CN109538311A - Control performance method of real-time towards steam turbine in high-end power generating equipment - Google Patents

Control performance method of real-time towards steam turbine in high-end power generating equipment Download PDF

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CN109538311A
CN109538311A CN201811110373.9A CN201811110373A CN109538311A CN 109538311 A CN109538311 A CN 109538311A CN 201811110373 A CN201811110373 A CN 201811110373A CN 109538311 A CN109538311 A CN 109538311A
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CN109538311B (en
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赵春晖
李明超
范海东
陈积明
孙优贤
李清毅
沙万里
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Zhejiang University ZJU
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
    • F01D21/00—Shutting-down of machines or engines, e.g. in emergency; Regulating, controlling, or safety means not otherwise provided for
    • F01D21/003—Arrangements for testing or measuring

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Abstract

本发明公开了一种面向高端发电装备中汽轮机的控制性能实时监测方法。汽轮机性能指标与火电厂机组的负荷、运行参数之间存在着复杂的关系，本发明针对高端发电装备中汽轮机因参数众多、工况多变导致的控制性能监测困难的问题，运用典型变量分析提取汽轮机控制系统变量间的相关信息，再利用慢特征分析算法，提取相关信息中的动态信息。最后，结合变量的相关性和变化快慢信息构造汽轮机控制性能在线监测模型。该方法克服了大型汽轮机因变量众多、工况变化而导致的控制性能监测困难的问题，大大提高了动态过程控制性能在线监测的准确度，有助于火电厂对汽轮机控制系统进行有效及时的监测，对保证高端发电装备的安全可靠运行意义重大。The invention discloses a real-time monitoring method for the control performance of a steam turbine in high-end power generation equipment. There is a complex relationship between the performance index of the steam turbine and the load and operating parameters of the thermal power plant unit. The present invention aims at the problem of difficulty in monitoring the control performance of the steam turbine in high-end power generation equipment due to numerous parameters and changing working conditions. The correlation information between the variables of the steam turbine control system is used to extract the dynamic information in the relevant information by using the slow feature analysis algorithm. Finally, the on-line monitoring model of steam turbine control performance is constructed based on the correlation of variables and the speed of change information. This method overcomes the difficulty of monitoring the control performance of large-scale steam turbines due to numerous variables and changes in operating conditions, greatly improves the accuracy of online monitoring of dynamic process control performance, and helps thermal power plants to effectively and timely monitor the steam turbine control system. It is of great significance to ensure the safe and reliable operation of high-end power generation equipment.

Description

Control performance method of real-time towards steam turbine in high-end power generating equipment
Technical field
The invention belongs to thermoelectricity Process Control System performance monitoring fields, more particularly to steamer in high-end power generating equipment The on-line performance monitoring method of machine operation correlation information and multidate information.
Background technique
Control system occupies very important status, the quality of production, safe operation, object in the industrial process of modernization The index that energy consumption etc. influences economic benefit is all direct or indirect related with the performance of control system.In actual production process In, often performance is good at the initial stage of coming into operation for control system, but after operation a period of time, due to the abrasion of equipment, determines Phase maintenance and the maintenance reasons such as not in time, may cause the performance decline of control system, control performance variation will have a direct impact on life Yield and quality causes economic benefit to be lost, if therefore causing fault in production, can also be related to the life security even enterprise, society of people The property safety of industry, brings great threat.Torrres et al. is to 2004-2005 Brazilian 12 factories (petrochemical industry, papermaking, water Mud, steel, mining etc.), more than 700 control loops are tested, and the valve wear in 14% circuit is excessive as the result is shown, 15% valve is there are braking problems, and there are serious problem of tuning, 24% controller output has saturation in 16% circuit Phenomenon, 41% circuit because the problem of problem of tuning, coupling, disturbance and actuator and there are oscillatory occurences.
In addition, a production process might have thousands of control loop collective effects in actual production, Two rectifying production equipments in Eastman chemical company possess much 14000 control loops, in HVAC production process, The quantity of its control loop can even reach 100,000.High-end power generating equipment complexity with higher, is embodied in rule Mould is huge, equipment is numerous, parameter is diversified and influences each other etc..In addition, large-scale generating set, scene has height The features such as temperature, high pressure and strong noise.
Control performance evaluation and monitoring technology are an emerging important technologies of process control field, it can utilize and set Standby day-to-day operation data, the variation of real-time monitoring monitoring system control performance make EARLY RECOGNITION to the problem of control system And optimization.For generating set, since the power load in electric system often changes, in order to maintain active power flat Weighing apparatus keeps system frequency to stablize, and the department of generating electricity is needed to change the power output of generator accordingly to adapt to the variation of power load, I.e. the operating condition of generating set is not stablized constant.But the evaluation of existing control performance and monitoring method such as principal component analysis, Partial Least Squares, Fei Sheer discriminant analysis are all based on the ideal assuming lower progress of stable conditions, therefore, are used In large-scale thermal power machine group steam turbine control system performance monitoring, good monitoring effect can not be obtained.
