参考文献
[ 1 ]
Ma X, Wang C, Dong B, Gu G, Chen R, Li Y, et al. Carbon emissions from energy consumption in China: its measurement and driving factors. Sci Total Environ 2019;648:1411–510.
链接1
[ 2 ]
International Energy Agency. Global energy & CO2 status report 2019: emissions [Internet]. Paris: International Energy Agency; 2020 [cited 2020 Jul 12]. Available from: https://www.iea.org/reports/global-energy-co2- status-report-2019/emissions.
链接1
[ 3 ]
Rogelj J, den Elzen M, Höhne N, Fransen T, Fekete H, Winkler H, et al. Paris Agreement climate proposals need a boost to keep warming well below 2 C. Nature 2016;534(7609):631–9.
链接1
[ 4 ]
O’Dwyer E, Pan I, Acha S, Shah N. Smart energy systems for sustainable smart cities: current developments, trends and future directions. Appl Energy 2019;237:581–97.
链接1
[ 5 ]
Kong X, Liu X, Ma L, Lee KY. Hierarchical distributed model predictive control of standalone wind/solar/battery power system. IEEE Trans Syst Man Cybern 2019;49(8):1570–81.
链接1
[ 6 ]
Wu X, Shen J, Li Y, Lee KY. Steam power plant configuration, design, and control. Wiley Interdiscip Rev Energy Environ 2015;4(6):537–63.
链接1
[ 7 ]
Mukati K, Rasch M, Ogunnaike BA. An alternative structure for next generation regulatory controllers. Part II: stability analysis, tuning rules and experimental validation. J Process Contr 2009;19(2):272–87.
链接1
[ 8 ]
Ellis M, Durand H, Christofides PD. A tutorial review of economic model predictive control methods. J Process Contr 2014;24(8):1156–78.
链接1
[ 9 ]
Bindlish R. Power scheduling and real-time optimization of industrial cogeneration plants. Comput Chem Eng 2016;87:257–66.
链接1
[10]
Ma J, Jiang J. Applications of fault detection and diagnosis methods in nuclear power plants: a review. Prog Nucl Energy 2011;53(3):255–66.
链接1
[11]
Sun L, Sun W, You F. Core temperature modelling and monitoring of lithiumion battery in the presence of sensor bias. Appl Energy 2020;271:115243.
链接1
[12]
Sun L, Li D, Lee KY. Optimal disturbance rejection for PI controller with constraints on relative delay margin. ISA Trans 2016;63:103–11.
链接1
[13]
Ponce CV, Saez D, Bordons C, Núñez A. Dynamic simulator and model predictive control of an integrated solar combined cycle plant. Energy 2016;109:974–86.
链接1
[14]
Facci AL, Andreassi L, Ubertini S. Optimization of CHCP (combined heat power and cooling) systems operation strategy using dynamic programming. Energy 2014;66:387–400.
链接1
[15]
Marano V, Rizzo G, Tiano FA. Application of dynamic programming to the optimal management of a hybrid power plant with wind turbines, photovoltaic panels and compressed air energy storage. Appl Energy 2012;97:849–59.
链接1
[16]
Liu J, Luo W, Yang X, Wu L. Robust model-based fault diagnosis for PEM fuel cell air-feed system. IEEE Trans Ind Electron 2016;63(5):3261–70.
链接1
[17]
Liu X, Cui J. Economic model predictive control of boiler–turbine system. J Process Contr 2018;66:59–67.
链接1
[18]
Kuboth S, Heberle F, König-Haagen A, Brüggemann D. Economic model predictive control of combined thermal and electric residential building energy systems. Appl Energy 2019;240:372–85.
链接1
[19]
Zhou T, Song Z, Sundmacher K. Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering 2019;5(6):1017–26.
链接1
[20]
Radovic A, Williams M, Rousseau D, Kagan M, Bonacorsi D, Himmel A, et al. Machine learning at the energy and intensity frontiers of particle physics. Nature 2018;560(7716):41–8.
链接1
[21]
Yosipof A, Nahum OE, Anderson AY, Barad HN, Zaban A, Senderowitz H. Data mining and machine learning tools for combinatorial material science of alloxide photovoltaic cells. Mol Inform 2015;34(6–7):367–79.
链接1
[22]
Shang C, You F. Data analytics and machine learning for smart process manufacturing: recent advances and perspectives in the big data era. Engineering 2019;5(6):1010–6.
