热力发电过程建模与状态参数检测研究
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摘要
火力发电在今后较长时期内仍将是我国电力能源供给的主要方式。维持火电机组正常、高效运转对实现我国节能、减排的指标至关重要,而实施状态检测及控制优化是实现这一目标的有效手段。由于火电机组热力系统较为复杂,一些重要的状态信号无法测量或测量不准确,成为影响机组参数控制品质和安全经济运行的重要原因。针对研究对象的特点,灵活应用机理分析和数据分析相结合的方法,对火电单元机组的主要热力系统进行分析。构造得到了一些物理意义明确、非常具有实际应用价值、能够反映热力系统运行规律的状态信号。具体包括:
     (1)煤发热量信号。通过分析发电过程中物质、能量流动的平衡关系,利用汽轮机调节级后压力、汽轮机高调门计算开度、汽包压力和锅炉总给煤量构造出煤发热量信号。信号动态性能优良,非常适合在控制系统中应用。
     (2)锅炉热量信号。通过对锅炉大量运行数据进行分析,发现锅炉总风量、排烟氧量、炉膛压力信号与给煤量信号之间存在特定的相关关系,进一步结合机理分析解释了这种关系的成因和规律,以此为基础构造出锅炉热量信号。信号精度足够、动态性能优良。
     (3)汽包锅炉虚假水位估计信号。通过分析汽包锅炉汽水系统物质与能量的平衡关系,建立描述汽包水位信号的模型。能够以较好的动态性能反映实际水位的变化,特别是“虚假水位”现象。该估计信号能够应用于控制系统优化。
     同时,在针对以上问题开展研究的过程中,涉及具体热力系统对象内在运行规律方面和理论研究方法方面,也获得了一定的研究成果。具体包括:(1)建立正压中速磨直吹式制粉系统动态模型,分析了模型中主要参数的变化规律,并指出一次风量是影响制粉系统惯性和迟延的关键因素。(2)提出容积蓄热系数的概念,在此基础上进一步提出一种锅炉蓄热系数的计算方法。(3)建立燃料量-引风机入口压力对炉膛压力-炉膛温度的简化非线性动态模型,揭示了炉膛压力与炉膛平均温度之间内在的相互作用关系。(4)建立汽包锅炉燃料量-汽轮机高调门开度-给水流量对机组负荷-机前压力-汽包水位的三入三出多变量动态模型,模型以微分方程形式给出,所有静态、动态参数均具有明确的物理意义,较Astrom于2000年建立的以偏微分方程形式给出的模型更具实用价值。(5)建立汽轮机回热加热系统模型,解释了凝结水节流影响机组负荷的内在原因及规律。
The thermal power will be a predominant way of electric energy supplies for years to come in China. Keeping the normal and efficient operation of thermal power units is important for realizing the indicators of energy saving and emission reducing. The state parameter detection and control optimization are the effective measures to achieve this goal. However, the thermodynamic systems in larger thermal power units are complicated, so some important state signals cannot be real-time measured or cannot be measured exactly, which is an important element affecting the control performance and safe and economic operation of units. According to the characteristics of research object, based on the flexible combination of mechanism analysis and data analysis, the main thermodynamic systems of units were researched. And some state signals, which were of explicit physical meaning, of practical application value and can reflect the real operation law of thermodynamic systems, were gained. The work can be presented as follows:
     1. The signal of coal heat value was constructed. Through analyzing the balanced relations of mass flow and energy flow in the process of electricity generation, using the pressure of the turbine first stage, calculation opening of governing valve, drum pressure and boiler feed coal flow, the signal of coal heat value was constructed. This signal has the advantages of finer dynamic performance and also has better practical value in control system engineering application.
     2. The boiler heat release signal was gained. Based on the analysis of the large amount of boiler operation data, the particular correlativity relation between boiler air flow, oxygen content in boiler flue gases and feed coal flow was discovered. And then, combining with the mechanism analysis, the cause and the law of this relation were explained, based on which, the boiler heat release signal was constructed. This signal has finer dynamic performance and enough accuracy for engineering application.
