CN105912878A - Gas turbine adaptive gas circuit component performance diagnostic method based on combination of thermal model and particle swarm optimization - Google Patents

Abstract

The invention aims at providing a gas turbine adaptive gas circuit component performance diagnostic method based on the combination of a thermal model and particle swarm optimization. The method comprises the steps that the gas turbine nonlinear thermal model is established, gas circuit health indexes of a gas compressor and a turbine are redefined with similarity reduced parameters, gar circuit measurement parameters in a certain time frame in the stable running process of a current object gas turbine are collected and subjected to noise reduction processing and then serve as measurement parameters of a gas circuit to be subjected to offline diagnosis, and gas circuit health indexes of current components are obtained through iteration optimizing calculation of the particle swarm optimization and used for evaluating the practical performance health condition of the object gas turbine. By means of the method, the problem that the diagnostic precision of a traditional gas turbine gas circuit component performance diagnostic method is likely to be affected by changes of environment conditions and operation conditions is solved, the local optimization feature of the traditional diagnostic algorithm is changed, the accuracy of a diagnostic result is improved, the diagnostic process is simplified, and the method can be effectively applied to performance diagnostic situations with measurement noise and for complex gas turbine units.

Description

The gas turbine self adaptation gas path component performance diagnogtics side combined with particle swarm optimization algorithm based on thermodynamic model Method

Technical field

The present invention relates to a kind of diagnostic method, specifically gas turbine engine performance diagnogtics method.

Background technology

In the second half in 20th century, along with gas turbine is extensively applied in aircraft industry, more and more it is subject to Industry field, power station, oil and natural gas pipeline transport and the concern of naval vessel industrial circle.It is in operation, Owing to high temperature, high pressure, high rotating speed and heavily stressed poor working conditions and environmental pollution etc. affect, various types of The gas turbine of type all can gradually performance degradation.The main gas path component of gas turbine comprises compressor, burning Room and turbine.These critical pieces can suffer different degradation phenomenas over time, such as dirt, leakage, corruption Erosion, thermal distoftion, exotic damage etc., it will cause penalty and be easily caused various serious fault generation Run safety problem.Therefore, for gas turbine user, the performance health status of current gas turbine It is very important information.

The maintenance policy of current most of gas turbine is preventive maintenance, generally according to combustion engine manufacturer The equivalent hours of operation (EOH) of instruction consider whether to need light maintenance, in repair, overhaul.Combustion engine is stopped transport, The most inside the plan or unplanned, always mean the cost price of costliness.In order to save maintenance cost, User needs the performance health status according to combustion engine is actual to take maintenance policy, i.e. prospective maintenance maintenance.

Gas path component performance diagnogtics method based on thermodynamic model decision-making has been widely used for gas turbine performance In health status monitoring, and one of have become as the key technology of backing up maintenance strategy reform.Common base Gas path component performance diagnogtics method in thermodynamic model decision-making uses the performance parameter (absolute reference) of gas path component Carrying out definition component health index, therefore before diagnosis, gas circuit is measured parameter and is needed to carry out data prediction and eliminate Environmental condition or operating condition and working medium change of component and cause the impact that gas turbine operation performance changes.And And due to component capabilities parameter for self adaptation variable, so typically requiring in two steps during diagnosis: the first step is Measure parameter according to gas circuit and be calculated component capabilities by linearly or nonlinearly newton-Lai Pusheng iterative algorithm Parameter, such as temperature, turbine isentropic efficiency before air mass flow, compressor pressure ratio, compressor isentropic efficiency, turbine Etc. absolute performance parameter；Second step is to compare the portion in the case of actual performance fails on same characteristics of components figure Part operating point and the parts operating point under healthy base case, thus observe the characteristic on now characteristics of components figure There is the degree (i.e. obtaining gas path component health index) of skew in line, thus the performance assessing current part is good for Health situation.When the part count participating in diagnosis in gas turbine increases, the dimension of failure coefficient matrix can be with Increase, disturbed by measurement noise in addition, (although i.e., some parts is actually and acomia for blurring effect Raw performance degradation, but the performance degradation situation being diagnosed to be almost is distributed on all of parts gas circuit health index) May be very strong, the parts causing actual performance to fail are unrecognized out.

Summary of the invention

It is an object of the invention to provide and can be effectively applicable to there is measurement noise and complicated gas turbine unit The gas turbine self adaptation gas path component performance diagnogtics side combined with particle swarm optimization algorithm based on thermodynamic model Method.