Summary of the invention
Steam turbine is steam continuously to flow as working medium, and the thermal energy of steam is converted to a kind of rotation of mechanical energy Formula Thermal Motor.Generally it is made of jet pipe (or stator blade), movable vane piece, impeller, arbor bearing and cylinder etc..Steamer equipment There are revolving speed height, the reliable, single-machine capacity that runs smoothly to can be made into very big (more than 1000MW) and convenient for directly coupling with generator The advantages that, but whole structure is complicated, has very strict requirements to design, manufacture, installation, operation and service technique, and required Configure the boiler of corresponding steam parameter and capacity.Except be widely used as in the modern times, large-size thermal power plant, nuclear power station and large size Outside the major engine on naval vessel, it may also be used for the integrated complexs such as large-size chemical or steel.
It is an object of the invention to be directed to large-scale thermal power machine group steam turbine because parameter is numerous, structure is complicated, operating condition is more The difficult problem of the monitoring of control performance caused by becoming extracts steamer with slow signature analysis blending algorithm with canonical variable analysis Relevant information between machine control system variable and variation speed information, overcome that large-size steam turbine dependent variable is numerous, operating condition variation Caused by the difficult problem of control performance monitoring.
The purpose of the present invention is be achieved through the following technical solutions: the control performance of steam turbine is real in high-end power generating equipment When monitoring method, method includes the following steps:
(1) obtain training data: the control system for setting steam turbine has J measurand and performance variable, adopts each time The observation vector y of available J × 1 of samplek, wherein subscript k is time index, samples the data obtained after n times and is expressed as One two-dimensional observation matrixThe measurand is that steam turbine operation in the process can Measured state parameter, including bearing metal temperature, bearing axial vibration, generator active power, steam turbine revolving speed, excitation Three-phase temperature etc.；The performance variable include condensing water flow (three minutes average), feed pressure, feedwater flow, feeder to Coal amount etc.；Training data should choose the sampled data of steam turbine under normal operating conditions.
(2) the timing relevant information of data is extracted using CVA algorithm, which is realized by following sub-step:
(2.1) timing expands building matrix and matrix in future in the past: in specific sampling instant k, by observation vector ykTo P step is expanded before k generates observation vector in the pastF step is expanded after to k Generate observation vector in futureAgain to yp,k, yf,kCarry out equalization processing:
Wherein: mean (yp,k) indicateMean value, mean (yf,k) indicateMean value.
Respectively with all past observation vectors and observation vector in future building past observing matrix YpSquare is observed with future Battle array Yf:
Wherein, M=N-f-p+1, p, f are two Delay parameters, enable p=f, value can pass through sample autocorrelation function To determine:
Wherein: autocorr (Yj, p) and representing matrix YpThe auto-correlation coefficient of j-th column vector and its time lag p；
(2.2) it constructs Hankel matrix: calculating the covariance matrix ∑ of matrix and matrix in future in the pastpp, ∑ffAnd he Cross-covariance ∑fp, recycle covariance and Cross-covariance to construct Hankel matrix H:
(2.3) singular value decomposition: carrying out the available Jp group canonical variable of singular value decomposition to Hankel matrix and match, With (ai TYp,bi TYf) indicate i-th group of canonical variable pairing, ai T、bi TIndicate the related coefficient between i-th group of canonical variable pairing:
H=UDVT (6)
U and V is respectively singular vector ui, viThe orthogonal matrix of composition, D are singular value matrix, and the singular vector in U, V is only It is related in pairs, and correlation size is by i-th of singular value γ corresponding in DiCharacterization.Bigger (the γ of singular value1>γ2>…> γJp), the correlation between canonical variable is bigger.
(2.4) it calculates transformation matrix and extracts canonical variable and residual error variable: interception matrixPreceding r Column, the matrix after generating dimensionality reductionVrStill remain most of timing relevant information.Wherein, the size of r value can To be determined by following criterion:
Cr expressiveness value, β are judgment threshold, β=0.5.
By VrCalculate canonical variable transition matrix C and residual error variable transition matrix L:
Recycle the available canonical variable space Z and residual error space E of transition matrix:
Column vector z in Z, Εk∈ r × 1, εkJp × 1 ∈ is illustrated respectively in the canonical variable of sampling instant k and residual error becomes Amount；Row vector z in Z, Εt, εtSame variable is contained in the timing information of different moments.
(3) canonical variable space is extracted respectively using slow feature analysis al (Slow Feature Analysis, SFA) Slow feature s in Z and residual error space ΕZ, sE.To extract slow feature s in the Z of canonical variable spaceZFor, this method key step It is as follows:
(3.1) data normalization: canonical variable space Z is standardized by variable, calculation formula is as follows:
ztTime series vector of the same variable of table in different moments, mean (zt) indicate ztMean value, std (zt) indicate zt's Standard deviation.
(3.2) output signal of the Z after projection is sZj, sZjIndicate sZJ-th of slow characteristic sequence.Consider linear conditions Under,Indicate coefficient vector, this is equivalent to searching one and mentions from normalized input signal Z Take slow characteristic signal sZ=[sZ1 T, sZ2 T..., sZr T]TTransition matrixI.e. sZ=WZZ.Slow characteristic signal sZjThe objective function and constraint condition to be met are as follows:
Objective function:
Constraint condition are as follows:
Wherein:Indicate slow characteristic signal sZTiming Difference, operation<>is expressed ast1, t0Respectively indicate time bound.
(3.3) albefaction: singular value decomposition is utilized, to covariance matrix < ZZ of input dataT> carry out whitening processing can be with The correlation in data is removed, the slow characteristic value extracted is made to carry different information:
Wherein: ΛZ -1/2BTFor whitening matrix, ΟZFor the input signal after corresponding albefaction.