     3. The estimation signal for drum false water level was constructed. Through analyzing the balanced relations of mass flow and energy flow in boiler water-steam system, the model that can describe the real drum water level signal was constructed. This signal can reflect the change of real drum water level with finer dynamic performance, especially the false water level phenomenon. This signal can be applied in the control system optimization.
     Meanwhile, in the process of research on the above problems, some research achievements in terms of the inner operation law and theoretical study methods for thermodynamic system were gained. The main work can be presented as follows:(1) The dynamic model of positive pressure pulverizing system of medium speed mill direct firing was constructed. The change law of main parameters in the model was analyzed. And it is pointed out that the primary air rate is the key factor, which affects the inertia and delay of pulverizing system. (2) The concept of volume heat storage coefficient was presented, on the basis of which, a method for calculating the heat storage coefficient was brought forward. (3) The simplified non-linear dynamic model of furnace pressure, furnace temperature versus fuel flow and the entrance pressure of induced draft fan was constructed. And the inner interaction relation between furnace pressure and furnace average temperature was revealed. (4) The multivariable dynamic model with 3 inputs/3 outputs of drum boiler fuel, opening of governing valve and feed water flow versus unit load, throttle pressure and drum water level was constructed, which was gained in the form of differential equation. All the static parameters and dynamic parameters of this model have the explicit physical meaning. This model has more practical application value than the one of Astrom model, which was constructed in 2000 in the form of partial differential equation. (5) The model of turbine heat regenerative system was constructed, and the inner cause and law of how the condensate throttling affects units load was explained.
引文
[1]中电联,2009年全国电力工业统计快报[Z]. http://www.cec.org.cn,2010年1月7日.
    [2]中国发电,《中国能源发展报告2008》蓝皮书[Z].2008年.
    [3]宋建峰.基于信息融合技术锅炉燃烧状态检测及控制优化[D].华北电力大学硕士学位论文.北京:2010.
    [4]于良春,董丽娃.中国电力工业的地区布局和电源结构分析[J].东岳论丛,2003,24(3):63-66.
    [5]王敏芳.我国东部沿海地区电源结构优化目标[J].中国能源,2005,27(8):35-38.
    [6]陈广绢.中国发电产业节能降耗的优化模型与方法研究[D].华北电力大学博士学位论文.北京:2008.
    [7]《中国的能源状况与政策》白皮书(全文)[Z]. www.chinagate.com.cn,2007年12月26日.
    [8]张卓元.以节能减排为着力点推动经济增长方式转变[J].经济纵横,2007,8:2-6.
    [9]国家中长期科学和技术发展规划纲要(2006-2020年)[Z]. www.gov.cn,2006年02月09日.
    [10]国家自然科学基金“十一五”发展规划[Z]. http://www.nsfc.gov.cn,2006年.
    [11]彭苏萍.中国煤炭资源开发与环境保护[J].科技导报,2009,17:3-3.
    [12]刘吉臻,刘鑫屏,田亮.基于信息融合技术的燃烧控制优化系统[J].华东电力.2009,37(12):2088-2092.
    [13]史进渊,杨宇,危奇.火电厂主设备状态检修技术的研究[J].动力工程.2002,22(6):2011-2039.
    [14]Christina Athanasopoulou, Vasilis Chatziathanasiou. Intelligent system for identification and replacement of faulty sensor measurements in thermal power plants (IPPAMAS:Part 1)[J]. Expert Systems with Applications,2009,36(5)Pages:8750-8757.
    [15]Christina Athanasopoulou,Vasilis Chatziathanasiou, Ioannis Petridis. Utilizing data mining algorithms for identification and reconstruction of sensor faults:a Thermal Power Plant case study[J]. Power Tech,2007, Pages:2082-2087.
