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Article(id=1236345821743076060, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345813933289655, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202405091, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1715270400000, receivedDateStr=2024-05-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772697414097, onlineDateStr=2026-03-05, pubDate=1729785600000, pubDateStr=2024-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772697414097, onlineIssueDateStr=2026-03-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772697414097, creator=13701087609, updateTime=1772697414097, updator=13701087609, issue=Issue{id=1236345813933289655, tenantId=1146029695717560320, journalId=1210938733613449225, year='2024', volume='53', issue='10', pageStart='1', pageEnd='162', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772697412236, creator=13701087609, updateTime=1772697498476, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236346175725556508, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345813933289655, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236346175725556509, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345813933289655, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=97, endPage=105, ext={EN=ArticleExt(id=1236345822087009007, articleId=1236345821743076060, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Dynamic modeling and variable load control optimization for 600 MW class ultra-supercritical coal-fired power generation units, columnId=1211002405299294959, journalTitle=Thermal Power Generation, columnName=Thermal energy science research, runingTitle=null, highlight=null, articleAbstract=

To improve the control effect of key parameters and energy conversion efficiency of ultra-supercritical coal-fired power generation units during load cycling process, 600 MW class ultra-supercritical coal-fired power generation units are taken as the research objects to carry out modeling and verification. The deviation of key thermal parameters meets the specified range of thermal power simulation standard. The spatiotemporal distribution model of internal heat storage in thermal system of coal-fired power generation units is established, and the water-fuel ratio and flue gas damper opening control logic of the feedforward internal heat storage state of the unit are proposed. The real-time heat storage state of the unit during the load cycling process is fed forward to the flow rate of feed water, coal, and flue gas damper control. The simulation results show that, when the unit load cycling rate varies from 1.0% Pe/min to 3.0%Pe/min within 40%~70% THA load range, the absolute value of the cumulative main steam temperature deviation rate decreases by 27%~31%. The average power generation standard coal consumption rate of the unit decreases by 0.37~0.65 g/(kW·h) during the transient process. The proposed control strategies improve the control accuracy of key thermal parameters and the energy conversion efficiencies of the ultra-supercritical coal-fired power generation units during load cycling transient processes.

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为提升超超临界燃煤发电机组变负荷过程关键参数控制效果及能效，以某600 MW等级超超临界燃煤发电机组为研究对象进行建模仿真，其关键热力参数偏差满足火电仿真标准规定，建立了燃煤发电机组热力系统内部蓄热分布模型，提出了前馈机组内部蓄热状态的水燃比和烟气挡板开度控制逻辑，将机组变负荷过程实时的蓄热状态前馈到给水、给煤流量和烟气挡板调控中。仿真结果表明：机组在40%THA~70%THA负荷段以1.0%Pe/min~ 3.0%Pe/min速率变负荷时，累计主蒸汽温度偏差率绝对值下降27%~31%，机组瞬态过程平均发电标准煤耗率下降0.37~0.65 g/(kW·h)。证明所提出的控制策略提升了超超临界燃煤发电机组变负荷瞬态过程关键热力参数控制精度和能量转化效率。

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王朝阳（1991），男，博士，副教授，主要研究方向为燃煤发电灵活高效运行理论与技术等，。
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刘茜（1988），女，硕士，高级工程师，主要研究方向为核能、节能降耗及自动控制等，。

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刘茜（1988），女，硕士，高级工程师，主要研究方向为核能、节能降耗及自动控制等，。

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刘茜（1988），女，硕士，高级工程师，主要研究方向为核能、节能降耗及自动控制等，。

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ArticleFig(id=1236345830689525998, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821743076060, language=EN, label=Tab.1, caption=

Main thermal parameters of the 680MW ultra-supercritical coal-fired power generation unit

, figureFileSmall=null, figureFileBig=null, tableContent=
项目主蒸汽温度/℃主蒸汽压力/MPa主蒸汽流量/(kg·s–1)再热蒸汽温度/℃再热蒸汽压力/MPa再热蒸汽流量/(kg·s–1)给水温度/℃给水压力/MPa
BMCR60526.15555.96035.87468.629829.75
75%THA60519.09360.56033.91312.627121.25
50%THA60512.62234.85902.59208.124614.69
), ArticleFig(id=1236345830827938035, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821743076060, language=CN, label=表1, caption=