链接1
[23]
Dey A. Machine learning algorithms: a review. Int J Comput Sci Inf Technol 2016;7(3):1174–9.
链接1
[24]
Liu Q, Jin QB, Huang B, Liu M. Iteration tuning of disturbance observer-based control system satisfying robustness index for FOPTD processes. IEEE Trans Control Syst Technol 2017;25(6):1978–88.
链接1
[25]
Hou Z, Gao H, Lewis FL. Data-driven control and learning systems. IEEE Trans Ind Electron 2017;64(5):4070–5.
链接1
[26]
Hou ZS, Wang Z. From model-based control to data-driven control: survey, classification and perspective. Inf Sci 2013;235:3–35.
链接1
[27]
Brockett R. New issues in the mathematics of control. In: Engquist B, Schmid W, editors. Mathematics unlimited—2001 and beyond. Berlin: Springer; 2001. p. 189–219.
链接1
[28]
Unbehauen H, Rao GP. A review of identification in continuous-time systems. Annu Rev Control 1998;22:145–71.
链接1
[29]
Sun L, Li D, Hu K, Lee KY, Pan F. On tuning and practical implementation of active disturbance rejection controller: a case study from a regenerative heater in a 1000 MW power plant. Ind Eng Chem Res 2016;55(23):6686–95.
链接1
[30]
Sun L, Li G, Hua QS, Jin Y. A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control. Renew Energy 2020;147:1642–52.
链接1
[31]
Zhu H, Shen J, Lee KY, Sun L. Multi-model based predictive sliding mode control for bed temperature regulation in circulating fluidized bed boiler. Control Eng Pract 2020;101:104484.
链接1
[32]
Åström KJ, Hägglund T. Advanced PID control. Research Triangle Park: The Instrumentation, Systems, and Automation Society; 2006.
链接1
[33]
Jin Y, Sun L, Hua Q, Chen S. Experimental research on heat exchanger control based on hybrid time and frequency domain identification. Sustainability 2018;10(8):2667.
链接1
[34]
Wu X, Wang M, Liao P, Shen J, Li Y. Solvent-based post-combustion CO2 capture for power plants: a critical review and perspective on dynamic modelling, system identification, process control and flexible operation. Appl Energy 2020;257:113941.
链接1
[35]
Ettihir K, Boulon L, Agbossou K. Energy management strategy for a fuel cell hybrid vehicle based on maximum efficiency and maximum power identification. IET Electr Syst Transp 2016;6(4):261–8.
链接1
[36]
Belmokhtar K, Ibrahim H, Merabet A. Online parameter identification for a DFIG driven wind turbine generator based on recursive least squares algorithm. In: Proceedings of 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE); 2015 May 3–6; Halifax, NS, Canada. New York: IEEE; 2015. p. 965–9.
链接1
[37]
Xiao W, Lind MGJ, Dunford WG, Capel A. Real-time identification of optimal operating points in photovoltaic power systems. IEEE Trans Ind Electron 2006;53(4):1017–26.
链接1
[38]
Xu Y, Jin W, Zhu X. Parameter identification of photovoltaic cell based on improved recursive least square method. In: Proceedings of 2017 20th International Conference on Electrical Machines and Systems (ICEMS); 2017 Aug 11–14; Sydney, NSW, Australia. New York: IEEE; 2017. p. 1–5.
链接1
[39]
Lu H, Zhang Y, Wu C, Sun W. Dynamic model identification of the main steam temperature for supercritical once-through boiler. Energy Procedia 2012;17 (Pt B):1704–9.
链接1
[40]
Dai H, Xu T, Zhu L, Wei X, Sun Z. Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales. Appl Energy 2016;184:119–31.
链接1
[41]
Lebbal ME, Lecœuche S. Identification and monitoring of a PEM electrolyser based on dynamical modelling. Int J Hydrogen Energy 2009;34(14): 5992–9.
链接1
[42]
Xia B, Zhao X, De Callafon R, Garnier H, Nguyen T, Mi C. Accurate lithium-ion battery parameter estimation with continuous-time system identification methods. Appl Energy 2016;179:426–36.
链接1
[43]
Song Z, Hofmann H, Lin X, Han X, Hou J. Parameter identification of lithiumion battery pack for different applications based on Cramer–Rao bound analysis and experimental study. Appl Energy 2018;231:1307–18.