    [16]Vlado Stankovski, Werner Dubitzky. Special section:Data mining in grid computing environments[J]. Future Generation Computer Systems,2007,23, Pages:31-33.
    [17]B.J.P. Buhre, L.K. Elliott,C.D. Sheng, et al. Oxy-fuel combustion technology for coal-fired power generation[J]. Progress in energy and combustion science,2005,34 (4) Pages:283-307.
    [18]P.F. Odgaard, Babak Mataji. Fault detection in coal mills used in power plants[J]. Power Plants and Power Systems Control,2006, Pages:177-182.
    [19]Peter Fogh Odgaard, Babak Mataji. Observer-based fault detection and moisture estimating in coal mills[J]. Control Engineering Practice, Volume 16, Issue 8, August 2008, Pages:909-921.
    [20]V. Ebert, T. Fernholz, C. Giesemann, et al. Simultaneous diode-laser-based in situ detection of multiple species and temperature in a gas-fired power plant[J]. Proceedings of the Combustion Institute, Volume 28, Issue 1,2000, Pages:423-430.
    [21]T. Oikawa, M. Tomizawa, S. Degawa. New monitoring system for thermal power plants using digital image processing and sound analysis[J]. Control Engineering Practice, Volume 5, Issue 1, January 1997, Pages:75-78.
    [22]A.K. Tangirala, S.L. Shah, N.F. Thornhill PSCMAP. A new tool for plant-wide oscillation detection[J]. Journal of Process Control, Volume 15, Issue 8, December 2005, Pages:931-941.
    [23]S. Simani, C. Fantuzzi. Fault diagnosis in power plant using neural networks[J]. Information Sciences, Volume 127, Issues 3-4, August 2000, Pages: 125-136.
    [24]M. Bramanti, E.A. Salerno, A. Tonazzini, et al. An acoustic pyrometer system for tomographic thermal imaging in power plant boilers[J]. IEEE Transactions on Instrumentation and Measurement,1996,45(1) Pages:159-167.
    [25]C.M. Stoisser, S. Audebert. A comprehensive theoretical, numerical and experimental approach for crack detection in power plant rotating machinery[J]. Mechanical Systems and Signal Processing, Volume 22, Issue 4, May 2008, Pages:818-844.
    [26]Emine Ayaz. Component-wide and plant-wide monitoring by neural networks for Borssele nuclear power plant[J]. Energy Conversion and Management, Volume 49, Issue 12, December 2008, Pages:3721-3728.
    [27]K. Zhao, B. R. Upadhyaya. Adaptive fuzzy inference causal graph approach to fault detection and isolation of field devices in nuclear power plants[J]. Progress in Nuclear Energy; 2005, Vol.46, No.3-4, Pages:226-240.
    [28]Q.Chen, Hang Ruan, Yaosheng Chen, et al. Fuel combustion monitoring apparatus and method[C].15th Optical Fiber Sensors Conference Technical Digest. OFS 2002(Cat. No.02EX533),2002, vol.1, Pages:487-490.
    [29]杨超,罗自学,周怀春.变氧量工况下煤粉炉内辐射能检测特性[J].热能动力工程,2008,23(3):273-277:
    [30]初云涛,周怀春.一种考虑控制系统耦合关系的汽包锅炉简化模型与分析[J].中国电机工程学报,2007,27(35):90-95.
    [31]杨超,周怀春.一种多层辐射能信号融合处理的新算法[J].动力工程,2008,28(1):54-57,75.
    [32]娄春,周怀春.炉膛中二维温度场与辐射参数的同时重建[J].动力工程,2005,25(5):633-638.
    [33]卓旭升,周怀春,陈楠.过热蒸汽比焓和密度的双线性拟合[J].华中科技大学学报(自然科学版),2007,35(12):111-113.
    [34]方庆艳,周怀春,汪华剑,等.W火焰锅炉结渣特性数值模拟[J].中国电机工程学报,2008,28(23):1-7.