(3.4) transition matrix W is calculatedZ: to input matrix OZIt does difference processing and obtains Timing Difference signalIt can demonstrate,prove It is bright, it is rightCovariance matrixAfter carrying out singular value decomposition, a series of obtained singular value ωZjAs formula (12) target function value described in
WZ=P ΛZ -1/2BT (17)
Slow feature s in the residual error space ΕEExtracting method and above-mentioned canonical variable space Z in slow feature sZMention Take method identical.
(4) slow feature s is dividedZ: most slow feature corresponds to the smallest characteristic value, by the ascending arrangement of characteristic value, and according to Preceding l feature is divided into s according to characteristic value sizeZThe slower feature of middle variation, uses sZ,dIt indicates；(r-l) a feature is drawn by after It is divided into sZThe middle faster feature of variation, uses sZ,eIt indicates.The determination method of partitioning standards l is, first with slow characteristic value sZChange Changing speed indicates process variableVariation speed:
Wherein: rjiFor matrix RZThe element that middle jth row i-th arranges, sZiIndicate that i-th of slow characteristic sequence, Δ () indicate meter Calculate a kind of operation of the slow degree of sequence variation:
The also big feature of degree slower than input data will be slowly spent in the slow characteristic value extracted is divided into fast feature, One shared MeA such fast feature:
Here card { } indicates element number in set { }.The M determined according to formula (19)eValue, it is corresponding by matrix ΩZ It is also divided into two parts:
(5) calculate Monitoring Indexes: since first sample point in canonical variable space, each sample point can be with Obtain one group of Monitoring Indexes (SZ,d 2, SZ,e 2)。
(6) it determines the control limit based on Monitoring Indexes: using the method for Density Estimator, first estimating dynamic and supervise Survey index SZ,d 2Probability density function p (x), for give level of significance α, SZ,d 2Control limit SZ,d 2 UCLCalculation Are as follows:
S can be calculated in the same wayZ,e 2Control limit SZ,e 2 UCL。
(7) step (6) the method is arrived according to step (3), extracts the slow feature s of residual error space ΕEAnd by sEIt is divided into Two parts sE,d, sE,e, establish monitoring index SE,d 2, SE,e 2And calculate control limit SE,d 2 UCL, SE,e 2 UCL.With to canonical variable space The processing mode of Z is identical, repeats no more.
(8) Online Monitoring Control performance: CVA-SFA model, step (5) based on step (2) to (4) foundation arrive step (7) performance state of resulting four monitoring and statistics amounts on-line monitoring steam turbine control system, the step by following sub-step Lai It realizes:
(8.1) new online data and new data pretreatment are obtained: collecting one section of new observation dataAfterwards, wherein following table new indicates new observation data, will first, in accordance with step (2) YnewExpansion becomes the past matrix, and is standardized according to the mean value and standard deviation that obtain in step (2) to past matrix Obtain Ypnew。
(8.2) canonical variable and residual error variable of new observation data are extracted: true using step (2) after standardization Fixed transition matrix VrThe canonical variable space Z of new observation data is calculated with LnewWith residual error space Enew。
(8.3) the canonical variable space Z of new observation data is extractednewIn slow feature: first, in accordance in step (3.1) really Fixed mean value and variance is to ZnewIt is standardized, utilizes the slow Feature Conversion matrix W determined in step (3.4) laterZ, Extract standardization ZnewSlow feature sZnew, and according to division parameter before by sZnewIt is divided into sZ,d newAnd sZ,e new, together Sample is according to WEAvailable Enew, further obtain sE,d newAnd sE,e new。
(8.4) new monitoring and statistics index is calculated: according to the calculating side determined in the model of foundation and step (5) (7) Method calculates the monitoring and statistics index S under canonical variable spaceZ,d 2 new, SZ,e 2 newWith residual error space monitoring index SE,d 2 new, SE,e 2 new:
(8.5) judge steam turbine control performance state online: relatively four monitoring indexes and its respective statistics are controlled in real time System limit shows that control system works normally if four monitoring indexes are all located within Statisti-cal control limit；If there is one or more Monitoring index is limited beyond normal control, shows that control system has unusual condition.
The beneficial effects of the present invention are: the present invention is for large-scale thermal power machine group steam turbine because parameter is numerous, structure Problem complicated, the changeable caused control performance monitoring of operating condition is difficult, proposes one kind towards steam turbine in high-end power generating equipment Control performance method of real-time, this method with canonical variable analysis extract steam turbine control system variable between correlation Information recycles slow feature analysis al, extracts the multidate information in relevant information and residual information.Finally, in conjunction with variable Correlation and variation speed information structuring steam turbine control performance on-line monitoring model.The method overcome large-size steam turbine because The difficult problem of control performance monitoring, substantially increases dynamic process control performance and exists caused by variable is numerous, operating condition changes The accuracy of line monitoring, facilitates thermal power plant and is effectively and timely monitored to steam turbine control system, help to ensure that large size The safe and reliable operation of generating set, while reaching the production requirement for improving its productivity effect.
Detailed description of the invention
Fig. 1 is the flow chart of the invention towards the control performance method of real-time of steam turbine in high-end power generating equipment, (a) it is off-line modeling process flow diagram flow chart, is (b) on-line monitoring process flow diagram flow chart；
Fig. 2 is the result figure that CVA-SFA method of the present invention is used for statistical process monitoring, and (a) is that monitoring is tied under normal circumstances Fruit figure is (b) monitoring result figure under abnormal conditions.
