CN113739841A - Multivariable steady-state detection method and system based on uncertainty theory - Google Patents

Multivariable steady-state detection method and system based on uncertainty theory Download PDF

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CN113739841A
CN113739841A CN202110693296.XA CN202110693296A CN113739841A CN 113739841 A CN113739841 A CN 113739841A CN 202110693296 A CN202110693296 A CN 202110693296A CN 113739841 A CN113739841 A CN 113739841A
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杨利
江浩
马汀山
余小兵
井新经
曾立飞
王东晔
王伟
郑天帅
刘学亮
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Xian Thermal Power Research Institute Co Ltd
Xian Xire Energy Saving Technology Co Ltd
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Xian Xire Energy Saving Technology Co Ltd
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Abstract

The invention discloses a multivariable steady-state detection method and a multivariable steady-state detection system based on an uncertainty theory, which comprise the following steps: 1) pre-estimation of maximum allowable reading range delta I of each operating parameter of thermal power generating unitAllow for(ii) a 2) Determining effective reading times N of each operation parameter sample of thermal power generating unita(ii) a 3) Minimum reading number N required for determining each operation parameter of thermal power generating unitR(ii) a 4) Minimum reading number N required by judging each operating parameter of thermal power generating unitRWhether all the effective reading times are less than the effective reading times N of each operation parameter sample of the thermal power generating unitaThe minimum reading times N required by each operating parameter of the thermal power generating unitRAll the effective reading times N are less than the effective reading times of each operation parameter sample of the thermal power generating unitaTaking the sampling interval of each operation parameter of the current thermal power generating unit as the steady-state working condition required by the steady-state detection algorithm, otherwise, turning to the step 1), and screening out the operation parameters of the thermal power generating unit by the method and the systemSteady state conditions required by the steady state detection algorithm.

Description

Multivariable steady-state detection method and system based on uncertainty theory
Technical Field
The invention belongs to the technical field of steam turbines, and relates to a multivariable steady-state detection method and system based on an uncertainty theory.
Background
The fluctuation of the on-line monitoring result of the performance of the thermal power generating unit is large, so that real information of on-line performance monitoring index change caused by the change of the performance condition of the equipment is submerged in a plurality of noise signals, and a correct analysis result cannot be obtained, so that steady state detection is a key link for calculating the performance index of the thermal power generating unit, and has important significance on equipment performance evaluation, operation optimization, system modeling and fault detection.
Since Narasimohan et al provided steady-state detection problems in the last 80 th century, many scholars conducted theoretical studies on the problems and provided different detection methods, mainly including combination of statistical test methods, filtering methods, sliding window methods, wavelet transformation methods, clustering, trend extraction methods and the like. The combined statistical test method is that a signal is supposed to be in a stable state in a test window, whether a variable is in a stable state or not is judged by comparing the mean value and the variance of data in adjacent windows, the occupied storage space is large, and the method is suitable for offline stable state detection; the filtering method is sensitive to noise signals and cannot perform multivariate detection, and the detection principle is that the stability of the system is determined by comparing the process variable difference before and after filtering with an allowable change threshold value; the sliding window is given a time window length, whether the operation data in the window is in a stable state or not is judged by moving along the time window, and the window length and the stable state threshold value have large influence on the detection result; the wavelet transformation method is to extract the process variation trend from the operation data, thus construct the steady state detection index according to the wavelet transformation coefficient which characterizes the process variation trend, used for judging the state of the process variable at each time point, have certain noise immunity to the abnormal value, can be used for online steady state detection; clustering is to divide the ordered samples arranged along the time axis into K segments according to the similarity index of the sample interval, each segment is regarded as a class, so that the difference sum in the class is as small as possible, the difference between the classes is as large as possible, and the same class is a steady state; the trend extraction method selects a steady-state detection index by extracting the change trend characteristics, and judges the steady-state working condition according to a given threshold value, wherein the influence of the steady-state threshold value selection on the detection result is large.
However, most of the existing steady-state detection methods need to determine the length of an inspection window and a steady-state threshold value according to experience, and multivariate steady-state detection research is less, and because the variables are numerous, the noise and signal characteristics are different, and the determination of the window length and the steady-state threshold value is very difficult in the actual production process, the application and popularization of various steady-state detection algorithms are directly influenced; in addition, the uncertainty (especially the random uncertainty) of the steady-state detection process cannot be predicted or controlled by various existing steady-state detection algorithms, so that the reliability of a performance calculation result is influenced, and the results of performance evaluation and fault detection of the thermal power unit are not ideal.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a multivariable steady-state detection method and system based on an uncertainty theory, and the method and the system can screen out steady-state working conditions required by a steady-state detection algorithm.