    [35]方庆艳,姚斌,江瑞宝,等.W型火焰锅炉炉内燃烧过程检测实验研究[J].热能动力工程,2005,20(4):361-364.
    [36]于达仁,范轶,徐志强.基于分布信息融合的直流锅炉燃料量信号重构[J].中国电机工程学报.2004,24(2):191-195.
    [37]于达仁,范轶,徐志强.炉膛辐射能信号和热量信号的信息融合方法[J].中国电机工程学报.2003,23(4):158-171.
    [38]于达仁,胡清华,鲍文.融合粗糙集和模糊聚类的连续数据知识发现[J].中国电机工程学报.2004,24(6):205-210.
    [39]鲍文,周瑞,李宁,等.采用非降采样第二代小波变换的信号降噪方法[J].中国电机工程学报,2008,28(20):82-87.
    [40]刘强,于达仁.高超声速飞行动态特性的特征值扰动分析[J].哈尔滨工业大学学报,2004,36(1):7-10.
    [41]鲍文,杨坤,胡清华,等.应用信息谱系图法检测火电厂的异常数据[J].动力工程,2005,25(6):865-869,906.
    [42]王伟,于达仁,赵辉,等.基于符号有向图模型的故障诊断方法[J].动力工程,2007,27(5):736-741.
    [43]原国成,于达仁,郭金光.电厂辅机振动监测系统研究[J].黑龙江电力,2007,29(2):86-90.
    [44]王伟,胡清华,于霄,等.多值属性系统的故障诊断策略最优化方法[J].仪器仪表学报,2008,29(5):1073-1078.
    [45]鲍文,于达仁,王伟,等.基于关联规则的火电厂传感器故障检测[J].中国电机工程学报,2003,23(12):170-174.
    [46]刘金福,于达仁,胡清华,等.基于加权粗糙集的代价敏感故障诊断方法[J].中国电机工程学报,2007,27(23):93-99.
    [47]于达仁,郭钰锋,王晓娟.计及回热器蓄热效应的汽轮机动态模型[J].中国电机工程学报,2005,25(14):84-88.
    [48]郭钰锋,赵晓敏,于达仁,等.用于汽轮机甩负荷动态计算的数学模型[J].汽轮机技术,2006,48(2):104-107,140.
    [49]于达仁,郭钰锋.电网一次调频能力的在线估计[J].中国电机工程学报,2004,24(3):72-76.
    [50]刘瑞兰,苏宏业,褚健.基于改进模糊神经网络的软测量建模方法[J].信息与控制,2003,32(4):367-370.
    [51]BI Zhi-yue, WANG Qing-feng, TANG Jian-zhong. Soft sensor model for dredging discharge pipeline slurry concentration measurement based on radial basis function neural network[J]. Chinese journal of sensors and actuators,2007,20(7)Pages: 1630-1634.
    [52]HOU Di-bo, ZHOU Ze-kui. Extended FNN soft sensor model for handling qualitative inputs [J]. Chinese journal of sensors and actuators,2006,19(3)Pages:895-899.
    [53]张英,苏宏业,褚健.基于模糊最小二乘支持向量机的软测量建模[J].控制与决策,2005,20(6):621-624.
    [54]刘瑞兰,牟盛静,苏宏业.基于支持向量机和粒子群算法的软测量建模[J].控制理论与应用,2006,23(6):895-899,906.
    [55]刘瑞兰,陈渭泉,苏宏业.基于改进GA.PLS算法的最优辅助变量选择及其在软测量建模中的应用[J].南京邮电大学学报(自然科学版),26(1):76-80.
    [56]梁军,汪小勇,王文庆.基于神经网络PLS方法的软测量建模研究[J].浙江大学学报(工学版),2004,38(6):676-681.
    [57]吕立华,宋执环,李平.用于过程软测量的多小波网络[J].仪器仪表学报,23(5):508-511.
    [58]苏志刚,王培红,于向军,等.中储式制粉系统出力在线监测软测量建模[J].中国电机工程学报,2007,27(29):90-95.