Specific embodiment
With reference to the accompanying drawing and specific example, invention is further described in detail.
For the present invention by taking subordinate Jia Hua power plant, Zhe Neng group #5 power generator turbine as an example, the power of the unit is 600,000 thousand Watt, it is large-scale thermal power machine group, including 60 process variables, including bearing metal temperature, bearing axial vibration, generator Active power, excitation three-phase temperature, condensing water flow (three minutes average), feed pressure, feedwater flow, gives coal at steam turbine revolving speed Machine coal-supplying amount etc. and some valve openings.
It should be understood that the present invention is not limited to the thermoelectricity power generation process of examples detailed above, all technologies for being familiar with this field Personnel can also make equivalent modifications or replacement without prejudice to the invention, these equivalent variation or replacement are wrapped Containing within the scope defined in the claims of this application.
As shown in Figure 1, the present invention is a kind of control performance real-time monitoring side towards steam turbine in high-end power generating equipment Method, comprising the following steps:
(1) obtain training data: the control system for setting steam turbine has J measurand and performance variable, adopts each time The observation vector y of available J × 1 of samplek, wherein subscript k is time index, samples the data obtained after n times and is expressed as One two-dimensional observation matrixIn this example, the sampling period is 10 minutes, totally 4566 A sample, 60 process variables, surveyed variable are the flow during steam turbine operation, vibration, temperature, pressure, valve opening Deng；
(2) the timing relevant information of data is extracted using CVA algorithm, which is realized by following sub-step:
(2.1) timing expands building matrix and matrix in future in the past: in specific sampling instant k, by observation vector ykTo P step is expanded before k generates observation vector in the pastF step is expanded after to k Generate observation vector in futureAgain to yp,k, yf,kCarry out equalization processing:
Wherein: mean (yp,k) indicateMean value, mean (yf,k) indicateMean value.
Respectively with all past observation vectors and observation vector in future building past observing matrix YpSquare is observed with future Battle array Yf:
Wherein, M=N-f-p+1, p, f are two Delay parameters, enable p=f, value can pass through sample autocorrelation function To determine:
Wherein: autocorr (Yj, p) and representing matrix YpThe auto-correlation coefficient of j-th column vector and its time lag p；
(2.2) it constructs Hankel matrix: calculating the covariance matrix ∑ of matrix and matrix in future in the pastpp, ∑ffAnd he Cross-covariance ∑fp, recycle covariance and Cross-covariance to construct Hankel matrix H:
(2.3) singular value decomposition: carrying out the available Jp group canonical variable of singular value decomposition to Hankel matrix and match, With (ai TYp,bi TYf) indicate i-th group of canonical variable pairing, ai T、bi TIndicate the related coefficient between i-th group of canonical variable pairing:
H=UDVT (6)
U and V is respectively singular vector ui, viThe orthogonal matrix of composition, D are singular value matrix, and the singular vector in U, V is only It is related in pairs, and correlation size is by i-th of singular value γ corresponding in DiCharacterization.Bigger (the γ of singular value1>γ2>…> γJp), the correlation between canonical variable is bigger.
(2.4) it calculates transformation matrix and extracts canonical variable and residual error variable: interception matrixPreceding r Column, the matrix after generating dimensionality reductionVrStill remain most of timing relevant information.Wherein, the size of r value can To be determined by following criterion:
Cr expressiveness value, β are judgment threshold, β=0.5.
By VrCalculate canonical variable transition matrix C and residual error variable transition matrix L:
Recycle the available canonical variable space Z and residual error space E of transition matrix:
Column vector z in Z, Εk∈ r × 1, εkJp × 1 ∈ is illustrated respectively in the canonical variable of sampling instant k and residual error becomes Amount；Row vector z in Z, Εt, εtSame variable is contained in the timing information of different moments.
(3) canonical variable space is extracted respectively using slow feature analysis al (Slow Feature Analysis, SFA) Slow feature s in Z and residual error space ΕZ, sE.To extract slow feature s in the Z of canonical variable spaceZFor, this method key step It is as follows:
(3.1) data normalization: canonical variable space Z is standardized by variable, calculation formula is as follows:
ztTime series vector of the same variable of table in different moments, mean (zt) indicate ztMean value, std (zt) indicate zt's Standard deviation.
(3.2) output signal of the Z after projection is sZj, sZjIndicate sZJ-th of slow characteristic sequence.Consider linear conditions Under,Indicate coefficient vector, this is equivalent to searching one and mentions from normalized input signal Z Take slow characteristic signal sZ=[sZ1 T, sZ2 T..., sZr T]TTransition matrix That is sZ=WZZ.Slow characteristic signal sZjThe objective function and constraint condition to be met are as follows:
Objective function:
Constraint condition are as follows:
Wherein:Indicate slow characteristic signal sZTiming Difference, operation<>is expressed ast1, t0Respectively indicate time bound.
(3.3) albefaction: singular value decomposition is utilized, to covariance matrix < ZZ of input dataT> carry out whitening processing can be with The correlation in data is removed, the slow characteristic value extracted is made to carry different information:
Wherein: ΛZ -1/2BTFor whitening matrix, ΟZFor the input signal after corresponding albefaction.
