; T-S fuzzy model; performance improvement;
D O I
10.3390/en9050310
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 suitable model of coordinated control system (CCS) with high accuracy and simple structure is essential for the design of advanced controllers which can improve the efficiency of the ultra-super-critical (USC) power plant. Therefore, with the demand of plant performance improvement, an improved T-S fuzzy model identification approach is proposed in this paper. Firstly, the improved entropy cluster algorithm is applied to identify the premise parameters which can automatically determine the cluster numbers and initial cluster centers by introducing the concept of a decision-making constant and threshold. Then, the learning algorithm is used to modify the initial cluster center and a new structure of concluding part is discussed, the incremental data around the cluster center is used to identify the local linear model through a weighted recursive least-square algorithm. Finally, the proposed approach is employed to model the CCS of a 1000 MW USC one-through boiler power plant by using on-site measured data. Simulation results show that the T-S fuzzy model built in this paper is accurate enough to reflect the dynamic performance of CCS and can be treated as a foundation model for the overall optimizing control of the USC power plant.
引用
收藏
页数:15
相关论文
共 19 条
  • [1] Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm
    Chakraborty, D
    Pal, NR
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2001, 31 (03): : 391 - 400
  • [2] Ding Jie, 2011, Electric Power Science and Engineering, V27, P29
  • [3] Han Zhong-xu, 2005, Proceedings of the CSEE, V25, P24
  • [4] Hou GL, 2011, C IND ELECT APPL, P1308, DOI 10.1109/ICIEA.2011.5975789
  • [5] Effective fuzzy c-means clustering algorithms for data clustering problems
    Kannan, S. R.
    Ramathilagam, S.
    Chung, P. C.
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (07) : 6292 - 6300
  • [6] Fuzzy clustering: More than just fuzzification
    Klawonn, Frank
    Kruse, Rudolf
    Winkler, Roland
    [J]. FUZZY SETS AND SYSTEMS, 2015, 281 : 272 - 279
  • [7] Controller Design for a Large-Scale Ultrasupercritical Once-Through Boiler Power Plant
    Lee, Kwang Y.
    Van Sickel, Joel H.
    Hoffman, Jason A.
    Jung, Won-Hee
    Kim, Sung-Ho
    [J]. IEEE TRANSACTIONS ON ENERGY CONVERSION, 2010, 25 (04) : 1063 - 1070
  • [8] A new T-S fuzzy-modeling approach to identify a boiler-turbine system
    Li, Chaoshun
    Zhou, Jianzhong
    Li, Qingqing
    An, Xueli
    Xiang, Xiuqiao
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (03) : 2214 - 2221
  • [9] Modeling of a 1000 MW power plant ultra super-critical boiler system using fuzzy-neural network methods
    Liu, X. J.
    Kong, X. B.
    Hou, G. L.
    Wang, J. H.
    [J]. ENERGY CONVERSION AND MANAGEMENT, 2013, 65 : 518 - 527
  • [10] Ma L.Y., 2015, CONTROL ENG PRACT, V9, P1, DOI DOI 10.1016/J.M0LP.2015.12.013