超超临界680 MW燃煤发电机组主要热力参数

, figureFileSmall=null, figureFileBig=null, tableContent=
项目主蒸汽温度/℃主蒸汽压力/MPa主蒸汽流量/(kg·s–1)再热蒸汽温度/℃再热蒸汽压力/MPa再热蒸汽流量/(kg·s–1)给水温度/℃给水压力/MPa
BMCR60526.15555.96035.87468.629829.75
75%THA60519.09360.56033.91312.627121.25
50%THA60512.62234.85902.59208.124614.69
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主蒸汽压力/MPa26.0425.800.84
再热蒸汽温度/℃603.0603.40.07
再热蒸汽压力/MPa5.135.170.77
给水温度/℃290.0290.20.05
给水压力/MPa29.3929.380.03
排烟温度/℃121.0122.10.91
75%THA主蒸汽温度/℃605.0605.20.03
主蒸汽压力/MPa19.0919.100.05
再热蒸汽温度/℃603.0603.10.01
再热蒸汽压力/MPa3.913.930.51
给水温度/℃270.6272.00.52
给水压力/MPa21.2521.430.84
排烟温度/℃108.0108.70.69
40%THA主蒸汽温度/℃605.0605.90.14
主蒸汽压力/MPa10.2910.461.65
再热蒸汽温度/℃580.0585.10.86
再热蒸汽压力/MPa2.072.101.35
给水温度/℃234.2238.81.96
给水压力/MPa12.1712.021.23
排烟温度/℃93.094.21.30
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100%THA主蒸汽温度/℃605.0605.90.15
主蒸汽压力/MPa26.0425.800.84
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排烟温度/℃108.0108.70.69
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主蒸汽压力/MPa10.2910.461.65
再热蒸汽温度/℃580.0585.10.86
再热蒸汽压力/MPa2.072.101.35
给水温度/℃234.2238.81.96
给水压力/MPa12.1712.021.23
排烟温度/℃93.094.21.30
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600 MW等级超超临界燃煤发电机组动态建模与变负荷控制优化
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刘茜 1, 2 , 刘泽锋 1 , 王朝阳 1 , 刘明 1 , 严俊杰 1
热力发电 | 热能科学研究 2024,53(10): 97-105
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热力发电 | 热能科学研究 2024, 53(10): 97-105
600 MW等级超超临界燃煤发电机组动态建模与变负荷控制优化
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刘茜1, 2 , 刘泽锋1, 王朝阳1 , 刘明1, 严俊杰1
作者信息
  • 1.西安交通大学动力工程多相流国家重点实验室，陕西　西安　710049
  • 2.西安热工研究院有限公司，陕西　西安　710054

    • 刘茜（1988），女，硕士，高级工程师，主要研究方向为核能、节能降耗及自动控制等，。

通讯作者:

王朝阳（1991），男，博士，副教授，主要研究方向为燃煤发电灵活高效运行理论与技术等，。
Dynamic modeling and variable load control optimization for 600 MW class ultra-supercritical coal-fired power generation units
Xi LIU1, 2 , Zefeng LIU1, Chaoyang WANG1 , Ming LIU1, Junjie YAN1
Affiliations
  • 1.State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
  • 2.Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China
出版时间: 2024-10-25 doi: 10.19666/j.rlfd.202405091
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摘要
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为提升超超临界燃煤发电机组变负荷过程关键参数控制效果及能效，以某600 MW等级超超临界燃煤发电机组为研究对象进行建模仿真，其关键热力参数偏差满足火电仿真标准规定，建立了燃煤发电机组热力系统内部蓄热分布模型，提出了前馈机组内部蓄热状态的水燃比和烟气挡板开度控制逻辑，将机组变负荷过程实时的蓄热状态前馈到给水、给煤流量和烟气挡板调控中。仿真结果表明：机组在40%THA~70%THA负荷段以1.0%Pe/min~ 3.0%Pe/min速率变负荷时，累计主蒸汽温度偏差率绝对值下降27%~31%，机组瞬态过程平均发电标准煤耗率下降0.37~0.65 g/(kW·h)。证明所提出的控制策略提升了超超临界燃煤发电机组变负荷瞬态过程关键热力参数控制精度和能量转化效率。