(3.4) transition matrix W is calculatedZ: to input matrix OZIt does difference processing and obtains Timing Difference signalIt can demonstrate,prove It is bright, it is rightCovariance matrixAfter carrying out singular value decomposition, a series of obtained singular value ωZjAs formula (12) target function value described in
WZ=P ΛZ -1/2BT (17)
Slow feature s in the residual error space ΕEExtracting method and above-mentioned canonical variable space Z in slow feature sZMention Take method identical.
(4) slow feature s is dividedZ: most slow feature corresponds to the smallest characteristic value, by the ascending arrangement of characteristic value, and according to Preceding l feature is divided into s according to characteristic value sizeZThe slower feature of middle variation, uses sZ,dIt indicates；(r-l) a feature is drawn by after It is divided into sZThe middle faster feature of variation, uses sZ,eIt indicates.The determination method of partitioning standards l is, first with slow characteristic value sZChange Changing speed indicates process variableVariation speed:
Wherein: rjiFor matrix RZThe element that middle jth row i-th arranges, sZiIndicate that i-th of slow characteristic sequence, Δ () indicate meter Calculate a kind of operation of the slow degree of sequence variation:
The also big feature of degree slower than input data will be slowly spent in the slow characteristic value extracted is divided into fast feature, One shared MeA such fast feature:
Here card { } indicates element number in set { }.The M determined according to formula (19)eValue, it is corresponding by matrix ΩZ It is also divided into two parts:
(5) calculate Monitoring Indexes: since first sample point in canonical variable space, each sample point can be with Obtain one group of Monitoring Indexes (SZ,d 2, SZ,e 2)。
(6) it determines the control limit based on Monitoring Indexes: using the method for Density Estimator, first estimating dynamic and supervise Survey index SZ,d 2Probability density function p (x), for give level of significance α, SZ,d 2Control limit SZ,d 2 UCLCalculation Are as follows:
S can be calculated in the same wayZ,e 2Control limit SZ,e 2 UCL。
(7) step (6) the method is arrived according to step (3), extracts the slow feature s of residual error space ΕEAnd by sEIt is divided into Two parts sE,d, sE,e, establish monitoring index SE,d 2, SE,e 2And calculate control limit SE,d 2 UCL, SE,e 2 UCL.With to canonical variable space The processing mode of Z is identical, repeats no more.
(8) Online Monitoring Control performance: CVA-SFA model, step (5) based on step (2) to (4) foundation arrive step (7) resulting four monitoring and statistics amounts can monitor the performance state of steam turbine control system on-line.The step is by following sub-step It is rapid to realize:
(8.1) new online data and new data pretreatment are obtained: collecting one section of new observation dataAfterwards, wherein following table new indicates new observation data, will first, in accordance with step (2) YnewExpansion becomes the past matrix, and is standardized according to the mean value and standard deviation that obtain in step (2) to past matrix Obtain Ypnew.In this example, new data shares two parts, and data one are the data acquired under nominal situation, and the sampling period is 10 points Clock, totally 2271 samples, 60 process variables, data two are to be abnormal the data recorded under operating condition, and the sampling period is 10 points Clock, totally 2563 samples, 60 process variables, surveyed variable are flow during steam turbine operation, vibration, temperature, turn Speed, electric current etc.；
(8.2) canonical variable and residual error variable of new observation data are extracted: true using step (2) after standardization Fixed transition matrix VrThe canonical variable space Z of new observation data is calculated with LnewWith residual error space Enew。
(8.3) the canonical variable space Z of new observation data is extractednewIn slow feature: first, in accordance in step (3.1) really Fixed mean value and variance is to ZnewIt is standardized, utilizes the slow Feature Conversion matrix W determined in step (3.4) laterZ, Extract standardization ZnewSlow feature sZnew, and according to division parameter before by sZnewIt is divided into sZ,d newAnd sZ,e new, together Sample is according to WEAvailable Enew, further obtain sE,d newAnd sE,e new。
(8.4) new monitoring and statistics index is calculated: according to the calculating side determined in the model of foundation and step (5) (7) Method calculates the monitoring and statistics index S under canonical variable spaceZ,d 2 new, SZ,e 2 newWith residual error space monitoring index SE,d 2 new, SE,e 2 new:
(8.5) judge steam turbine control performance state online:
Relatively four monitoring indexes and its respective Statisti-cal control limit in real time, if four monitoring indexes are all located at statistics control Within system limit, show that control system works normally；
If there is one or more monitoring index to limit beyond normal control, show that control system has unusual condition.
In Fig. 2 (a), in four groups of statistics and corresponding control line, the statistic only put individually has been more than control line, is being set Under conditions of believing horizontal α=0.05, it is believed that new floor data be it is normal, i.e., control system is acted normally；Fig. 2 (b) In, four groups of statistic SZ,d 2, SZ,e 2, SE,d 2, SE,e 2In the 1330th-1430,1808-1825,1943-1972,2093-2128 Four sections obviously exceed threshold line, statistic QkThe shape that transfinites is maintained always after the 326th sampled point or so obviously transfinites for the first time State, may determine that has abnormal generation in these period controlling systems accordingly, at this moment can use fault diagnosis side appropriate Method, such as contribution drawing method analysis isolate possible failure variable.
