CN109325294B - Evidence characterization construction method for performance state of air preheater of thermal power generating unit - Google Patents
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Abstract
The application provides a evidence characterization construction method of a performance state of an air preheater of a thermal power generating unit, which comprises the following steps: obtaining data samples of unit load, flue gas pressure drop, flue gas side and air side pressure drop, exhaust gas temperature, air temperature at the outlet of the air preheater and air temperature at the inlet of the air preheater according to a preset period; acquiring a data sample set of the air preheater in a quasi-steady state process from the sample; computing an initial class core for each performance state of the data sample setAccording to the initial class center of each performance state, calculating typical performance states in each performance state; class core for calculating each typical performance stateAnd class core for transitional performance statesCalculating the credibility of each performance state of the data sample membership; an air preheater performance state evidence characterization was constructed. Imprecise and uncertain information can be processed, and the evolution process of the performance of the air preheater from one state to the other state can be effectively described.
Description
Technical Field
The application relates to the technical field of thermal power unit air preheater equipment, in particular to a evidence representation construction method of a thermal power unit air preheater performance state.
Background
The air preheater is one of main auxiliary equipment of the thermal power unit for improving the heat exchange performance of the boiler and reducing the heat loss, and the state of the air preheater has an important influence on the economy and the safety of the unit. Particularly, in recent years, thermal power generating unit accidents caused by abnormal states of the air preheater frequently occur, and the state degradation of the thermal power generating unit accidents has a great influence on the energy efficiency of the thermal power generating unit. In order to avoid influencing the use of the thermal power unit caused by abnormal states of the air preheater, the states of the air preheater need to be monitored. However, the state of the device is not effectively supervised at present, and the main reasons are as follows: firstly, due to problems of detection cost or meters and the like, inaccuracy and uncertainty exist in parameters related to states of the problems; and secondly, the state detection and diagnosis method is imperfect.
Disclosure of Invention
The application provides a method for constructing evidence characterization of the performance state of an air preheater of a thermal power unit, which is used for constructing the evidence characterization of the performance state of the air preheater of the thermal power unit, can process inaccurate and uncertain information and can effectively describe the evolution process of the performance of the air preheater from one state to other states.
The application provides a evidence characterization construction method of a performance state of an air preheater of a thermal power generating unit, which comprises the following steps:
obtaining data samples of unit load, flue gas pressure drop, flue gas side and air side pressure drop, exhaust gas temperature, air temperature at the outlet of the air preheater and air temperature at the inlet of the air preheater according to a preset period;
acquiring a data sample set of the air preheater in a quasi-steady state process from the sample;
computing an initial class core for each performance state of the data sample set
According to the initial class center of each performance state, calculating typical performance states in each performance state;
class core for calculating each typical performance stateAnd class I of transitional performance states>
Calculating the credibility of each performance state of the data sample membership;
an air preheater performance state evidence characterization was constructed.
Optionally, in the above method, acquiring a data sample set of the air preheater in a quasi-steady state process from the samples includes:
setting a time step K, and when K is more than or equal to K, enabling the air preheater to be in a quasi-steady state process;
data sample set X with air preheater in quasi-steady state process l ：X l ＝{X k ||x j,k+1 -x j,k |≤Δ j ,j＝1,2,3,4,5,6,k＝1,2,3,…,N l }，N l Is the data sample acquired during the first quasi-steady state.
Optionally, in the above method, an initial class center of each performance state of the data sample set is calculatedComprising the following steps:
wherein (1)>Is X l Is the k-th data sample of the data, respectively X l Medium maximum and minimum.