    [59]苏志刚,于向军,吕震中,等.灰色软测量在球磨机料位检测中的应用[J].热能动力工程,2006,21(6):578-581.
    [60]明学星,吕震中.基于混沌理论的球磨机出力特性分析[J].电力系统及其自动化学报,2008,20(2):117-120.
    [61]王秋东,徐治皋,周建新.基于数据融合的凝汽器真空应达值软测量研究[J].汽轮机技术,2008,50(4):285-288.
    [62]李军,王雷,洪军.基于多主体系统架构的锅炉汽包应力动态软测量[J].中国电机工程学报,2008,28(2):11 8-122.
    [63]周建新,王雷,吴海姬,等.基于支持向量回归的大容量机组主蒸汽流量建模[J].热能动力工程,2008,23(2):122-126.
    [64]许传龙,赵延军,黄健,等.基于人工神经网络的固相质量流量软测量研究[J].计量学报,2006,27(3):246-249.
    [65]刘颖,吕震中.基于软仪表的电站风粉浓度测量的研究[J].电站系统工程,2006,22(1):49-50,54.
    [66]周建新,王雷,徐治皋.大型电站锅炉飞灰含碳量优化模型研究[J].锅炉技术,2008,39(3): 21-24.
    [67]熊志化,邵惠鹤,张卫庆.基于支持向量机的火电厂烟气含氧量软测量[J].测控技术,2004,23(8):15-16.
    [68]张小桃,倪维斗,李政,等.基于主元分析与现场数据的过热汽温动态建模研究[J].中国电机工程学报,2005,25(5):131-136.
    [69]张小桃,倪维斗,李政,等.基于现场数据与神经网络的热工对象动态建模[J].热能动力工程,2005,20(1):34-39.
    [70]鲍文,杨坤,胡清华.应用信息谱系图法检测火电厂的异常数据[J].动力工程.2005,25(6):865-869.
    [71]倪维斗,徐向东.李政.热动力系统建模与控制的若干问题[M].北京:科学出版社.1996.
    [72]郑松,倪维斗.分布式控制系统动态重构技术研究与实现[J].原子能科学技术,2009,43(8):724-729.
    [73]张小桃,倪维斗,李政.基于现场数据的汽包压力动态建模研究与仿真[J].动力工程,2004,24(3):370-374.
    [74]倪维斗.循环流化床锅炉状态监测与故障诊断专家系统(CFBBEXPTS)的推理控制机制[J].动力工程,1998,18(3):1-1.
    [75]汪健,倪维斗.人工神经网络用于电厂故障诊断[J].动力工程,1997,17(2):7-11.
    [76]张小桃,倪维斗,李政.基于现场数据的中速磨煤机动态建模研究[J].2004,19(6):614-616,633.
    [77]刘福国.电厂入炉煤元素分析和发热量的软测量实时监测技术[J].中国电机工程学报.2005,25(6):139-146.
    [78]刘福国.煤粉炉燃烧效率工程预测模型[J].动力工程.2004,25(5):636-639.
    [79]刘福国.电站锅炉入炉煤水分实时监测的研究[J].锅炉技术,2003,34(6):12-14.
    [80]刘福国.电站锅炉入炉煤质实时监测的研究与应用报告[R].济南:山东电力研究院,2002.
    [81]刘福国,王学同,苏相河,等.基于系统测量冗余的电厂异常运行数据检测与校正[J].中国电机工程学报,2003,23(7):204-207.
    [82]刘福国,王学同.性能监测和能损诊断系统应用手册[R].济南:山东电力研究院,2002,1-15.
    [83]刘福国.ASME标准锅炉热效率计算的线算法[J].电站系统工程,2001,17(3):135-138.
    [84]刘福国,郝卫东,杨建柱,等.电厂锅炉变氧量运行经济性分析及经济氧量的优化确定[J].中国电机工程学报,2003,23(2):172-176.