燃煤发电  /  变负荷  /  汽温控制  /  瞬态节能

To improve the control effect of key parameters and energy conversion efficiency of ultra-supercritical coal-fired power generation units during load cycling process, 600 MW class ultra-supercritical coal-fired power generation units are taken as the research objects to carry out modeling and verification. The deviation of key thermal parameters meets the specified range of thermal power simulation standard. The spatiotemporal distribution model of internal heat storage in thermal system of coal-fired power generation units is established, and the water-fuel ratio and flue gas damper opening control logic of the feedforward internal heat storage state of the unit are proposed. The real-time heat storage state of the unit during the load cycling process is fed forward to the flow rate of feed water, coal, and flue gas damper control. The simulation results show that, when the unit load cycling rate varies from 1.0% Pe/min to 3.0%Pe/min within 40%~70% THA load range, the absolute value of the cumulative main steam temperature deviation rate decreases by 27%~31%. The average power generation standard coal consumption rate of the unit decreases by 0.37~0.65 g/(kW·h) during the transient process. The proposed control strategies improve the control accuracy of key thermal parameters and the energy conversion efficiencies of the ultra-supercritical coal-fired power generation units during load cycling transient processes.

coal-fired power generation  /  variable load  /  steam temperature control  /  transient energy saving
刘茜, 刘泽锋, 王朝阳, 刘明, 严俊杰. 600 MW等级超超临界燃煤发电机组动态建模与变负荷控制优化. 热力发电, 2024 , 53 (10) : 97 -105 . DOI: 10.19666/j.rlfd.202405091
Xi LIU, Zefeng LIU, Chaoyang WANG, Ming LIU, Junjie YAN. Dynamic modeling and variable load control optimization for 600 MW class ultra-supercritical coal-fired power generation units[J]. Thermal Power Generation, 2024 , 53 (10) : 97 -105 . DOI: 10.19666/j.rlfd.202405091