Optionally, in the above method, counting typical performance states in each performance state according to the initial class center of each performance state includes:
computing Euclidean distance of initial class center of each adjacent performance state
When (when)Epsilon is given as a constant, { omega }, is considered k Sum { omega } l -the same performance state, merging the two types of data samples;
X l ←X k ∪X l ，N l ←N l +N k l=l-1; repeating the above operation until no satisfaction can be foundIs a class of (2); finally, c different typical performance states { omega l },l＝1,2,…,c。
Optionally, in the above method, each typical performance state is calculatedIs of the class of (2)And class core for transitional performance statesComprising the following steps:
class core of typical performance stateClass I of transitional Performance State>
The evidence representation construction method for the performance state of the air preheater of the thermal power unit is used for constructing the evidence representation of the performance state of the air preheater of the thermal power unit based on mechanism analysis and operation data mining, and the method for describing the performance state of the air preheater by adopting the evidence can process inaccurate and uncertain information, and can effectively describe the evolution process of the performance of the air preheater from one state to other states (including transition states).
Drawings
In order to more clearly illustrate the technical solution of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a structural flow chart of an evidence characterization construction method of a thermal power unit air preheater performance state provided by an embodiment of the application;
FIG. 2 is a diagram of sample data in an example provided by an embodiment of the present application;
FIG. 3 is a diagram illustrating an example of steady-state process determination according to an embodiment of the present application;
FIG. 4 is a class diagram illustrating exemplary performance states and transition states provided by embodiments of the present application;
FIG. 5 is a diagram of an example of evidence construction results provided by an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The air preheater of the thermal power generating unit heats air required by boiler combustion by utilizing the heat of flue gas at the tail of the boiler (the air preheater can be divided into heat transfer type and heat storage type according to a heat transfer mode), so that the purposes of recovering the heat of the flue gas, reducing the temperature of discharged smoke and improving the efficiency of the boiler are achieved.
For the air preheater, the air leakage and ash accumulation degree indexes of the air preheater are reduced under a given working condition, and meanwhile, the low-temperature corrosion is avoided, so that the air preheater is an important index for measuring the working performance of the air preheater.
(1) Basic principle of air preheater:
the main indexes affecting the working performance of the air preheater of the thermal power unit are the air leakage quantity, the ash accumulation degree index and the lowest surface temperature of the heating surface. The air leakage mainly comes from the direct air leakage of the air preheater. Thus, these three indices can be determined by the following relationship.
In the formula (1), G is direct air leakage quantity, and the unit is t/h
In the formula (1), a is the air leakage coefficient, and is dimensionless
In the formula (1), A is the total area of the sealing gap, and the unit is m 2
In the formula (1), g is the gravitational acceleration, and the unit is m/s 2
In formula (1), ρ a Is air density in kg/m 3
In formula (1), Δp is the pressure difference between the flue gas side and the air side in MPa
In the formula (1), the air density ρ a Is basically unchanged. Leakage deviceThe air factor a depends on the air preheater equipment structure (such as the sealing weight, the sealing form and the like), and the equipment air leakage factor and the sealing gap area determined by the structure are basically unchanged. In addition, the pressure differential between the flue gas side and the air side is determined by the resistance of the boiler system. The resistance of the system depends on the overall design condition of the boiler (the type of the coal mill, the type of the burner, the arrangement of the heating surfaces of the boiler and the like), and the soot blowing of the heating surfaces of the boiler is carried out in operation (the primary air pressure and the secondary air quantity are timely adjusted according to the type of coal, the load of a unit, the combination mode of the coal mill and the like so as to strengthen the soot blowing).
In the formula (2), lambda is the ash accumulation degree index
In formula (2), Δp y Is the flue gas pressure drop of the air preheater, and the unit is MPa
In formula (2), V y The unit is m, which is the volume of the flue gas 3
In formula (2), G y The unit is kg of smoke quality
In formula (2), B j To calculate the fuel quantity, the unit is t/h
In formula (2), θ py The unit is the smoke discharge temperature of the smoke
In the formula (2), K is a constant, dimensionless number
In the formula (2), the fuel amount B j The calculation of the load of a unit, industrial analysis data of fire coal, smoke analysis data and the like; mass of flue gas G y And flue gas volume V y In addition to the excess air ratio a, the calculation of (c) requires elemental analysis data of the fire coal.
In addition, the lower the temperature of the heating surface of the air preheater, the more easily the low-temperature corrosion occurs. The minimum surface temperature may be determined by the following relationship.
