CN109344488B - Evidence characterization construction method for performance state of regenerative heater of thermal power generating unit - Google Patents
Evidence characterization construction method for performance state of regenerative heater of thermal power generating unit Download PDFInfo
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Abstract
The application provides a evidence characterization construction method of a thermal power generating unit regenerative heater performance state, which comprises the following steps: collecting samples of unit load and water outlet temperature of a regenerative heater according to a preset period; acquiring a data sample set of a regenerative heater 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; and constructing a regenerative heater performance state evidence characterization. The application provides a evidence characterization construction method of the performance state of a regenerative heater of a thermal power generating unit, which can process inaccurate and uncertain information and can effectively describe the evolution process of the performance of the heater from one state to other states.
Description
Technical Field
The application relates to the technical field of regenerative heater equipment of thermal power generating units, in particular to a evidence characterization construction method of a performance state of a regenerative heater of a thermal power generating unit.
Background
The heater is one of main auxiliary equipment of the thermal power generating unit, and the state of the heater has an important influence on the economy and safety of the unit. Particularly, in recent years, thermal power generation unit accidents frequently occur due to abnormal states of heaters (particularly high-voltage heaters), and the state degradation thereof has a great influence on the energy efficiency of the unit. In order to avoid the influence on the use of the thermal power unit caused by abnormal states of the heater, the states of the heater 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 a regenerative heater of a thermal power unit, which is used for constructing the evidence characterization of the performance state of the regenerative heater of the thermal power unit, can process inaccurate and uncertain information and can effectively describe the evolution process of the performance of the heater from one state to other states.
The application provides a evidence characterization construction method of a thermal power generating unit regenerative heater performance state, which comprises the following steps:
collecting samples of unit load and water outlet temperature of a regenerative heater according to a preset period;
acquiring a data sample set of a regenerative heater 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;
and constructing a regenerative heater performance state evidence characterization.
Optionally, in the above method, acquiring a data sample set of the regenerative heater 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 regenerative heater to be in a quasi-steady state process;
data sample set X with regenerative heater in quasi-steady state process l ：X l ＝{X k ||x j,k+1 -x j,k |≤Δ j ,j＝1,2,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, the class center of each typical performance state is calculatedAnd 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 of the thermal power unit regenerative heater performance state is used for constructing the evidence representation of the thermal power unit regenerative heater performance state, and the method of describing the heater performance state by adopting the evidence is particularly adopted, so that inaccurate and uncertain information can be processed, and the evolution process of the heater performance from one state to other states (including transition states) can be effectively described.
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 generating unit regenerative heater performance state provided by an embodiment of the application;
FIG. 2 is a diagram illustrating an example of steady-state process determination according to an embodiment of the present application;
FIG. 3 is a class diagram illustrating exemplary performance states and transition states provided by embodiments of the present application;
fig. 4 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 regenerative heater device of the thermal power generating unit heats the water supply (in a high-pressure heater) or the condensate (in a low-pressure heater) by utilizing regenerative extraction of a steam turbine through heat transfer modes such as condensation heat release and the like, thereby realizing the aim of improving the average heat absorption temperature of the steam-water working medium.
For each high-pressure (or low-pressure) heater, increasing the outlet water temperature of each heater under a given operating condition is an important indicator for measuring the operating performance of each heater.
(1) Basic principle of heater:
because the main heat transfer mode affecting the regenerative heater of the thermal power generating unit is the heat release of condensation on the steam side and the heat absorption of convection on the water supply (or condensate) side, the water outlet temperature is determined by the following relationship.
twj＝tsj-dtj (1)
In formula (1), twj is the outlet water temperature of the jth stage heater in degrees celsius.
In equation (1), tsj is the saturation temperature in degrees celsius for the jth stage heater vapor side pressure.
In the formula (1), dtj is the heat transfer end difference of the j-th stage, and the constant related to the heat transfer area, the flow mode of the cold and hot working media and other factors is generally-2 to 3 in terms of ℃.
tsj＝f1(pnj) (2)
In equation (2), pnj is the heater vapor side pressure in MPa.