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在“双碳”目标的引领下，我国正在建设以新能源为主体的新型电力系统[1-3]。燃煤发电正从主体性电源向基础保障性和系统调节性电源转型[4]，长期频繁处于深度快速调峰调频等瞬态过程[5]。超临界机组是燃煤发电技术的发展趋势，装机约占煤电总装机的60%[6]，是我国燃煤发电的中流砥柱。提高超临界机组调峰调频性能是煤电面临的最为迫切的技术需求。但是燃煤机组自身大延迟、大惯性特点制约了其快速变负荷能力，亟需开展燃煤发电热力系统变负荷瞬态特性研究[7-8]，以挖掘机组自身的变负荷潜力并提高机组的负荷调节能力。
国内外学者针对燃煤发电灵活性提升展开了大量研究，建立热力系统动态模型是实现机组灵活性提升研究的理论基础。王超等[9]针对超临界直流锅炉开展了仿真建模，研究了给水温度和流量阶跃增加5%、燃料量阶跃增加5%时，机组关键热力参数的动态特性。赵征等[10]提出了综合利用凝结水节流调节和冷却工质节流调节2种快速变负荷方式以改善煤电机组变负荷性能。苏志刚等[11]建立了超临界机组动态模型，并研究了系统的动态特性。严国栋等[12]从机理分析出发建立了超临界循环流化床机组动态模型，研究了给煤系统、炉膛热量释放、汽水加热系统和汽轮机系统的动态过程。卿浩等[13]对超临界循环流化床锅炉深度调峰跨临界过程中水冷壁动态特性开展了试验研究。弓林娟[14]建立了燃煤机组动态模型，并研究了预测控制在灵活性提升中的应用。
热力系统中具有大量的蓄热，变负荷过程中蓄热量将发生变化，同时也可对内部蓄热进行利用，提升机组的变负荷能力。王玮等[15-16]研究了凝结水节流参与机组快速变负荷调节的性能，通过辨识凝结水节流通道传递函数，并融入机组变负荷过程，证明了凝结水节流的快速性和有效性。马良玉等[17-18]建立了超临界锅炉控制系统的智能控制策略，研究了凝结水节流参与的机组智能优化运行。赵永亮等[19-21]建立了超临界机组热力系统动态模型，并研究了主汽调节阀调节、给水节流、凝结水节流、高加抽汽节流、低加抽汽节流等热力系统构型调整方案下机组灵活性的提升效果。
变负荷过程中，机组的关键热力参数稳定是运行的保障。蒸汽温度控制是热工控制中的重要内容，机组在快速变负荷过程中蒸汽温度已发生大幅度波动，威胁运行安全和经济性[22]。孙立等[23-24]研究了多目标优化及直接能量平衡法对蒸汽温度的控制效果。李东海等[25]研究了过热蒸汽温度控制系统并提出了将自抗扰控制应用于燃煤发电的过热蒸汽温度控制中。马平等[26]提出了在锅炉过热蒸汽温度控制中，应用动态预测PID算法，结果表明控制效果优于传统串级PID控制，超调量小，调节过程快。超临界机组的主蒸汽温度控制是水燃比粗略调整+喷水减温精细调整，并配合烟气挡板控制，再热蒸汽温度主要依靠烟气挡板调温控制。水燃比是指进入锅炉的给水和给煤之比，主要用来控制汽水分离器出口的温度，进而为锅炉主汽参数的稳定提供保障。机组变负荷瞬态过程与其在某个负荷点稳定运行的本质差异在于其自身的蓄热状态在实时变化。传统的水燃比控制系统未充分考虑机组在变负荷过程中蓄热差异对控制参数的影响。因此，研究考虑热力系统内部蓄热状态演变规律的燃煤发电变负荷控制计算尤为重要。
为此，本文针对超超临界600 MW等级一次再热燃煤机组开展了热力系统建模与验证，研究了考虑蓄热状态演变规律的水燃比控制及烟气挡板控制，为超超临界燃煤机组调峰调频等瞬态运行的蒸汽温度控制及瞬态节能理论奠定基础。
1　超超临界燃煤机组系统建模与验证
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1.1　机组参数
本文研究对象为华能威海发电有限责任公司超超临界680 MW一次再热机组，热力系统主机分别为哈尔滨锅炉厂设计制造的HG－2001/26.15－YM3型变压运行直流锅炉和上海汽轮机厂采用西门子技术生产制造的N660-25/600/600型纯凝汽式汽轮机，图1为超超临界680 MW燃煤发电机组热力系统示意，该机组主要热力参数见表1。主蒸汽和再热蒸汽参数分别为605 ℃/26.15 MPa和603 ℃/5.87 MPa。此锅炉设计煤种的煤质分析见表2，煤的低位发热量是23 570 kJ/kg。
1.2　机组模型
本文基于GSE仿真平台建立了燃煤发电热力系统动态模型。建模思路是利用节点表征各受热面的容积，利用流线将各受热面连接形成流动网络，冷热流体流过节点并通过节点间的换热板完成换热。按照热力系统拓扑结构，完成锅炉及汽轮机模型的搭建，所搭建锅炉及汽轮机仿真模型分别如图2和图3所示。
1.3　关键参数验证
本文从稳态与瞬态2个方面验证所建模型准确性和可靠性。表3为热力系统主要热力参数稳态验证结果，本文对40%Pe~100%Pe稳态工况关键参数精度进行了验证，最大相对误差为1.96%，满足ANSI/ISA 77.20.01—2012[27]标准中规定的关键热力参数稳态误差控制在2%以内的要求。