In the formula (3), the amino acid sequence of the compound,θ py the unit is the smoke discharge temperature of the smoke
In formula (3), t lk Is the temperature of cold air with the unit of DEG C
In formula (3), a k ，a y The heat release coefficients of the air side and the flue gas side are respectively shown as w/(m) 2 .k)
In formula (3), τ k ，τ y For the residence time of the rotor in the air and in the flue gas, the unit is s
In the formula (3), according to further analysis of the related knowledge of heat transfer chemistry, the heating coefficients of the air side and the flue gas side are related to the flue gas at the inlet and outlet of the air preheater and the air temperature, respectively using theta jy ，θ py ，t lk ，t rk And (3) representing.
To sum up, the air leakage G, the ash accumulation index lambda and the lowest surface temperature t of the heating surface min Is the main performance evaluation index of the air preheater, and the main influencing factors comprise the operation parameters delta p and delta p y 、θ jy 、θ py 、a、t lk 、t rk The air leakage coefficient a, the total sealing gap area A and the V depending on the coal type and the coal quality of the coal are related to the equipment form y 、G y 、B j Etc. For a certain unit of a certain coal type, the fuel quantity B j Quality of flue gas G y Volume of flue gas V y Is a parameter of random group load variation.
Therefore, the air preheater has air leakage, ash accumulation index and heating surface with the lowest surface temperature including random load Pel, air excess coefficient a and smoke pressure drop Deltap y Inlet flue gas temperature theta of air preheater jy Smoke exhaust temperature theta py Inlet and outlet air temperature t of air preheater lk 、t rk The change in the flue gas air side pressure difference Δp changes. And further analyzing the operation parameters, wherein the excess air coefficient is in positive correlation with the inlet flue gas temperature and the flue gas discharge temperature of the air preheater according to the combustion characteristics of the boiler.
Therefore, the unit load Pel and the smoke side pressure drop deltap can be reduced y Smoke exhaust temperature deltap and smoke air side pressure differenceΔp, inlet and outlet air temperature t of air preheater lk 、t rk As a process parameter reflecting the performance state of the air preheater, the following is defined:
X:＝(x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ):＝(Pel,Δp y ,Δp,θ py ,t rk ,t lk ) (4)
in the formula (4), the vector transpose is represented by "'".
In formula (4), X is a process parameter (column) vector reflecting the air preheater, X 1 ＝Pel,x 2 ＝Δp y ,x 3 ＝Δp,x 4 ＝θ py ,x 5 ＝t rk ,x 6 ＝t lk 。
(2) Data mining for typical performance states of an air preheater
Data mining analysis is carried out on the air preheater to obtain a data sample X i ＝(x 1i ,x 2i ,x 3i ,x 4i ,x 5i ,x 6i ) ' i=1, 2, …, typical performance states ω contained in these data samples l L=1, 2, …, the method is as follows:
judging whether the air preheater reaches or is in a quasi-steady state process according to the following steps:
|x j,k+1 -x j,k |≤Δ j ,j＝1,2,3,4,5,6,k＝1,2,3,…(5)
in formula (5), Δ j The jth process parameter x of the air preheater j Is a threshold of variation of (a).
Given a suitable time step K, when K is greater than or equal to K, the air preheater is deemed to have reached a quasi-steady state process. Once the air preheater reaches a quasi-steady state, a data sample set X of the quasi-steady state process can be obtained l :
X l ＝{X k ||x j,k+1 -x j,k |≤Δ j ,j＝1,2,3,4,5,6,k＝1,2,3,…,N l } (6)
In formula (6), N l Is the data sample size acquired in the first quasi-steady state process.
After obtaining c quasi-steady-state processes which are different from each other, the typical performance states omega of c air preheaters are obtained l L=1, 2, …, c. The data samples of these quasi-steady state processes constitute a data sample set of the air preheater:
in equation (7), the symbol U is collective and operates.
In equation (7), N is the number of samples of the obtained data sample set X.