In order to achieve the above purpose, the multivariate steady-state detection method suitable for the thermal power generating unit comprises the following steps:
1) pre-estimation of maximum allowable reading range delta I of each operating parameter of thermal power generating unitAllow for；
2) Determining effective reading times N of each operation parameter sample of thermal power generating unita；
3) Minimum reading number N required for determining each operation parameter of thermal power generating unitR；
4) Minimum reading number N required by judging each operating parameter of thermal power generating unitRWhether all the effective reading times are less than the effective reading times N of each operation parameter sample of the thermal power generating unitaThe minimum reading times N required by each operating parameter of the thermal power generating unitRAll the effective reading times N are less than the effective reading times of each operation parameter sample of the thermal power generating unitaAnd taking the sampling interval of each operation parameter of the current thermal power generating unit as a steady-state working condition required by a steady-state detection algorithm, otherwise, turning to the step 1).
Thermal power generating unit operation parameter samples Na(I1、I2、……Ia) Requiring the steady state detection uncertainty to be less than USThen the maximum allowable reading range Δ IAllow forComprises the following steps:
Figure RE-GDA0003320141850000031
wherein, theta1The ratio of the percentage of change in the characteristic index of the turbine to the percentage of change in the reading of the meter, theta2The percentage of change in turbine performance indicators caused by each unit meter reading change is characterized,
Figure RE-GDA0003320141850000032
characterizing a sample Na(I1、I2、……Ia) Average value of (1), USA relatively random standard uncertainty is characterized.
Thermal power generating unit operation parameter samples Na(I1、I2、……Ia) Obtaining various operating parameter samples of the thermal power generating unitReading range delta I of booka＝Imax-Imin；
According to Δ IaCalculating ZaWherein Z isaRepresenting influence factors of the fluctuation range of each parameter reading on the performance index calculation result;
Figure RE-GDA0003320141850000033
when Δ IaLess than Δ IAllow forThen the number of valid readings is Na。
According to the number of effective readings as NaTo obtain omegaaWherein, ω isaThe ratio of the standard deviation of the readings to the reading range is expressed to obtain the minimum reading times N required by each operating parameter of the thermal power generating unitRComprises the following steps:
Figure RE-GDA0003320141850000034
a multivariable steady-state detection system suitable for a thermal power generating unit comprises:
the pre-estimation module is used for pre-estimating the maximum allowable reading range delta I of each operating parameter of the thermal power generating unitAllow for；
The first determination module is used for determining the effective reading times N of each operation parameter sample of the thermal power generating unita；
A second determination module for determining the minimum reading times N required by each operating parameter of the thermal power generating unitR；
The judging module is used for judging the minimum reading times N required by each operating parameter of the thermal power generating unitRWhether all the effective reading times are less than the effective reading times N of each operation parameter sample of the thermal power generating unitaThe minimum reading times N required by each operating parameter of the thermal power generating unitRAll the effective reading times N are less than the effective reading times of each operation parameter sample of the thermal power generating unitaTaking the sampling interval of each operation parameter of the current thermal power generating unit as the steady-state working condition required by the steady-state detection algorithm, otherwise, triggering the pre-estimation moduleAnd (6) working.
Thermal power generating unit operation parameter samples Na(I1、I2、……Ia) Requiring the steady state detection uncertainty to be less than USThen the maximum allowable reading range Δ IAllow forComprises the following steps:
Figure RE-GDA0003320141850000041
wherein, theta1The ratio of the percentage of change in the characteristic index of the turbine to the percentage of change in the reading of the meter, theta2The percentage of change in turbine performance indicators caused by each unit meter reading change is characterized,
Figure RE-GDA0003320141850000042
characterizing a sample Na(I1、I2、……Ia) Average value of (1), USA relatively random standard uncertainty is characterized.