In formula (2), f1 is the water and water vapor property relationship between pressure and its saturation temperature (IAPWS-IF 97).
pnj＝pj*dpj (3)
In the formula (3), pj is the j-th stage extraction pressure in MPa.
In formula (3), dpj is the pressure drop rate of the j-th stage steam extraction pipeline, and the constant related to factors such as the pipeline length, the number of bent pipes and reducer pipes is generally 3-6, and the unit is%.
Pj＝f2(Pel) (4)
In equation (4), pel is the unit load, and the unit is MW.
In the formula (4), f2 is a relationship between the turbine extraction pressure and the load, and generally satisfies the relationship of fee Lv Geer.
In summary, the outlet water temperature twj is a main performance index of the regenerative heater, and its main influencing factors include the operation parameter Pel and parameters depending on the device structure (such as the length of the pipe, the number of bent pipes and reducing pipes, the heated area, and the flow mode of the cold and hot working media) dpj, dtj, etc., for a given unit determined by the structure, dpj and dtj are parameters of random load variation, that is, the outlet water temperature of the heater is a parameter that varies with the load.
Therefore, the unit load Pel and the outlet water temperature twj can be defined as the process parameters reflecting the performance state of the regenerative heater as follows:
X:＝(x 1 ,x 2 )′:＝(Pel,twj)′ (5)
in equation (5), the vector transpose is represented by "'".
In equation (5), X is a process parameter (column) vector reflecting the regenerative heater, x1=pel, x2= twj.
(2) Data mining of heater typical performance states
Data mining analysis is carried out on the regenerative heater to obtain a data sample X i ＝(x 1i ,x 2i ) ' i=1, 2, …, and the typical performance states { ω ] contained in these data samples l I=1, 2, …, the method is as follows:
judging whether the heater reaches or is in a quasi-steady state process according to the following (6):
|x j,k+1 -x j,k |≤Δ j ,j＝1,2,k＝1,2,3,…(6)
in formula (6), Δ j The jth process parameter x for the heater j Is generally set to a constant greater than the standard deviation.
Given a time step K, when K is greater than or equal to K, the heater is considered to have reached a quasi-steady state process. Once the heater 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,k＝1,2,3,…,N l } (7)
In formula (7), N l Is the data sample size acquired in the first quasi-steady state process.
After obtaining c quasi-steady-state processes different from each other, c heater typical performance states { ω l L=1, 2, …, c. The data samples of these quasi-steady state processes constitute a data sample set of the heater:
in equation (8), the symbol U is collective and operates.
In equation (8), N is the number of samples of the obtained data sample set X.
(3) Evidence characterization and definition of heater 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 heater 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 the formula (9), 2 Ω Is a power set of the set Ω.
In formula (9), a is any subset of Ω.
In equation (9), m (A) is the confidence that the current performance state of the heater belongs to state set A.
Set a describes the inaccuracy of the heater current state value, while m (a) describes the confidence, i.e., uncertainty, that the current performance state belongs to state set a. For example, assume that the regenerative heater 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 The case of } = 0.7 describes that the heater performance is being from state { ω } 1 Transition state { omega } to 1 ,ω 2 Diffraction variations of 0.3 and 0.7, respectively, characterize the uncertainty of heater membership to different performance states, a= { ω 1 ,ω 2 Inaccuracy of heater performance state is described.
(4) Construction of heater performance State evidence mi
First, 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 the heater.
Second, calculate the degree of probability (/ uncertainty) that each data sample is affiliated with each performance state:
in the formula(14) In,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 the heater.
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 heater performance status (set)
Based on the above description, on the basis that a heater process parameter data sample X is available, a evidence characterization (set) for reflecting the regenerative heater 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 the regenerative heater performance state.
Specifically, as shown in fig. 1, the evidence characterization construction method for the performance state of the regenerative heater of the thermal power generating unit provided by the embodiment of the application includes:
S1O1: samples of unit load and the water outlet temperature of the regenerative heater are collected according to a preset period.