The present invention extracts the relevant information between steam turbine control system variable with canonical variable analysis, recycles slow special Parser is levied, the behavioral characteristics in relevant information are extracted, can be made by the feature that this method is extracted with the adjusting of response controller With.Finally, in conjunction with the correlation and variation speed information structuring steam turbine control performance on-line monitoring model of variable, this method It overcomes that large-size steam turbine dependent variable is numerous, the difficult problem of control performance monitoring caused by operating condition variation, substantially increases The accuracy of dynamic process control performance on-line monitoring facilitates thermal power plant and is effectively and timely monitored to turbine system, It helps to ensure that the safe and reliable operation of large-scale thermal power machine group, while reaching the production requirement for improving its productivity effect.

Claims (1)

1.一种面向高端发电装备中汽轮机的控制性能实时监测方法，其特征在于，该方法包括以下步骤:1. a real-time monitoring method for the control performance of steam turbines in high-end power generation equipment, is characterized in that, the method comprises the following steps: (1)获取训练数据：设汽轮机的控制系统具有J个测量变量和操作变量，每一次采样可以得到一个J×1的观测向量yk，其中下标k为时间指标，采样N次后得到的数据表述为一个二维观测矩阵所述测量变量为汽轮机运行过程中可被测量的状态参数，包括轴承金属温度、轴承轴向振动、发电机有功功率、汽机转速、励磁三相温度等；所述操作变量包括凝结水流量(三分钟平均)、给水压力、给水流量、给煤机给煤量等；训练数据应当选取汽轮机在正常运行状态下的采样数据。(1) Obtaining training data: Assuming that the control system of the steam turbine has J measurement variables and operating variables, a J×1 observation vector y k can be obtained for each sampling, where the subscript k is the time index, which is obtained after sampling N times. The data is represented as a two-dimensional observation matrix The measured variables are state parameters that can be measured during the operation of the steam turbine, including bearing metal temperature, bearing axial vibration, generator active power, turbine speed, excitation three-phase temperature, etc.; the operating variables include condensate flow (three (minute average), feed water pressure, feed water flow, coal feed volume of the coal feeder, etc.; the training data should be the sampling data of the steam turbine under normal operation. (2)利用CVA算法提取数据的时序相关信息，该步骤通过以下子步骤实现：(2) Utilize the CVA algorithm to extract the time-series related information of the data, and this step is realized by the following sub-steps: (2.1)时序拓展构建过去矩阵与将来矩阵：在特定的采样时刻k，将观测向量yk向k之前拓展p步生成过去观测向量向k之后拓展f步生成将来观测向量再对yp,k，yf,k进行均值化处理：(2.1) Time series expansion to construct the past matrix and the future matrix: at a specific sampling time k, expand the observation vector y k to p steps before k to generate the past observation vector Extend f steps beyond k to generate future observation vectors Then perform mean processing on y p,k , y f,k : 其中：mean(yp,k)表示的均值，mean(yf,k)表示的均值。Among them: mean(y p,k ) means The mean of , mean(y f,k ) represents mean value of . 分别用所有的过去观测向量和将来观测向量构建过去观测矩阵Yp和将来观测矩阵Yf：Construct past observation matrix Y p and future observation matrix Y f with all past observation vectors and future observation vectors, respectively: 其中，M＝N-f-p+1，p,f为两类时滞参数，令p＝f，其值可以通过样本自相关函数来确定：Among them, M=N-f-p+1, p, f are two types of time delay parameters, let p=f, and its value can be determined by the sample autocorrelation function: 其中：autocorr(Yj,p)表示矩阵Yp第j个列向量与其时滞p的自相关系数；where: autocorr(Y j , p) represents the autocorrelation coefficient between the jth column vector of matrix Y p and its time lag p; (2.2)构建Hankel矩阵：计算过去矩阵和将来矩阵的协方差矩阵∑pp，∑ff以及他们的互协方差矩阵∑fp，再利用协方差与互协方差矩阵构建Hankel矩阵H：(2.2) Constructing the Hankel matrix: Calculate the covariance matrices ∑ pp , ∑ ff and their cross-covariance matrices ∑ fp of the past and future matrices, and then use the covariance and cross-covariance matrices to construct the Hankel matrix H: (2.3)奇异值分解：对Hankel矩阵进行奇异值分解可以得到Jp组典型变量配对，用(ai TYp,bi TYf)表示第i组典型变量配对，ai T、bi T表示第i组典型变量配对间的相关系数：(2.3) Singular value decomposition: The singular value decomposition of the Hankel matrix can obtain the pairing of typical variables in the Jp group, and (a i T Y p , b i T Y f ) represents the pairing of the i-th group of typical variables, a i T , b i T represents the correlation coefficient between pairs of canonical variables in group i: H＝UDVT (6)H=UDV T (6) U和V分别为奇异向量ui，vi组成的正交矩阵，D为奇异值矩阵，U、V中的奇异向量只成对相关，且相关性大小由D中对应的第i个奇异值γi表征。奇异值越大(γ1>γ2>…>γJp)，典型变量间的相关性越大。