    [85]阎维平,梁秀俊,周健,等.300 MW燃煤电厂锅炉积灰结渣计算机在线监测与优化吹灰[J].中国电机工程学报,2000,20(9):84-88.
    [86]阎维平,朱予东,谭蓬,等.变负荷工况下锅炉对流受热面污染的监测[J].动力工程,2007,27(1):58-61,129.
    [87]朱予东,阎维平,欧宗现.熵产分析法在锅炉吹灰优化模型中的应用[J].中国电机工程学报,2008,28(8):13-17.
    [88]陈宝康,阎维平,高正阳.300MW燃煤电站锅炉受热面优化吹灰模型的研究与实现[J].动力工程,2004,24(4):485-486.
    [89]李科,安连锁,沈国清,等.伪随机序列在声学测温中的应用研究[J].华北电力大学学报,34(6):47-50,56.
    [90]安连锁,沈国清,张波,等.电站锅炉中声学测温的实验研究[J].电站系统工程,2007,23(2):23-25.
    [91]杨祥良,安连锁,沈国清,等.单路径声学高温计实时监测锅炉炉膛烟温的试验研究[J].动力工程,29(4):379-383.
    [92]沈国清,安连锁,姜根山,等.基于声学丁重建炉膛二维温度场的仿真研究[J].中国电机工程学报,2007,27(2):11-14.
    [93]姜根山,安连锁,杨昆.温度梯度场中声线传播路径数值研究[J].中国电机工程学报,2004,24(10):210-214.
    [94]安连锁,宋志强,姜根山,等.考虑声波折射的声学锅炉温度场测量技术的研究[J].动力工程,2005,25(3):378-381.
    [95]沈国清,吴智泉,安连锁.基于少量声学数据的炉内温度场重建[J].动力工程,2007,27(5):702-706.
    [96]安连锁,沈国清,郭金鹏,等.声学技术在电厂设备状态监测中的应用研究[J].中国电力,2007,40(1):60-65.
    [97]韩中合,杨昆.凝汽式汽轮机排汽焓的简便算法与误差分析[J].汽轮机技术,2006,48(3):167-170.
    [98]安连锁,王智,韩中合.汽轮机叶栅内湿蒸汽两相凝结流动的数值研究[J].中国电机工程学报,2009,29(11):70-74.
    [99]韩中合,杨昆,田松峰,等.汽轮机油含水率在线检测方法的发展与研究[J].热力发电,2004,7:34-37.
    [100]韩中合,钱江波,杨昆,等.谐振腔微扰法测量汽轮机排汽湿度技术的关键问题[J].动力工程,2005,25(3):387-391.
    [101]韩中合,钱江波,田松峰.在线测量汽轮机排汽湿度的微波谐振腔结构优化[J].中国电机工程学报,2009,29(26):1-6.
    [102]韩中合,杨昆,田松峰.在线确定凝汽式汽轮机排汽焓的热力学方法[J].动力工程,2004,24(3):356-359,374.
    [103]朱建平.应用多元统计分析[M].北京:科学出版社,2006.
    [104]袁志发,宋世德.多元统计分析[M].北京:科学出版社,2009.
    [105]http://www.cad.zju.edu.cn/home/chenlu/pca.htm.
    [106]朱松青,史金飞.状态监测与故障诊断中的主元分析法[J].机床与液压,2007,35(1):241-243.
    [107]张昱君,刘爱伦.多尺度主元分析方法在化工过程故障检测中的应用[J].世界仪表与自动化,2005,9(12):62-64.
    [108]肖应旺,徐保国.改进PCA在发酵过程监测与故障诊断中的应用[J].控制与决策,2005,20(5):571-574.
    [109]李京茹,李平康,郑宏伟.主元分析法在火电厂过程控制中的应用[J].仪器仪表学报,2004,25(4)z:1016-1017,1036.
    [110]胡春海,王晓丽,邹晓红.D-S证据理论和粗集理论在数据融合中的应用[J].现代电力,2004,26(9):53-55.