对于瞬态过程模型可靠性与精度验证，选取负荷区段为40%Pe~70%Pe，对比机组主要参数仿真值与现场测量值，分别将机组有功功率以及主蒸汽压力无量纲化后观察其变化，结果如图4所示。由图4可见，有功功率及主蒸汽压力测量值与仿真值变化趋势一致，相对误差较小，满足ANSI/ISA 77.20.01—2012[27]标准中关键热力参数瞬态过程最大偏差控制规定。综上，本文建立燃煤发电机组热力系统动态模型具备较高仿真精度。
2　机组控制策略优化
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2.1　机组蓄热状态
燃煤发电机组热力系统内部具有大量的蓄热，机组在变负荷过程中蓄热将发生变化。本文提出在机组变负荷过程中，根据热力系统各部件的温度、压力等热力参数和各受热面蓄存工质的流量评估机组实时的蓄热状态。燃煤机组热力系统蓄热包括工质蓄热和金属蓄热，其计算公式为：
Qg,i=Mg,i(hg,i−h0)
Qm,i=Mm,icp,m(Tm,i−T0)
式中：Qg,i为第i个换热器内工质的蓄热值，kJ；Mg,i为第i个换热器内蓄存工质的质量，kg；hg,i为第i个换热器内工质的焓，kJ/kg；h0为环境温度下工质的焓，kJ/kg；cp,m为金属的定压比热容，kJ/(kg·℃)；Qm,i为第i个换热器内金属的蓄热值，kJ；Mm,i为第i个换热器内金属的质量，kg；Tm,i为第i个换热器内金属的壁温，℃；T0为环境温度，℃。
整个热力系统各受热面的总蓄热为：
Qt=∑i=1nQg,i+∑i=1nQm,i
式中：Qt为热力系统中全部换热器的蓄热值，kJ。
2.2　前馈蓄热状态的水燃比控制
为提升机组变负荷过程中关键参数控制效果，本文提出了基于考虑蓄能演变的水燃比动态调控策略。图5为水燃比控制策略优化示意，在机组原有控制策略（黑色部分）的基础上，增加了主蒸汽系统蓄热状态修正量（红色部分）。依据2.1节提出的热力系统蓄热状态计算方法，计算出不同工况下主蒸汽系统（一次蒸汽）蓄热状态，并以各稳态工况下的数据作为基础数据，进行分段线性插值，来获得研究范围的任意负荷下对应的蓄热基准值，分段线性插值函数为f5。根据现场实时测量数据，代入蓄热状态计算模型，获得实时的蓄热状态，将基准值与实时值作差，通过求导函数f6换算出蓄热状态前馈量，并与原水燃比计算量相加，得到水燃比新值。
2.3　前馈蓄热状态的烟气挡板控制
本文提出了前馈蓄热状态的烟气挡板调控策略，将低再侧烟气挡板的调控策略融入再热系统的蓄热分布特性，图6为低再侧烟气挡板控制策略。在变负荷过程中，将再热系统蓄热状态前馈到蒸汽温度控制中，可更准确地调控挡板开度。
3　结果分析
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基于建立的热力系统动态仿真模型，本文对比分析了优化前后的控制效果。在40%THA~ 70%THA负荷区段内，分别采用原有水燃比策略及蓄热修正的水燃比策略。选取变负荷速率1.0%Pe/min为例，图7和图8分别为优化前后的主蒸汽温度和再热蒸汽温度控制效果对比。
由图7可见，优化后主蒸汽温度的控制效果得到了改善，优化后的主蒸汽温度最大偏差下降1.8 ℃，且更快地达到新稳态。由图8可见，再热蒸汽温度的控制效果一定程度上得到了改善。
蓄热状态直接体现燃煤发电机组各受热面的热力状态。以机组在额定负荷工况的蓄热值为基准，记变负荷过程蓄热实时值与基准值的商为无量纲蓄热值。机组在不同工况下的无量纲蓄热有对应准稳态值。图9为变负荷瞬态过程中机组无量纲蓄热变化。由图9可见，升负荷过程中，各设备总蓄热值低于准稳态下的对应值，关键受热面的传热温差增大，变负荷瞬态过程不可逆性增加。当采用优化的控制策略后，蓄热变化与设定值更加接近，瞬态蓄热偏差得到了减小，这有利于实现瞬态节能。
为展现优化后控制策略对机组变负荷过程关键参数控制效果的提升作用，本文研究了不同调节速率时机组变负荷瞬态过程中蒸汽温度控制和运行能效等的变化。为此，本文提出了利用无量纲蒸汽温度偏差累积量表征变负荷瞬态过程中蒸汽温度的控制效果，利用平均供电煤耗量表征运行能效改善效果。
瞬态过程的无量纲蒸汽温度累积偏差计算公式为：
Xi=∫0τ0|T(τ)−Tsp(τ)Tsp(τ)|dτ
式中：τ为时间，s；τ0为瞬态过程总时间，s；Xi为无量纲蒸汽温度累积偏差；T为主蒸汽温度或再热蒸汽温度，℃；下标sp代表设定值。
瞬态过程的平均标准煤耗率计算公式为：
bs,avg=3600∫0τ0B˙dτ∫0τ0P˙dτQar,netQ0