(3) Evidence characterization and definition of air preheater performance states
Evidence theory is an effective tool for effectively processing imprecise and uncertain information, wherein evidence characterization and definition are the basis of evidence theory. Given a set of air preheater typical performance states Ω= { ω 1 ,ω 2 ,…,ω c Any evidence m describing the current performance of the thermal device can be defined as follows:
in formula (8), 2 Ω Is a power set of the set Ω.
In formula (8), a is any subset of Ω.
In formula (8), m (A) is the confidence that the current performance state of the air preheater belongs to state set A.
Set a describes the inaccuracy of the air preheater current state values, while m (a) describes the confidence, i.e., uncertainty, that the current performance state belongs to state set a. For example, assume that air preheater performance can be defined as three typical states Ω= { ω 1 ,ω 2 ,ω 3 Evidence m i :m i (ω 1 )＝0.3,m i ({ω 1 ,ω 2 0.7 describes that the air preheater performance is being driven from state ω 1 To transition state { omega ] 1 ,ω 2 Diffraction of 0.3 and0.7 represents the uncertainty of the air preheater belonging to different performance states, A= { ω 1 ,ω 2 The inaccuracy of the air preheater performance state is described.
(4) Evidence m of air preheater Performance State i Construction of (3)
First, in order to eliminate the influence of different variable dimensions, the original variables are normalized according to equation (10).
In the formula (10), the amino acid sequence of the compound,is X l In (c) k data samples, a->Respectively X l Medium maximum and minimum.
Calculating each typical performance state { omega } l { omega } and transitional states { omega } l ,ω l+1 Core of l=1, 2, …, c-1:
conversely, the class center corresponding to the original variable space can be obtained by the formula (13):
in the formula (13), A is E F, F is = { { omega 1 },{ω 1 ,ω 2 },{ω 2 },{ω 2 ,ω 3 }…,{ω c-1 },{ω c-1 ,ω c },{ω c And is a typical set of performance states for an air preheater.
Second, calculate the degree of probability (/ uncertainty) that each data sample is affiliated with each performance state:
in the formula (14), the amino acid sequence of the amino acid sequence,is->Euclidean distance from performance state a.
In the formula (14), F = { { ω 1 },{ω 1 ,ω 2 },{ω 2 },{ω 2 ,ω 3 }…,{ω c-1 },{ω c-1 ,ω c },{ω c And is a typical set of performance states for an air preheater.
When the transition state { omega } is not considered l ,ω l+1 When l=1, 2, …, c-1, a evidence m i (A) The degradation is a probability distribution or a fuzzy number.
(5) Evidence characterization of air preheater Performance State (set)
Based on the above description, on the basis that an air preheater process parameter data sample X is available, a evidence characterization (set) for reflecting the air preheater performance state can be constructed as follows:
{(X i ,m i )|i＝1,2,…,N} (15)
in formula (15), X i Is the ith data sample of X.
In formula (15), m i Corresponds to X as defined by formula (13) i Evidence of air preheater performance status.
Specifically, as shown in fig. 1, the method for constructing evidence characterization of performance states of an air preheater of a thermal power unit provided by the embodiment of the application comprises the following steps:
s101: and obtaining data samples of unit load, flue gas pressure drop, flue gas side and air side pressure drop, exhaust gas temperature, air temperature at the outlet of the air preheater and air temperature at the inlet of the air preheater according to a preset period.
During normal operation of the thermal power plant, data samples (unit load x 1 Pel, flue gas pressure drop x 2 ＝Δp y Flue gas side and air side pressure drop x 3 =Δp, exhaust gas temperature x 4 ＝θ py Air preheater outlet air temperature x 5 ＝t rk Inlet air temperature x of air preheater 6 ＝t lk ) The sampling period of (2) is 1 second. Typically, for the raw second-level data samples, a data screening window of 60 sampling periods is established, with a 1 minute average as a representative data sample.
S1O2: a data sample set of the air preheater in a quasi-steady state process is obtained from the samples.
