Abstract:The core objective to optimize a complex energy system is to set the reference target to guide the parameter adjustment of system operation. In this paper, a new case-based approach is proposed based on an online performance assessment program and its long-term operation data for a large power unit. The online model of a coal-fired power unit’s performance assessment is demonstrated, and the distribution pattern of the performance index is revealed by statistical analysis of the abundant data. The fundamental issues (representation of the similarity of two thermal processes, similarity measure, etc.) are tackled. The key sections and key parameters for the completion of similarity determination are proposed, which are essential to realize a case-based strategy. A full-scope simulator of power unit is used to test the availability of the method. The advantage of the case-based approach is the integrality of information over other methods.
. A reliable and practical reference objective
for the deviation diagnosis of energy system parameters[J]. Front. Energy, 2009, 3(4): 440-445.
Liping LI, Zheng LI, . A reliable and practical reference objective
for the deviation diagnosis of energy system parameters. Front. Energy, 2009, 3(4): 440-445.
Prasad G, Swidenbank E, Hogg B W. A novel performance monitoring strategy for economicalthermal power plant operation. IEEE Transactionson Energy Conversion, 1999, 14(3): 802―809 doi: 10.1109/60.790955
Garduno-Ramirez R, Lee K Y. Multi-objective optimal powerplant operation through coordinate control with pressure set pointscheduling. IEEE Transactions on EnergyConversion, 2001, 16(2): 115―122 doi: 10.1109/60.921461
Bell R D, Astrom K J. Dynamic models for boiler-turbine-alternatorunits: Data logs and parameter estimation for a 160?MW unit. Report TFRT-3192, Lund Institute of Technology,Sweden, 1987
ASME Standards. ASME PTC 6-2004 Performance Test Codes 6 on Steam Turbine. New York: ASME, 2004
Chen J-H, Hsu S C. Hybrid ANN-CBR model fordisputed change orders in construction projects. Automation in Construction, 2007, 17(1): 56―64 doi: 10.1016/j.autcon.2007.03.003
Diaz-Agudo B. BuildingCBR systems with jcolibri. Science of ComputerProgramming, 2007, 69(1―3): 68―75 doi: 10.1016/j.scico.2007.02.004
Wu M-C, Lo Y-F, Hsu S-H. A fuzzy CBR technique for generating product ideas. Expert Systems with Applications, 2008, 34(1): 530―540 doi: 10.1016/j.eswa.2006.09.018
Ou M H. Dynamic knowledge validation and verification for CBR teledermatologysystem. Artificial Intelligence in Medicine, 2007, 39(1): 79―96 doi: 10.1016/j.artmed.2006.08.004