链接1
[44]
Song Z, Hou J, Hofmann HF, Lin X, Sun J. Parameter identification and maximum power estimation of battery/supercapacitor hybrid energy storage system based on Cramer–Rao bound analysis. IEEE Trans Power Electron 2019;34(5):4831–43.
链接1
[45]
Yang B, Wang J, Zhang M, Shu H, Yu T, Zhang X, et al. A state-of-the-art survey of solid oxide fuel cell parameter identification: modelling, methodology, and perspectives. Energy Convers Manage 2020;213:112856.
链接1
[46]
Yang B, Wang J, Zhang X, Yu T, Yao W, Shu H, et al. Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification. Energy Convers Manage 2020;208:112595.
链接1
[47]
Chen Z, Yuan X, Tian H, Ji B. Improved gravitational search algorithm for parameter identification of water turbine regulation system. Energy Convers Manage 2014;78:306–15.
链接1
[48]
Buchholz M, Eswein M, Krebs V. Modelling PEM fuel cell stacks for FDI using linear subspace identification. In: Proceedings of 2008 IEEE International Conference on Control Applications; 2008 Sep 3–5; San Antonio, TX, USA. New York: IEEE; 2008. p. 341–6.
链接1
[49]
Chen S, Xi Z, Yong H. Model identification of reheated steam temperature in 600 MW ultra-supercritical unit. In: Proceedings of 2015 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration; 2015 Dec 3–4; Wuhan, China. New York: IEEE; 2015. p. 148–51.
[50]
Hadavand A, Jalali AA, Famouri P. An innovative bed temperature-oriented modeling and robust control of a circulating fluidized bed combustor. Chem Eng J 2008;140(1–3):497–508.
链接1
[51]
Buchholz M, Krebs V. Dynamic modelling of a polymer electrolyte membrane fuel cell stack by nonlinear system identification. Fuel Cells 2007;7 (5):392–401.
链接1
[52]
Da Costa LF, Watanabe EH, Rolim LGB. A control-oriented model of a PEM fuel cell stack based on NARX and NOE neural networks. IEEE Trans Ind Electron 2015;62(8):5155–63.
链接1
[53]
Liu XJ, Kong XB, Hou GL, Wang JH. Modeling of a 1000 MW power plant ultra super-critical boiler system using fuzzy-neural network methods. Energy Convers Manage 2013;65:518–27.
链接1
[54]
Kouadri A, Namoun A, Zelmat M. Modelling the nonlinear dynamic behaviour of a boiler–turbine system using a radial basis function neural network. Int J Robust Nonlinear Control 2014;24(13):1873–86.
链接1
[55]
Gunasekar N, Mohanraj M, Velmurugan V. Artificial neural network modeling of a photovoltaic–thermal evaporator of solar assisted heat pumps. Energy 2015;93(Pt 1):908–22.
链接1
[56]
Kang YW, Li J, Cao GY, Tu HY, Li J, Yang J. Dynamic temperature modeling of an SOFC using least squares support vector machines. J Power Sources 2008;179(2):683–92.
链接1
[57]
Marugán AP, Márquez FPG, Perez JMP, Ruiz-Hernández D. A survey of artificial neural network in wind energy systems. Appl Energy 2018;228:1822–36.
链接1
[58]
Li CH, Zhu XJ, Cao GY, Sui S, Hu MR. Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines. J Power Sources 2008;175(1):303–16.
链接1
[59]
Tan P, He B, Zhang C, Rao D, Li S, Fang Q, et al. Dynamic modeling of NOX emission in a 660 MW coal-fired boiler with long short-term memory. Energy 2019;176:429–36.
链接1
[60]
Patwardhan SC, Prakash J, Shah SL. Soft sensing and state estimation: review and recent trends. IFAC Proc 2007;40(19):65–72.
链接1
[61]
Kadlec P, Grbic´ R, Gabrys B. Review of adaptation mechanisms for data-driven soft sensors. Comput Chem Eng 2011;35(1):1–24.
链接1
[62]
Kadlec P, Gabrys B, Strandt S. Data-driven soft sensors in the process industry. Comput Chem Eng 2009;33(4):795–814.
链接1
[63]
Su Z, Wang P, Shen J, Yu X, Lv Z, Lu L. Multi-model strategy based evidential soft sensor model for predicting evaluation of variables with uncertainty. Appl Soft Comput 2011;11(2):2595–610.