U and V are orthogonal matrices composed of singular vectors ui and v i respectively, D is a matrix of singular values, the singular vectors in U and V are only correlated in pairs, and the magnitude of the correlation is determined by the corresponding i-th singular value in D γi characterization. The larger the singular value (γ 1 >γ 2 >…>γ Jp ), the greater the correlation between canonical variables. (2.4)计算变换矩阵并提取出典型变量和残差变量：截取矩阵的前r列，生成降维后的矩阵Vr仍保留了大部分时序相关信息。其中，r值的大小可以通过以下准则确定：(2.4) Calculate the transformation matrix and extract the typical variables and residual variables: interception matrix The first r columns of , generate the matrix after dimensionality reduction V r still retains most of the timing related information. Among them, the size of the r value can be determined by the following criteria: Cr表示准则值，β为判断阈值，β＝0.5。Cr represents the criterion value, β is the judgment threshold, and β=0.5. 由Vr计算典型变量转换矩阵C和残差变量转换矩阵L：Compute the canonical variable transformation matrix C and the residual variable transformation matrix L from V r : 再利用转换矩阵可以得到典型变量空间Z和残差空间E：The typical variable space Z and residual space E can be obtained by using the transformation matrix: Z，Ε中的列向量zk∈r×1，εk∈Jp×1分别表示在采样时刻k的典型变量和残差变量；Z，Ε中的行向量zt，εt包含了同一变量在不同时刻的时序信息。The column vectors z k ∈ r×1 and ε k ∈ Jp×1 in Z,E represent the typical variables and residual variables at sampling time k, respectively; the row vectors z t and ε t in Z, E contain the same variable Timing information at different times. (3)利用慢特征分析算法(Slow Feature Analysis，SFA)分别提取典型变量空间Z和残差空间Ε中的慢特征sZ，sE。典型变量空间Z中慢特征sZ的提取方法如下：(3) The slow feature analysis algorithm (Slow Feature Analysis, SFA) is used to extract the slow features s Z and s E in the typical variable space Z and the residual space E respectively. The extraction method of the slow feature s Z in the canonical variable space Z is as follows: (3.1)数据标准化：对典型变量空间Z按变量进行标准化处理，计算公式如下：(3.1) Data standardization: standardize the typical variable space Z according to the variables, and the calculation formula is as follows: zt表同一变量在不同时刻的时序向量，mean(zt)表示zt的均值，std(zt)表示zt的标准差。z t represents the time series vector of the same variable at different times, mean(z t ) represents the mean of z t , and std(z t ) represents the standard deviation of z t . (3.2)Z经过投影后的输出信号为sZj，sZj表示sZ第j个慢特征序列。考虑线性条件下， 表示系数向量，这等价于寻找一个从标准化输入信号Z中提取慢特征信号sZ＝[sZ1 T，sZ2 T，...，sZr T]T的转换矩阵即sZ＝WZZ。慢特征信号sZj要满足的目标函数及约束条件为：(3.2) The output signal of Z after projection is s Zj , and s Zj represents the jth slow feature sequence of sZ. Considering the linear condition, represents the coefficient vector, which is equivalent to finding a transformation matrix that extracts the slow eigensignal s Z = [s Z1 T , s Z2 T , ..., s Zr T ] T from the normalized input signal Z That is, s Z =W Z Z. The objective function and constraints to be satisfied by the slow feature signal s Zj are: 目标函数：Objective function: 约束条件为：The constraints are: 其中：表示慢特征信号sZ的时序差分，运算<·>表示为t1，t0分别表示时间上下限。in: Represents the temporal difference of the slow characteristic signal s Z , and the operation <·> is expressed as t 1 , t 0 represent the upper and lower time limits, respectively. (3.3)白化：利用奇异值分解，对输入数据的协方差矩阵<ZZT>进行白化处理可以去除数据中的相关性，使提取出的慢特征值携带不同的信息：(3.3) Whitening: Using singular value decomposition, whitening the covariance matrix <ZZ T > of the input data can remove the correlation in the data, so that the extracted slow eigenvalues carry different information: 其中：ΛZ -1/2BT为白化矩阵，ΟZ为对应的白化后的输入信号。Among them: Λ Z -1/2 B T is the whitening matrix, and Ο Z is the corresponding whitened input signal. (3.4)计算转换矩阵WZ：对输入矩阵OZ做差分处理得到时序差分信号可以证明，对的协方差矩阵进行奇异值分解后，得到的一系列奇异值ωZj即为式(12)所述的目标函数值 (3.4) Calculate the transformation matrix W Z : perform differential processing on the input matrix O Z to obtain a time series differential signal can be proved, yes The covariance matrix of After performing singular value decomposition, the obtained series of singular values ω Zj are the objective function values described in equation (12). WZ＝PΛZ -1/2BT (17)W Z =PΛ Z -1/2 B T (17) 所述残差空间Ε中的慢特征sE的提取方法与上述典型变量空间Z中慢特征sZ的提取方法相同。The extraction method of the slow feature s E in the residual space E is the same as the extraction method of the slow feature s Z in the above-mentioned typical variable space Z. (4)划分慢特征sZ：最慢的特征对应最小的特征值，将特征值由小到大排列，并依据特征值大小将前l个特征划分为sZ中变化较慢的特征，用sZ,d表示；将后(r-l)个特征划分为sZ中变化较快的特征，用sZ,e表示。划分依据l的确定方法为，首先利用慢特征值sZ的变化快慢表示过程变量的变化快慢：(4) Divide the slow feature s Z : the slowest feature corresponds to the smallest eigenvalue, arrange the eigenvalues from small to large, and divide the first l features into the slow-changing features in sZ according to the size of the eigenvalue, using s Z, d represents; the last (rl) features are divided into fast-changing features in s Z , which are represented by s Z, e . The method for determining the division basis l is to first use the change speed of the slow eigenvalue s Z to represent the process variable. The speed of change: 其中：rji为矩阵RZ中第j行第i列的元素，sZi表示第i个慢特征序列，Δ(·)表示计算序列变化缓慢程度的一种运算： Among them: r ji is the element of the j-th row and the i-th column in the matrix R Z , s Zi represents the ith slow feature sequence, and Δ( ) represents an operation to calculate the slowness of the sequence change: 将提取出的慢特征值中缓慢度比输入数据缓慢度还要大的特征划分为快特征，一共有Me个这样的快特征：In the extracted slow feature values, the features whose slowness is greater than the slowness of the input data are divided into fast features. There are a total of Me such fast features: 这里card{·}表示集合{·}中元素个数。