Setting a time step K, and when K is more than or equal to K, enabling the air preheater to be in a quasi-steady state process;
data sample set X with air preheater in quasi-steady state process l ：X l ＝{X k ||x j,k+1 -x j,k |≤Δ j ,j＝1,2,3,4,5,6,k＝1,2,3,…,N l }，N l Is the data sample acquired during the first quasi-steady state.
Specifically, (1) the initial setting l=1, if N l >K, the first typical performance state { ωl } is obtained l -and its corresponding set of data samples X l The method comprises the steps of carrying out a first treatment on the surface of the (2) l=l+1, repeating operation (1) until the traversal search calculation is completed for all original normal operation data samples; (3) a set of l=l performance states is obtained.
S103: computing an initial class core for each performance state of the data sample set
Calculating initial class centers for each performance state according to equation (11)Specifically, the->
Wherein (1)>Is X l Is the k-th data sample of the data, respectively X l Medium maximum and minimum.
S1O4: and counting typical performance states in each performance state according to the initial class center of each performance state.
Calculating each typical performance state { omega } l Euclidean distance between class centersIf the distance between two different classes is relatively close, i.e.: />(ε is a given constant, which determines that the number of final typical state class cores should be between 2-10), then consider { ω } k Sum { omega } l The two types of data samples are combined; x is X l ←X k ∪X l ，N l ←N l +N k L=l-1; repeating the above operation until no satisfaction +.>Is a class of (2); finally, c different typical performance states { omega l },l＝1,2,…,c。
S1O5: computing classes for typical performance statesHeart shapeAnd class I of transitional performance states>
Calculating each typical performance state (ωl) according to the formulas (11), (12) and (13) l { ω) and transitional performance states { ω } l ,ω l + 1 Heart-like of }, l=1, 2, …, c-1And->
S106: and calculating the credibility of each performance state of the data sample membership.
Calculating the reliability (/ uncertainty) of each data sample belonging to each performance state according to the step (14)
S1O7: the air preheater can be constructed to represent evidence of the state.
And (3) constructing a heater performance state evidence characterization (set) according to the formula (15).
The following describes in detail the evidence characterization construction method of the performance state of the air preheater of the thermal power generating unit provided by the embodiment of the application with reference to a specific example:
take a supercritical unit #1 high-pressure heater in a certain place as an example.
Step 1: original normal sample (unit load x) 1 ＝p el Pressure drop x of flue gas 2 ＝Δp y Flue gas side and air side pressure drop x 3 =Δp, exhaust gas temperature x 4 ＝θ py Air preheater outlet air temperature x 5 ＝t rk Inlet air temperature x of air preheater 6 ＝t lk ) And (5) pretreatment. The sampling duration of this example is about 3200 minutes, the original running data is a data sample of 1 second, an arithmetic average value of 60 cycles is taken as a representative data sample, and 3150 samples are finally selected. Schematic of sampled dataSee fig. 2.
Step 2: for each selected process parameter, a set load x is established 1 ＝p el Is given by a single variable steady state model of (a), a given change threshold delta 1 =6, according to equation (5), determine if the air preheater is in a quasi-steady state process.
The specific implementation is as follows: (1) initial setting l=1, if N l >K: =20, then the first representative performance state ω is obtained l And its corresponding data sample set X l The method comprises the steps of carrying out a first treatment on the surface of the (2) l=l+1, repeating operation (1) until the traversal search calculation is completed for all original normal operation data samples; (3) a set of performance states of l=34 is obtained. The steady state process determination schematic diagram of this example is shown in fig. 3.
Step 3: calculating each performance state { ω ] according to (11) l I=1, 2, 3..the class core of 34(where the superscript "0" indicates the initial class core). The class centers are ordered according to ascending load, and the results are respectively:
step 4: calculating each typical performance state { omega } l Euclidean distance between class centersIf it isThen consider { omega ] k Sum { omega } l The two types of data samples are combined; x is X l ←X k ∪X l ，N l ←N l +N k L=l-1; repeating the above operation until no satisfaction +.>Is a class of (2); finally, 5 different typical performance states { omega } are obtained l },l＝1,2,…,5。
Step 5: each of the typical performance states { ω ] is calculated according to equations (11), (12) and (13), respectively l { ω) and transitional performance states { ω } l ,ω l+1 Heart-like of l=1, 2, …,4And->
Exemplary performance states and transition states are outlined in FIG. 4.