Thermal power generating unit operation parameter samples Na(I1、I2、……Ia) Obtaining the reading range delta I of each operation parameter sample of the thermal power generating unita＝Imax-Imin；
According to Δ IaCalculating ZaWherein Z isaRepresenting influence factors of the fluctuation range of each parameter reading on the performance index calculation result;
Figure RE-GDA0003320141850000051
when Δ IaLess than Δ IAllow forThen the number of valid readings is Na。
According to the number of effective readings as NaTo obtain omegaaWherein, ω isaThe ratio of the standard deviation of the readings to the reading range is expressed to obtain the minimum reading times N required by each operating parameter of the thermal power generating unitRComprises the following steps:
Figure RE-GDA0003320141850000052
the invention has the following beneficial effects:
when the multivariable steady-state detection method and the multivariable steady-state detection system based on the uncertainty theory are operated specifically, based on the minimum reading frequency calculation principle of a performance test, the influence of the reading dispersity of each parameter on the random uncertainty of the performance index calculation result is controlled within a specified range, the length of a steady-state interval is determined in a self-adaptive manner, a proper steady-state working condition is screened out, and a foundation is laid for analyzing and evaluating the performance of a steam turbine by using operation data.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of standard deviation/reading range ω versus sample size N;
FIG. 3 is a schematic of a steady state screening process.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, the multivariate steady-state detection method suitable for the thermal power generating unit comprises the following steps:
1) pre-estimation of maximum allowable reading range delta I of each operating parameter of thermal power generating unitAllow for；
2) Determining effective reading times N of each operation parameter sample of thermal power generating unita；
3) Determining minimum reading times N required by each operating parameter of each thermal power generating unitR；
4) Minimum reading times N required for judging each operation parameter of each thermal power generating unitRWhether all the effective reading times are less than the effective reading times N of each operation parameter sample of the thermal power generating unitaThe minimum reading times N required by each operating parameter of the thermal power generating unitRAll the effective reading times N are less than the effective reading times of each operation parameter sample of the thermal power generating unitaTaking the sampling interval of each operation parameter of the current thermal power generating unit as the steady-state working condition required by the steady-state detection algorithm, otherwise, turning toTo step 1).
The specific operation of the step 1) is as follows:
thermal power generating unit operation parameter samples Na(I1、I2、……Ia) Requiring steady state detection uncertainty below USThen the maximum allowable reading range Δ IAllow forComprises the following steps:
Figure RE-GDA0003320141850000061
wherein, theta1The ratio of the percentage of change in the characteristic index of the turbine to the percentage of change in the reading of the meter, theta2The percentage of change in turbine performance indicators caused by each unit meter reading change is characterized,
Figure RE-GDA0003320141850000062
characterizing a sample Na(I1、I2、……Ia) Average value of (1), USCharacterizing a relative random standard uncertainty;
the specific operation of the step 2) is as follows:
thermal power generating unit operation parameter samples Na(I1、I2、……Ia) Obtaining the reading range delta I of each operation parameter sample of the thermal power generating unita＝Imax-Imin；
According to Δ IaCalculating ZaWherein Z isaRepresenting influence factors of the fluctuation range of each parameter reading on the performance index calculation result;
Figure RE-GDA0003320141850000063
when Δ IaLess than Δ IAllow forThen the number of valid readings is Na；
When Δ IaGreater than Δ IAllow forIf so, repeating the step 1);
the specific operation of the step 3) is as follows:
according to the number of effective readings as NaFrom FIG. 2, find ωaWherein, ω isaRepresenting the ratio of the standard deviation of the readings to the reading range, which is used for representing the discrete degree of the readings of each operation parameter sample of the thermal power generating unit, and referring to fig. 2;
minimum number of readings NRComprises the following steps:
Figure RE-GDA0003320141850000071
the invention discloses a multivariable steady-state detection system suitable for a thermal power generating unit, which comprises:
the pre-estimation module is used for pre-estimating the maximum allowable reading range delta I of each operating parameter of the thermal power generating unitAllow for；
The first determination module is used for determining the effective reading times N of each operation parameter sample of the thermal power generating unita；
A second determination module for determining the minimum reading times N required by each operating parameter of the thermal power generating unitR；
The judging module is used for judging the minimum reading times N required by each operating parameter of the thermal power generating unitRWhether all the effective reading times are less than the effective reading times N of each operation parameter sample of the thermal power generating unitaThe minimum reading times N required by each operating parameter of the thermal power generating unitRAll the effective reading times N are less than the effective reading times of each operation parameter sample of the thermal power generating unitaAnd if not, triggering the pre-estimation module to work.
Example one
The present invention will be described in detail by taking the calculation of the heat rate of the steam turbine as an example and using historical operating data to perform steady state detection.
In the thermodynamic cycle of the steam turbine, the main parameters influencing the calculation result of the heat consumption rate of the steam turbine are main water supply flow, electric power, main steam temperature, main steam pressure, reheating temperature and the like, the invention adopts 5 parameters, and the influence of each main parameter on the heat consumption rate is determinedRelative standard random uncertainty UsNot more than 0.1%, and sensitivity coefficient theta of each main measurement parameter relative to heat rate1And theta2The calculation results are shown in table 1.