链接1
[64]
Gao XH, Su ZG. Artificial bee colony optimization of NOx emission and reheat steam temperature in a 1000 MW boiler. Math Probl Eng 2019;2019:1–13.
链接1
[65]
Shinskey FG. Process control: as taught vs as practiced. Ind Eng Chem Res 2002;41(16):3745–50.
链接1
[66]
Yang ZK, Liu CY, Song XL, Song ZY, Wang ZS. Application of RBF neural network PID in wet flue gas desulfurization of thermal power plant. In: Proceedings of 2016 International Conference on Machine Learning and Cybernetics (ICMLC). 2016 Jul 10–13; Jeju, Republic of Korea. New York: IEEE; 2016. p. 301–6.
[67]
Damour C, Benne M, Lebreton C, Deseure J, Grondin-Perez B. Real-time implementation of a neural model-based self-tuning PID strategy for oxygen stoichiometry control in PEM fuel cell. Int J Hydrogen Energy 2014;39 (24):12819–25.
链接1
[68]
Azali S, Sheikhan M. Intelligent control of photovoltaic system using BPSOGSA-optimized neural network and fuzzy-based PID for maximum power point tracking. Appl Intell 2016;44(1):88–110.
链接1
[69]
Xing Z, Li Q, Su X, Guo H. Application of BP neural network for wind turbines. In: Proceedings of 2009 Second International Conference on Intelligent Computation Technology and Automation; 2009 Oct 10–11; Changsha, China. New York: IEEE; 2009. p. 42–4.
链接1
[70]
Asgharnia A, Shahnazi R, Jamali A. Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms. ISA Trans 2018;79:27–44.
链接1
[71]
Ou K, Wang YX, Li ZZ, Shen YD, Xuan DJ. Feedforward fuzzy-PID control for air flow regulation of PEM fuel cell system. Int J Hydrogen Energy 2015;40 (35):11686–95.
链接1
[72]
Lygouras JN, Botsaris PN, Vourvoulakis J, Kodogiannis V. Fuzzy logic controller implementation for a solar air-conditioning system. Appl Energy 2007;84 (12):1305–18.
链接1
[73]
Haji Haji V, Monje CA. Fractional order fuzzy-PID control of a combined cycle power plant using particle swarm optimization algorithm with an improved dynamic parameters selection. Appl Soft Comput 2017;58:256–64.
链接1
[74]
Zhang S, Taft CW, Bentsman J, Hussey A, Petrus B. Simultaneous gains tuning in boiler/turbine PID-based controller clusters using iterative feedback tuning methodology. ISA Trans 2012;51(5):609–21.
链接1
[75]
Han J. From PID to active disturbance rejection control. IEEE Trans Ind Electron 2009;56(3):900–6.
链接1
[76]
Sun L, Zhang Y, Li D, Lee KY. Tuning of active disturbance rejection control with application to power plant furnace regulation. Control Eng Pract 2019;92:104122.
链接1
[77]
Sun L, Hua Q, Shen J, Xue Y, Li D, Lee KY. Multi-objective optimization for advanced superheater steam temperature control in a 300 MW power plant. Appl Energy 2017;208:592–606.
链接1
[78]
Chakib R, Cherkaoui M, Essadki A. Inertial response used for a short term frequency control for DFIG wind turbine controlled by ADRC. ARPN J Eng Appl Sci 2016;11(5):2916–22.
链接1
[79]
Yu Y, Hu X. Active disturbance rejection control strategy for grid-connected photovoltaic inverter based on virtual synchronous generator. IEEE Access 2019;7:17328–36.
链接1
[80]
Sun L, Jin Y, You F. Active disturbance rejection temperature control of opencathode proton exchange membrane fuel cell. Appl Energy 2020;261:114381.
链接1
[81]
Sun L, Shen J, Hua Q, Lee KY. Data-driven oxygen excess ratio control for proton exchange membrane fuel cell. Appl Energy 2018;231:866–75.
链接1
[82]
Ahn HS, Chen YQ, Moore KL. Iterative learning control: brief survey and categorization. IEEE Trans Syst Man Cybern Part C 2007;37(6): 1099–121.
链接1
[83]
Pan T, Shen J, Sun L, Lee KY. Thermodynamic modelling and intelligent control of fuel cell anode purge. Appl Therm Eng 2019;154:196–207.