Step 6: calculating the reliability (/ uncertainty) of each data sample belonging to each performance state according to (14). In a certain state X i For example, = {616.02mw,0.97mpa,9.74mpa,118.83 ℃,329.88 ℃,22.01 ℃ }, the calculated credibility of each performance state is:
m i {ω 1 )＝0.0395；m i (ω 1 ,ω 2 )＝0.8742；m i (ω 2 )＝0.0565；m i (ω 2 ,ω 3 )＝0.0160；
m i (ω 3 )＝0.0074；m i (ω 3 ,ω 4 )＝0.0028；m i (ω 4 )＝0.0015；m i (ω 4 ,ω 5 )＝0.0011；
m i (ω 5 )＝0.0010；
due to allocation to transitional states { omega ] 1 ,ω 2 The confidence level is highest, so X i = {616.02MW,0.97Mpa,9.74Mpa,118.83 ℃,329.88 ℃,22.01 ℃ should belong to ω 1 And omega 2 Is a transition class of (c).
Step 7: and (3) constructing a heater performance state evidence characterization (set) according to the formula (15). { (X) i ,m i )|i＝1,2,…,N}，X i Is the ith data sample of X. m is m i For the corresponding X from step 6 i Evidence of the regenerative heater performance state. Taking a section of real-time data as an example, the evidence construction result is shown in figure 5, and the first part of the section belongs to omega 5 Followed by transition classes (omega 4 ,ω 5 ) Transition to omega 4 And through transition class (omega 4 ,ω 5 ) Re-transition to omega 5 . At omega 4 In the state, transition classes (ω 4 ,ω 5 ) The phenomenon of mutation in confidence indicates a directional ω at this time 5 Trend of state transition. The above process can be described by the evidence constructed by the present patent, as can be seen in fig. 5.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and the same and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments, and relevant parts refer to part of description of method embodiments. Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (1)
1. An evidence characterization construction method of a performance state of an air preheater of a thermal power generating unit is characterized by comprising the following steps:
obtaining data samples of unit load, flue gas pressure drop, flue gas side and air side pressure drop, exhaust gas temperature, air temperature at the outlet of the air preheater and air temperature at the inlet of the air preheater according to a preset period;
acquiring a data sample set of the air preheater in a quasi-steady state process from the sample comprises:
setting a time step K, and when K is more than or equal to K, enabling the air preheater to be in a quasi-steady state process;
，X l for a data sample set with the air preheater in a quasi-steady state process,N l is the firstlData samples collected during quasi-steady state;
computing an initial class core for each performance state of the data sample setThe formula of (1) includes: />，
Wherein->，/>Is thatX l The first of (3)kData sample,/->、Respectively isX l Medium maximum and minimum;
based on the initial cores of the performance states, counting the typical performance states in the performance states includes calculating the typical performance statesEuclidean distance between class centers>If the distance between two different classes is relatively close,，/>for a given constant, its determination should be such that the final number of typical state classes is between 2-10, then considerAnd->Combining the two types of data samples for the same performance state> ，/>，L= L -1; repeating the above operation until no satisfaction +.>Class of last obtained mutualDifferent from each othercStatus of typical Performance>；
Class core for calculating each typical performance stateAnd class I of transitional performance states>The formula of (1) includes:；
；
，/>，/>is a typical set of performance states for an air preheater;
the formula for calculating the credibility of each performance state of the membership of the data sample comprises，Is a typical set of performance states for an air preheater,is->Euclidean distance from performance state a;
the formula for constructing the evidence representation of the performance state of the air preheater comprises，X i Is thatXIs the first of (2)iThe data samples are taken from the data samples,m i to correspond toX i Evidence of air preheater performance status.
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