    [111]张文修,吴伟志.粗糙集理论介绍和研究综述[J].模糊系统与数学,2000,14(4):1-12.
    [112]赵峰,苏宏升.证据理论和粗集在变电站故障诊断中的应用[J].电力系统及其自动化学报,2009,21(2):42-46.
    [113]黄鲲,陈森发,周振国,等.基于粗集理论和证据理论的多源信息融合方法[J].信息与控制,2004,33(4):422-425,433.
    [114]张书奎,崔志明,龚声蓉,等.基于Bayes序贯估计的无线传感器网络数据融合算法[J].电子与信息学报,2009,31(3):716-721.
    [115]王炯琦,周海银,吴翊.基于最优估计的数据融合理论[J].应用数学,2007,20(2):392-399.
    [116]程云鹏,肖兵.一种多传感器数据融合算法评估平台的设计[J].空军雷达学院学报,2006,20(1):11-13.
    [117]项新建.基于多传感器数据融合的粮食仓库温度监测系统[J].仪器仪表学报,2003,24(5):525-527,535.
    [118]周建新,王雷,吴海姬,等.基于支持向量回归的大容量机组主蒸汽流量建模[J].热能动力工程,2008,23(2):122-126.
    [119]刘瑞兰,牟盛静,苏宏业,等.基于支持向量机和粒子群算法的软测量建模[J].控制理论与应用,2006,23(6):895-899,906.
    [120]张英,苏宏业,褚健.基于模糊最小二乘支持向量机的软测量建模[J].控制与决策,2005,20(6):621-624.
    [121]VAPNIK V. Statistical Learning Theory[M]. New York:Springer,1998.
    [122]CORTES C. VAPNIK V. Support vector networks[J]. Machine Learning,1995,20(4): 273-297.
    [123]彭文季,罗兴镝,郭鹏程,等.基于最小二乘支持向量机和信息融合技术的水电机组振动故障诊断[J].中国电机工程学报,2007,27(23):86-92.
    [124]胡广书.数字信号处理理论、算法与实现[M].北京:清华大学出版社,1997.
    [125]刘博,彭宏,郑启伦.一种新的数据预处理算法——NLCA[J].计算机应用,2006,26(6):1406-1408.
    [126]董晓萌,曹彬婕,罗风娟,等.一种度量生物性状非线性相关性的广义相关系数[J].西北农林科技大学学报,2008,36(5):191-195.
    [127]邓集祥,赵丽丽.主导低频振荡模式二阶非线性相关作用的研究[J].中国电机工程学报,2005,25(7):75-80.
    [128]樊重俊,王浣尘,韩崇昭,等.基于分数维数的非线度及其应用[J].自动化学报,1999,25(2):145-151.
    [129]Kahn P B. Zarmi Y. Nonlinear dynamics:Exploration through normal forms[M]. New York:Wiley-Interscience, John Wiley&Sons. Inc.,1997.
    [130]M.H.海因斯著,张建华,卓力,等译.数字信号处理[M].北京:科学出版社,2003.
    [131]阎毅、黄联芬.数字信号处理[M].北京:北京大学出版社,2006.
    [132]徐以涛.数字信号处理[M].西安:西安电子科技大学出版社,2009.
    [133]陆光华,彭学愚,张林让,等.随机信号处理[M].西安:西安电子科技大学出版社,2009.
    [134]张玲玲,唐晓英,刘伟峰.一种新的变步长LMS自适应滤波算法性能分析[J].生命科学仪器,2005,3(5):39-41.
    [135]龚耀寰.自适应滤波[M].北京:电子工业出版社,2003.
    [136]袁丽英.自适应推广卡尔曼滤波算法研究[D].哈尔滨:哈尔滨理工大学控制理论与控制工程[硕士研究生学位论文],1999.
    [137]吴湘淇.信号、系统与信号处理(下)[M].北京:电子工业出版社,2002.