式中：bs,avg为机组瞬态过程的平均发电煤耗率，g/(kW·h)；P˙为机组发电功率，MW；B˙为入炉煤速率，kg/s；Qar,net为燃用煤种的低位发热量，kJ/kg；Q0为标准煤的低位发热量，kJ/kg。
图10为不同变负荷过程中无量纲主蒸汽温度偏差。由图10可见，随着变负荷速率增加，无量纲主蒸汽温度偏差Xms呈逐渐增大的趋势。采用优化控制策略后，Xms明显降低。机组变负荷速率为1.0%Pe/min、2.0%Pe/min和3.0%Pe/min时，无量纲主蒸汽温度偏差值分别下降了31%、27%和28%。
图11为不同变负荷过程中的无量纲再热蒸汽温度偏差。
由图11可见，随着变负荷速率的增加，无量纲再热蒸汽温度偏差Xrh呈逐渐增大的趋势。当采用本文提出的前馈蓄热状态优化的控制策略后，Xrh明显降低。当机组变负荷速率为1.0%Pe/min、2.0%Pe/min、3.0%Pe/min时，Xrh分别下降了4%、1%和3%。
图12为不同变负荷速率下机组瞬态过程的节能量。由图12可见，在40%THA~70%THA负荷区段内，以1.0%Pe/min~3.0%Pe/min速率进行变负荷时，采用本文提出的优化控制策略后机组变负荷瞬态过程节能量达0.37~0.65 g/(kW·h)。
4　结论
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本文以华能威海发电有限责任公司6号机组为研究对象，建立了超超临界600 MW等级燃煤发电机组动态模型，并完成了关键热力参数可靠性校验，提出了前馈机组内部蓄热状态的水燃比和烟气挡板控制逻辑，提升了机组变负荷瞬态过程关键热力参数的控制精度，实现了变负荷过程能效提升，主要结论如下。
1）基于GSE仿真平台，建立了燃煤发电热力系统动态模型，开展了变负荷瞬态过程仿真模拟，发电功率和主蒸汽压力等关键参数计算值与测量值的偏差满足ANSI/ISA 77.20.01—2012标准规定范围，为本文变负荷性能研究奠定了模型基础。
2）建立了燃煤发电热力系统内部蓄热分布模型，获得了变负荷瞬态过程蓄能演变规律，发现升负荷过程中，各设备总蓄热值低于准稳态下对应值，关键受热面传热温差增大，变负荷瞬态过程不可逆性增加，进而引起变负荷过程瞬态能耗上升。
3）提出了前馈内部蓄热分布的水燃比和烟气挡板控制策略，将机组变负荷过程实时的蓄热状态分别前馈到给水和给煤的物质流调控和烟气挡板调控中，机组以1.0%Pe/min~3.0%Pe/min速率变负荷时，累计主蒸汽温度偏差率绝对值下降27%~31%，累计再热蒸汽温度偏差率绝对值下降1%~4%，变负荷过程的瞬态能耗下降0.37~0.65 g/(kW·h)。
基金
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  • 国家重点研发计划项目(2022YFB4100402)
文献
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2024年第53卷第10期
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doi: 10.19666/j.rlfd.202405091
  • 接收时间：2024-05-10
  • 首发时间：2026-03-05
  • 出版时间：2024-10-25
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  • 收稿日期：2024-05-10
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National Key Research and Development Program of China(2022YFB4100402)
国家重点研发计划项目(2022YFB4100402)
作者信息
    1.西安交通大学动力工程多相流国家重点实验室，陕西　西安　710049
    2.西安热工研究院有限公司，陕西　西安　710054

通讯作者:

王朝阳（1991），男，博士，副教授，主要研究方向为燃煤发电灵活高效运行理论与技术等，。
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species		 占总种数比例
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total species (%)		 属
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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