TABLE 1
Figure RE-GDA0003320141850000081
According to the method, stable historical operating data of a certain section of an ultra-supercritical 1000MW unit in 2016 and 5 months is taken as a research object, part of sampling data is shown in a table 2, the sampling time interval is 1min, and power, feed water flow, main steam pressure, main steam temperature and reheated steam temperature are taken as distinguishing parameters of a system in a steady state.
TABLE 2
Figure RE-GDA0003320141850000082
The specific operation process of this embodiment is as follows:
1) estimating the maximum allowable reading range delta I of each main parameterAllow for；
As can be seen from the formula (1), given is θ1And theta2Maximum allowable reading range DeltaI of power and main water supply flowAllow forAverage value of readings of existing sampling parameters
Figure RE-GDA0003320141850000083
Considering that the changes of the power and the main feed water flow are small under the steady state working condition, the power and the main feed water flow at the steady state starting point are taken as the reading average value of the steady state working condition, so that the maximum allowable reading range of the power and the main feed water flow is obtained, and the maximum allowable reading range of the temperature and the pressure is constant after the reading times N are given.
Given the number of readings N of 60, from fig. 2, ω, according to equation (1), the initial value of the electric power is known to be 574.36MW, i.e. the maximum allowable range Δ I of the powerAllow for10.34MW, indicating the maximum allowable deviation of power samples in 60 minutesThe difference is 10.34MW, invalid sampling data is obtained when the difference exceeds the value, and the maximum allowable reading range delta I of the main water supply flow is obtained in the same wayAllow for＝30.10t/h。
2) Determining the number of valid readings N of each parametera；
Taking the calculation of the effective reading times of the electric power as an example, the sampling time is set to be 3min, and three sampling data of the power are obtained, I1＝574.36MW，I2＝576.72MW，I3576.22MW, the sample reading fluctuation range Δ I is Imax-Imin＝I2-I1＝2.36MW；
For the power values, Δ I<ΔIAllow forTherefore, the three sampling data in the period are all valid, i.e. the number of valid readings Na＝3；
3) Determining the minimum number of readings N of each main parameterR；
From NaWhen ω is 0.5822 and Z is 0.45 calculated from equation (2) and the minimum number of readings N of power at that time is found from equation (3)R＝28；
Namely NR>NaIf the stability judging condition of the uncertainty theory is not satisfied, the sampling is continued and I is read in4The above loop calculation process is repeated until N is 576.82MWR<NaThe number of readings of the power at this time satisfies the uncertainty requirement, i.e., the power is stable during the sampling time.
Similarly, the minimum reading times of other main parameters can be calculated, and when the reading times of all the parameters meet the effective reading times NaGreater than the minimum number of readings NRAnd then, detecting to obtain a section of steady-state working condition, wherein the influence of the reading dispersion degree of each main parameter on the random uncertainty of the performance calculation result in the section of steady-state working condition is not more than 0.1% of Us.
In summary, the following steps: the specific process of steady state detection is the minimum number of readings N required for searching each main parameterRAll less than the number of valid readings NaThe part of the common interval where the time is located is the length of the steady-state interval. Therefore, based on the uncertainty theoryThe multivariate steady-state detection is a process of repeated loop calculation, and the end condition of the loop calculation is NR<NaAnd then, repeating the process to enter the screening process of the next steady-state working condition until all the steady-state working conditions meeting the requirements are screened out, wherein the screening process of a certain section of steady-state working condition is shown in fig. 3.
When the number of sampling samples is 48, namely the system is stabilized for 48min, the minimum reading times of all the main parameters are all smaller than the effective reading times, and the specification that the random uncertainty of the performance calculation result caused by the reading dispersity of all the main parameters does not exceed 0.1% is met, so that the steady-state interval in the sampling time is [0, 48], the length of the steady-state interval is determined in a self-adaptive manner according to a steady-state detection algorithm, and the defect that the length of a window (the length of the steady-state interval) is required to be determined in advance by most existing steady-state detection algorithms is overcome.

Claims (8)

1. A multivariable steady-state detection method suitable for a thermal power generating unit is characterized by comprising the following steps:
1) pre-estimation of maximum allowable reading range delta I of each operating parameter of thermal power generating unitAllow for；
2) Determining effective reading times N of each operation parameter sample of thermal power generating unita；
3) Minimum reading number N required for determining each operation parameter of thermal power generating unitR；
4) Minimum reading number N required by judging each operating parameter of thermal power generating unitRWhether all the effective reading times are less than the effective reading times N of each operation parameter sample of the thermal power generating unitaThe minimum reading times N required by each operating parameter of the thermal power generating unitRAll the effective reading times N are less than the effective reading times of each operation parameter sample of the thermal power generating unitaAnd taking the sampling interval of each operation parameter of the current thermal power generating unit as a steady-state working condition required by a steady-state detection algorithm, otherwise, turning to the step 1).