根据式(19)确定的Me值，对应将矩阵ΩZ也划分成两部分：Here card{·} represents the number of elements in the set {·}. According to the value of Me determined by equation (19), the matrix Ω Z is also divided into two parts correspondingly: (5)计算动态监测指标：从典型变量空间的第一个样本点开始，每个样本点可以得到一组动态监测指标(SZ,d 2，SZ,e 2)。(5) Calculation of dynamic monitoring indicators: starting from the first sample point in the typical variable space, each sample point can obtain a set of dynamic monitoring indicators (S Z,d 2 , S Z,e 2 ). (6)确定基于动态监测指标的控制限：利用核密度估计的方法，先估计出动态监测指标SZ,d 2的概率密度函数p(x)，对于给定显著性水平α，SZ,d 2的控制限的计算方式为：(6) Determine the control limit based on the dynamic monitoring index: Using the method of kernel density estimation, first estimate the probability density function p(x) of the dynamic monitoring index S Z, d 2 , for a given significance level α, S Z, Control limit for d 2 is calculated as: 以同样的方法可以计算出SZ,e 2的控制限 In the same way, the control limit of S Z,e 2 can be calculated (7)按照步骤(3)到步骤(6)所述方法，提取残差空间Ε的慢特征sE并将sE划分成两部分sE,d，sE,e，建立监测指标SE,d 2，SE,e 2并计算控制限 (7) According to the method described in step (3) to step (6), extract the slow feature s E of the residual space E and divide s E into two parts s E,d , s E,e , and establish the monitoring index S E ,d 2 , SE,e 2 and calculate the control limits (8)在线监测控制性能：基于步骤(2)到(4)建立的CVA-SFA模型、步骤(5)到步骤(7)所得的四个监测统计量在线监测汽轮机控制系统的性能状态，该步骤由以下子步骤来实现：(8) Online monitoring and control performance: Based on the CVA-SFA model established in steps (2) to (4), and the four monitoring statistics obtained in steps (5) to (7), the performance status of the steam turbine control system is monitored online. The steps are implemented by the following sub-steps: (8.1)获取新在线数据以及新数据预处理：采集到新的一段观测数据后，其中，下表new表示新观测数据，首先按照步骤(2)将Ynew拓展成过去矩阵，并根据步骤(2)中获得的均值和标准差对过去矩阵进行标准化处理得到Ypnew。(8.1) Acquisition of new online data and new data preprocessing: a new segment of observation data is collected where, the following table new represents the new observation data. First, according to step (2), Y new is expanded into a past matrix, and Y pnew is obtained by standardizing the past matrix according to the mean and standard deviation obtained in step (2). (8.2)提取出新观测数据的典型变量和残差变量：标准化处理后，利用步骤(2)确定的转换矩阵Vr和L计算出新观测数据的典型变量空间Znew和残差空间Enew。(8.2) Extract the typical variables and residual variables of the new observation data: After standardization, use the transformation matrices V r and L determined in step (2) to calculate the typical variable space Z new and the residual space E new of the new observation data . (8.3)提取新观测数据的典型变量空间Znew中的慢特征：首先按照步骤(3.1)中确定的均值和方差对Znew进行标准化处理，之后利用步骤(3.4)中确定的慢特征转换矩阵WZ，提取出标准化Znew的慢特征sZnew，并按照之前的划分参数将sZnew划分成sZ,d new和sZ,e new，同样根据WE可以得到Enew，进一步得到sE,d new和sE,e new。(8.3) Extract the slow features in the typical variable space Z new of the new observation data: first, standardize Z new according to the mean and variance determined in step (3.1), and then use the slow feature transformation matrix determined in step (3.4) W Z , extract the slow feature s Znew of the standardized Z new , and divide s Znew into s Z,d new and s Z,e new according to the previous division parameters, and also obtain E new according to W E , and further obtain s E ,d new and s E,e new . (8.4)计算新监测统计指标：根据建立的模型以及步骤(5)(7)中确定的计算方法，计算典型变量空间下的监测统计指标和残差空间监测指标 (8.4) Calculate the new monitoring statistical indicators: According to the established model and the calculation method determined in steps (5) (7), calculate the monitoring statistical indicators in the typical variable space and residual space monitoring metrics (8.5)在线判断汽轮机控制性能状态：实时比较四个监测指标与其各自的统计控制限，若四个监测指标都位于统计控制限之内，表明控制系统正常工作；若有一个或以上监测指标超出正常控制限，表明控制系统有异常状况发生。(8.5) Judging the control performance status of the steam turbine online: compare the four monitoring indicators and their respective statistical control limits in real time. If the four monitoring indicators are all within the statistical control limits, it indicates that the control system is working normally; if one or more monitoring indicators exceed the statistical control limits The normal control limit indicates that there is an abnormal situation in the control system.
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