    [138]张文革,刘芳,高新波,等.一种自适应多尺度积阈值的图像去噪算法[J].电子与信息学报,2009,31(8):1779-1785.
    [139]周祚峰,水鹏朗.利用数学形态学和方向窗的小波域双重局部维纳滤波图像去噪算法[J].电子与信息学报,2008,30(4):885-888.
    [140]刘贵忠,邸双亮.小波分析及其应用[M].西安:西安电子科技大学出版社,2000.
    [141]李永亭,齐咏生,肖志云.基于小波变换的动态心电信号伪差识别[J].计算机工程,2009,35(18):269-271.
    [142]孙书学,吕艳新,顾晓辉,等.噪声相关条件下多传声器时延多尺度估计[J].振动与冲击,2008,27(12):160-163,174.
    [143]文成林.多尺度估计理论及其应用[M].北京:清华大学出版社,2002.
    [144]文成林.多尺度动态建模理论及其应用[M].北京:科学出版社,2008.
    [145]贝达特,皮索尔.相关分析和谱分析的工程应用[M].北京:国防工业出版社,1983.
    [146]行鸿彦,刘照泉,万明习.基于小波变换的广义相关时延估计算法[J].声学学报,2002,27(1):88-93.
    [147]田丹,田亮,刘鑫屏,等.中速磨直吹式制粉系统的动态模型[J].电力科学与工程,2008,24(9):41-44.
    [148]刘洁.一种基于炉膛压力分析的燃烧特征信号提取方法[D].河北保定:华北电力大学硕士学位论文,2010.
    [149]于希宁,刘洁,阳亮.一种基于炉膛压力分析的燃烧特征信号提取方法[J].电力科学与工程,2009,25(2):27-30.
    [150]刘鑫屏,田亮,赵征,等.汽包锅炉蓄热系数的定量分析[J].动力工程,2008,28(2):216-220.
    [151]阳亮.单元机组非线性动态模型的研究[D].河北保定:华北电力大学博士学位论文,2005.
    [152]田亮,刘鑫屏,赵征,等.一种新的热量信号构造方法及实验研究[J].动力工程, 2006,26(4):499-502.
    [153]田亮,刘鑫屏,刘吉臻.汽包锅炉负荷-压力-水位简化非线性动态模型[J].动力工程,2009,29(10):926-929,940.
    [154]刘鑫屏,田亮,曾德良,等.凝结水节流参与机组负荷调节过程建模与分析[J].华北电力大学学报,2009,36(2):80-84.
    [155]刘鑫屏,田亮,曾德良,等.基于机组负荷-压力动态模型的燃煤发热量实时计算方法[J].动力工程,2008,28(1):50-53.
    [156]田亮,曾德良,刘鑫屏,等.500MW机组简化的非线性动态模型[J].动力工程,2004,24(4):522-525.
    [157]田亮,曾德良,刘吉臻,等.简化的330MW机组非线性动态模型[J].中国电机工程学报,2004,24(8):180-184.
    [158]范从振.锅炉原理[M].北京:中国电力出版社,1986.
    [159]樊泉桂.锅炉原理[M].北京:中国电力出版社,2004.
    [160]LIU Dao-guang, LV Li-xia, LIU Chang-liang, ea al. Flame Furnace In Thermal Power Plant Condition Monitoring Using SVM[C]. Second International Conference on Intelligent Computation Technology and Automation,2009, p:67-70.
    [161]朱林忠,胡平,顾德东,等.掉焦对炉膛空气动力场热冲击的分析和计算[J].动力工程,2007,27(6):881-884,902.
    [162]刘吉臻.协调控制与给水全程控制[M].北京:中国电力出版社,1993.
    [163]刘鑫屏,田亮,刘吉臻.汽包锅炉汽包虚假水位特性研究[J].中国电机工程学报,2009,29(32):1-5.
    [164]金以慧.过程控制[M].北京:清华大学出版社,1993.