链接1
[84]
Tutty OR, Blackwell M, Rogers E, Sandberg R. Computational fluid dynamics based iterative learning control of peak loads in wind turbines. In: Proceedings of 2012 IEEE 51st IEEE Conference on Decision and Control (CDC); 2012 Dec 10–12; Maui, HI, USA. New York: IEEE; 2012. p. 3948–53.
链接1
[85]
Liu H, Li S, Chai T. Intelligent decoupling control of power plant main steam pressure and power output. Int J Electr Power Energy Syst 2003;25 (10):809–19.
链接1
[86]
Thounthong P, Luksanasakul A, Koseeyaporn P, Davat B. Intelligent modelbased control of a standalone photovoltaic/fuel cell power plant with supercapacitor energy storage. IEEE Trans Sustain Energy 2013;4(1): 240–9.
链接1
[87]
Wang W, Li HX, Zhang J. Intelligence-based hybrid control for power plant boiler. IEEE Trans Control Syst Technol 2002;10(2):280–7.
链接1
[88]
Sun L, Dong J, Li D, Lee KY. A practical multivariable control approach based on inverted decoupling and decentralized active disturbance rejection control. Ind Eng Chem Res 2016;55(7):2008–19.
链接1
[89]
Sun L, Hua Q, Li D, Pan L, Xue Y, Lee KY. Direct energy balance based active disturbance rejection control for coal-fired power plant. ISA Trans 2017;70:486–93.
链接1
[90]
Wu X, Shen J, Li Y, Lee KY. Data-driven modeling and predictive control for boiler–turbine unit using fuzzy clustering and subspace methods. ISA Trans 2014;53(3):699–708.
链接1
[91]
Wang X, Huang B, Chen T. Data-driven predictive control for solid oxide fuel cells. J Process Contr 2007;17(2):103–14.
链接1
[92]
Wu X, Shen J, Sun S, Li Y, Lee KY. Data-driven disturbance rejection predictive control for SCR denitrification system. Ind Eng Chem Res 2016;55 (20):5923–30.
链接1
[93]
Wu X, Shen J, Li Y, Wang M, Lawal A. Flexible operation of post-combustion solvent-based carbon capture for coal-fired power plants using multi-model predictive control: a simulation study. Fuel 2018;220:931–41.
链接1
[94]
Zeng P, Li HP, He HB, Li SH. Dynamic energy management of a microgrid using approximate dynamic programming and deep recurrent neural network learning. IEEE Trans Smart Grid 2019;10(4):4435–45.
链接1
[95]
Kim TY, Kim BS, Park TC, Yeo YK. Model-based control of a molten carbonate fuel cell (MCFC) process. Korean J Chem Eng 2018;35(1):118–28.
链接1
[96]
Dong Z, Zhang Z, Dong Y, Huang X. Multi-layer perception based model predictive control for the thermal power of nuclear superheated-steam supply systems. Energy 2018;151:116–25.
链接1
[97]
Liu S, Sun L, Zhu S, Li J, Chen X, Zhong W. Operation strategy optimization of desulfurization system based on data mining. Appl Math Model 2020;81:144–58.
链接1
[98]
Gu Y, Zhao W, Wu Z. Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems. J Process Contr 2011;21(7):1040–8.
链接1
[99]
More A, Deo MC. Forecasting wind with neural networks. Mar Struct 2003;16 (1):35–49.
链接1
[100]
Li F, Ren G, Lee J. Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks. Energy Convers Manage 2019;186:306–22.
链接1
[101]
Wang Y, Wang H, Srinivasan D, Hu Q. Robust functional regression for wind speed forecasting based on sparse Bayesian learning. Renew Energy 2019;132:43–60.
链接1
[102]
Ning C, You F. Data-driven adaptive robust unit commitment under wind power uncertainty: a Bayesian nonparametric approach. IEEE Trans Power Syst 2019;34(3):2409–18.
链接1
[103]
Papaefthymiou G, Klockl B. MCMC for wind power simulation. IEEE Trans Energy Convers 2008;23(1):234–40.
链接1
[104]
Feng C, Cui M, Hodge BM, Zhang J. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. Appl Energy 2017;190:1245–57.
链接1
[105]
Yildiz B, Bilbao JI, Sproul AB. A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renew Sust Energ Rev 2017;73:1104–22.