2. The multivariable steady-state detection method suitable for the thermal power generating unit as claimed in claim 1, wherein the multivariable steady-state detection method is characterized in thatSetting running parameter samples N of thermal power generating unita(I1、I2、……Ia) Requiring the steady state detection uncertainty to be less than USThen the maximum allowable reading range Δ IAllow forComprises the following steps:
Figure FDA0003127009120000011
wherein, theta1The ratio of the percentage of change in the characteristic index of the turbine to the percentage of change in the reading of the meter, theta2The percentage of change in turbine performance indicators caused by each unit meter reading change is characterized,
Figure FDA0003127009120000012
characterizing a sample Na(I1、I2、……Ia) Average value of (1), USA relatively random standard uncertainty is characterized.
3. The multivariable steady-state detection method suitable for the thermal power generating unit as claimed in claim 1, wherein each operation parameter sample N of the thermal power generating unit is seta(I1、I2、……Ia) Obtaining the reading range delta I of each operation parameter sample of the thermal power generating unita＝Imax-Imin；
According to Δ IaCalculating ZaWherein Z isaRepresenting influence factors of the fluctuation range of each parameter reading on the performance index calculation result;
Figure FDA0003127009120000021
when Δ IaLess than Δ IAllow forThen the number of valid readings is Na。
4. The multivariable steady-state detection method suitable for the thermal power generating unit as claimed in claim 1, wherein the multivariable steady-state detection method is characterized in thatAccording to the number of valid readings being NaTo obtain omegaaWherein, ω isaThe ratio of the standard deviation of the readings to the reading range is expressed to obtain the minimum reading times N required by each operating parameter of the thermal power generating unitRComprises the following steps:
Figure FDA0003127009120000022
5. a multivariable steady-state detection system suitable for a thermal power generating unit is characterized by comprising:
the pre-estimation module is used for pre-estimating the maximum allowable reading range delta I of each operating parameter of the thermal power generating unitAllow for；
The first determination module is used for determining the effective reading times N of each operation parameter sample of the thermal power generating unita；
A second determination module for determining the minimum reading times N required by each operating parameter of the thermal power generating unitR；
The judging module is used for judging the minimum reading times N required by each operating parameter of the thermal power generating unitRWhether all the effective reading times are less than the effective reading times N of each operation parameter sample of the thermal power generating unitaThe minimum reading times N required by each operating parameter of the thermal power generating unitRAll the effective reading times N are less than the effective reading times of each operation parameter sample of the thermal power generating unitaAnd if not, triggering the pre-estimation module to work.
6. The multivariable steady-state detection system suitable for thermal power generating unit as claimed in claim 5, wherein each operation parameter sample N of the thermal power generating unit is seta(I1、I2、……Ia) Requiring the steady state detection uncertainty to be less than USThen the maximum allowable reading range Δ IAllow forComprises the following steps:
Figure FDA0003127009120000031
wherein, theta1The ratio of the percentage of change in the characteristic index of the turbine to the percentage of change in the reading of the meter, theta2The percentage of change in turbine performance indicators caused by each unit meter reading change is characterized,
Figure FDA0003127009120000032
characterizing a sample Na(I1、I2、……Ia) Average value of (1), USA relatively random standard uncertainty is characterized.
7. The multivariable steady-state detection system suitable for thermal power generating unit as claimed in claim 5, wherein each operation parameter sample N of the thermal power generating unit is seta(I1、I2、……Ia) Obtaining the reading range delta I of each operation parameter sample of the thermal power generating unita＝Imax-Imin；
According to Δ IaCalculating ZaWherein Z isaRepresenting influence factors of the fluctuation range of each parameter reading on the performance index calculation result;
Figure FDA0003127009120000033
when Δ IaLess than Δ IAllow forThen the number of valid readings is Na。
8. The multivariable steady-state detection system suitable for the thermal power generating unit according to claim 5, wherein the number of valid readings is NaTo obtain omegaaWherein, ω isaThe ratio of the standard deviation of the readings to the reading range is expressed to obtain the minimum reading times N required by each operating parameter of the thermal power generating unitRComprises the following steps:
Figure FDA0003127009120000034
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