During normal operation of the thermal power plant, data samples (unit load x 1 =pel, outlet water temperature x 2 = twj) 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: and acquiring a data sample set of the regenerative heater in a quasi-steady state process from the sample.
Setting a time step K, and when K is more than or equal to K, enabling the regenerative heater to be in a quasi-steady state process;
data sample set X with regenerative heater in quasi-steady state process l ：X l ＝{X k ||x j,k+1 -x j,k |≤Δ j ,j＝1,2,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 exemplary performance state { ω }, 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.
S1O3: 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: class core for calculating each typical performance stateAnd class I of transitional performance states>
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, …, c-1And->
S1O6: 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: and constructing a regenerative heater performance state evidence characterization.
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 thermal power generating unit regenerative heater performance state 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 =pel, outlet water temperature x 2 = twj) pretreatment. The sampling duration of the present example is about 4000 minutes, the original running data is 1 second data sample, the arithmetic average value of 60 cycles is taken as a representative data sample, and 4055 samples are finally selected.
Step 2: for selected unit load x 1 =pel, outlet water temperature x 2 = twj, the threshold of change Δ of a given parameter 1 ＝4,Δ 2 =5, judging whether the heater is in a quasi-steady state process according to the formula (6). The specific implementation is as follows: (1) initial setting l=1, if N l >K=20, the first performance state { ω 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 performance states of l=24 is obtained. The steady state process determination schematic diagram in this example is shown in fig. 2.
Step 3: calculating each performance state { ω ] according to (11) l Heart-like of l=1, 2, …,24(where the superscript "0" indicates the initial class center) and back-calculating the original variable space from equation (13)Heart-like part of (A)>Wherein, the class centers are ordered according to ascending load, and the results are respectively:
step 4: calculate each typicalPerformance 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. 3.
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, = {690.1mw,274.3 ℃ }, the calculated confidence level for each performance state is:
m i ({ω 1 })＝0.007898；m i ({ω 1 ,ω 2 })＝0.01353；m i ({ω 2 })＝0.02831；m i ({ω 2 ,ω 3 })＝0.8446；
m i ({ω 3 })＝0.07040；m i ({ω 3 ,ω 4 })＝0.01931；m i ({ω 4 })＝0.008863；m i ({ω 4 ,ω 5 })＝0.004417；
m i ({ω 5 })＝0.002637。
from the results X i = {690.1mw,274.3 ℃ } should belong to ω 2 And omega 3 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 calculated by 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 4, and the first part of the section belongs to { omega ] 2 Then pass through transition class { omega } 2 ,ω 3 Transition to { omega ] 3 And then pass through transition class { omega } 2 ,ω 3 Re-transition to { omega ] 2 Evidence constructed from the present example can be seen in fig. 4 to describe the above process.
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. The evidence characterization construction method of the thermal power generating unit regenerative heater performance state is characterized by comprising the following steps:
collecting samples of unit load and water outlet temperature of a regenerative heater according to a preset period;
acquiring a data sample set of a regenerative heater 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>The transition performance state is obtained after a plurality of transitionsPerformance state of (2);
calculating the credibility of each performance state of the data sample membership;
constructing a regenerative heater performance state evidence representation;
acquiring a data sample set of a regenerative heater in a quasi-steady state process from the sample, comprising:
setting a time step K, and when K is more than or equal to K, enabling the regenerative heater to be in a quasi-steady state process;
data sample set X with regenerative heater in quasi-steady state process l ：X l ＝{X k ||x j,k+1 -x j,k |≤Δ j ,j＝1,2,k＝1,2,3,…,N l }，N l Data samples collected in the first quasi-steady state process;
computing an initial class core for each performance state of the data sample setComprising the following steps:
wherein (1)> Is X l In (c) k data samples, a-> Respectively X l Medium maximum and minimum;
based on the initial cores of the performance states, the statistics of typical performance states in the performance states comprise:
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 two types of data samples are combined;
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；
Class core for calculating each typical performance stateAnd class I of transitional performance states>Comprising the following steps:
class core of typical performance stateClass I of transitional Performance State>
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