链接1
[106]
Voyant C, Notton G, Kalogirou S, Nivet ML, Paoli C, Motte F, et al. Machine learning methods for solar radiation forecasting: a review. Renew Energy 2017;105:569–82.
链接1
[107]
Sutton RS, Barto AG. Reinforcement learning: an introduction. IEEE Trans Neural Netw 1998;9(5):1054.
链接1
[108]
Ren Y, Liao Z, Sun J, Jiang B, Wang J, Yang Y, et al. Molecular reconstruction: recent progress toward composition modeling of petroleum fractions. Chem Eng J 2019;357:761–75.
链接1
[109]
Liao Z, Hu Y, Wang J, Yang Y, You F. Systematic design and optimization of a membrane–cryogenic hybrid system for CO2 capture. ACS Sustain Chem Eng 2019;7(20):17186–97.
链接1
[110]
Jasmin EA, Imthias Ahamed TP, Jagathy Raj VP. Reinforcement learning approaches to economic dispatch problem. Int J Electr Power Energy Syst 2011;33(4):836–45.
链接1
[111]
Wei C, Zhang Z, Qiao W, Qu L. Reinforcement-learning-based intelligent maximum power point tracking control for wind energy conversion systems. IEEE Trans Ind Electron 2015;62(10):6360–70.
链接1
[112]
Kofinas P, Dounis AI, Vouros GA. Fuzzy Q-learning for multi-agent decentralized energy management in microgrids. Appl Energy 2018;219:53–67.
链接1
[113]
Hua H, Qin Y, Hao C, Cao J. Optimal energy management strategies for energy Internet via deep reinforcement learning approach. Appl Energy 2019;239: 598–609.
链接1
[114]
Yang T, Zhao L, Li W, Zomaya AY. Reinforcement learning in sustainable energy and electric systems: a survey. Annu Rev Control 2020;49:145–63.
链接1
[115]
Odgaard PF, Mataji B. Observer-based fault detection and moisture estimating in coal mills. Control Eng Pract 2008;16(8):909–21.
链接1
[116]
Peter O, Lin B, Sten J. Observer-based and regression model-based detection of emerging faults in coal mills. IFAC Proc 2006;39(13):687–92.
链接1
[117]
Yin S, Wang G, Karimi HR. Data-driven design of robust fault detection system for wind turbines. Mechanism 2014;24(4):298–306.
链接1
[118]
Schlechtingen M, Ferreira SI. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection. Mech Syst Signal Process 2011;25(5):1849–75.
链接1
[119]
Lowery Natalie LH, Vahdati Maria M, Potthast Roland WE, Holderbaum W. Classification and fault detection methods for fuel cell monitoring and quality control. J Fuel Cell Scl Tech 2013;10(2):021002.
链接1
[120]
Salahshoor K, Kordestani M, Khoshro MS. Fault detection and diagnosis of an industrial steam turbine using funsion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers. Energy 2010;35 (12):5472–82.
链接1
[121]
Moradi M, Chaibakhsh A, Ramezani A. An intelligent hybrid technique for fault detection and condition monitoring of a thermal power plant. Appl Math Model 2018;60:34–47.
链接1
[122]
Shafer G. A mathematical theory of evidence. Princeton: Princeton University Press; 1976.
链接1
[123]
Smets P, Kennes R. The transferable belief model. Artif Intell 1994;66 (2):191–234.
链接1
[124]
Denz´ux T. 40 years of Dempster–Shafer theory. Int J Approx Reason 2016;79:1–6.
链接1
[125]
Dempster AP. Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 1967;38(2):325–39.
链接1
[126]
Ma S, Jia B, Wu J, Yuan Y, Jiang Y, Li W. Multi-vibration information fusion for detection of HVCB faults using CART and D-S evidence theory. ISA Trans. In press.
[127]
Su ZG, Wang PH. Improved adaptive evidential k-NN rule and its application for monitoring level of coal powder filling in ball mill. J Process Contr 2009;19(10):1751–62.
链接1
[128]
Su ZG. Research on theory of belief function and modelling for cognizing unmeasured parameters in power system [dissertation]. Nanjing: Southeast University; 2010. Chinese.
链接1
[129]
Chen XL, Wang PH, Hao YS, Zhao M. Evidential KNN-based condition monitoring and early warning method with applications in power plant. Neurocomputing 2018;315:18–32.
链接1