The object of the present invention is achieved like this:

The gas turbine self adaptation gas path component that the present invention combines with particle swarm optimization algorithm based on thermodynamic model Performance diagnogtics method, is characterized in that:

(1) gas circuit when newly putting into operation based on object gas turbine or be healthy is measured parameter and is set up gas turbine non-thread Property thermodynamic model, wherein compressor all represents by similar reduced parameters form with turbine；

(2) redefine the gas circuit health index of compressor and turbine with similar reduced parameters, eliminate due to ring The impact that border condition changes and causes gas turbine operation performance to change；

(3) gas circuit of period when gathering existing object gas turbine stable operation measures parameter, carries out After noise reduction process, the gas circuit as diagnosis to be taken off-line measures parameter；

(4) the environment initial conditions of the gas turbine heating power model set up is set and operates initial conditions and adopt Object gas turbine operation operating mode during sample is consistent, eliminates owing to environmental condition and operating condition change and cause The impact of gas turbine operation performance change；

(5) measure with the gas circuit of diagnosis to be taken off-line between the gas path parameter data of parameter and thermodynamic model calculating Root-mean-square error is object function, obtains each current portion by particle swarm optimization algorithm iteration optimizing The gas circuit health index of part, in order to the performance health status that evaluation object gas turbine is actual.

The present invention can also include:

1, step (1) sets up specifically comprising the following steps that of gas turbine Nonlinear Thermal model

A () utilizes parts relative to reduced parameters, set up gas turbine component level thermodynamic model, wherein compressor General relative reduced parameters form is become with turbine characteristic line data compilation:

It is as follows that compressor characteristic curves is organized into general similar reduced parameters form:

G_C,cor,rel=f (n_cor,rel,π_C,rel)

η_C,rel=f (n_cor,rel,π_C,rel)

WhereinFor relatively converting into rotating speed, n is actual speed,Enter for compressor Mouth stagnation temperature, R_gFor flowing through the gas constant of compressor working medium, subscript 0 represents design point；

For relative corrected flow, G_CFor actual compressor inlet stream Amount,For compressor inlet stagnation pressure,For relative pressure ratio, π_CFor actual compressor pressure ratio, η_C,rel=η_C/η_C0For relative isentropic efficiency, η_CFor actual compressor isentropic efficiency；

It is as follows that turbine characteristic line is organized into general similar reduced parameters form:

G_T,cor,rel=f (n_cor,rel,π_T,rel)

η_T,rel=f (n_cor,rel,π_T,rel)

In formula:For relatively converting into rotating speed, n is actual speed,For thoroughly Flat entrance stagnation temperature, R_gFor flowing through the gas constant of turbine working medium,For relative corrected flow, G_TFor actual turbine inlet flow rate,For relative pressure ratio, η_T,rel=η_T/η_T0For relative isentropic efficiency, η_TImitate for actual turbine constant entropy Rate, subscript 0 represents design point；

Gas circuit when () is newly put into operation according to the object gas turbine gathered or be healthy b measures parameter, progressively revises The characteristic line data of all parts, make the value of calculation of built gas turbine heating power model survey parameter with gas circuit Match, thus eliminate thermodynamic model and calculate the negatively influencing that error is brought to diagnostic result.

2, step (2) redefines the tool of gas circuit health index of compressor and turbine with similar reduced parameters Body step is as follows:

A () Gas Turbine health status is represented by the gas circuit health index of each parts, use phase Reduced parameters are redefined the gas circuit health index of compressor and turbine, to eliminate due to changes in environmental conditions And the negatively influencing brought to diagnostic result；

B () compressor gas circuit health index is defined as follows:

SF_C,FC=G_{C,cor,rel,deg}/G_C,cor,rel

ΔSF_C,FC=(G_{C,cor,rel,deg}-G_C,cor,rel)/G_C,cor,rel

SF_C,Eff=η_C,deg/η_C

ΔSF_C,Eff=(η_C,deg-η_C)/η_C

Wherein SF_C,FCFor compressor discharge characteristic index；G_{C,cor,rel,deg}For Capability of Compressor decline phase doubling interflow Amount；G_C,cor,relFor compressor health phase to corrected flow；SF_C,EffFor compressor efficiency performance index；η_C,deg Isentropic efficiency when failing for Capability of Compressor；η_CFor isentropic efficiency during compressor health；

Combustor gas circuit health index is defined as follows:

SF_B,Eff=η_B,deg/η_B

ΔSF_B,Eff=(η_B,deg-η_B)/η_B

Wherein SF_B,EffFor combustion efficiency of combustion chamber performance index；η_B,degEfficiency of combustion when failing for chamber performance；η_B For efficiency of combustion during combustor health；

Turbine gas circuit health index is defined as follows:

SF_T,FC=G_T,cor,deg/G_T,cor

ΔSF_T,FC=(G_T,cor,deg-G_T,cor)/G_T,cor

SF_T,Eff=η_T,deg/η_T

ΔSF_T,Eff=(η_T,deg-η_T)/η_T

Wherein SF_T,FCFor turbine flow performance index；G_T,cor,degCorrected flow when failing for Turbine Performance； G_T,corFor corrected flow during turbine health；SF_T,EffFor efficiency of turbine performance index；η_T,degDecline for Turbine Performance Isentropic efficiency when moving back；η_TFor isentropic efficiency during turbine health.

3, step (5) measures, with the gas circuit of diagnosis to be taken off-line, the gas path parameter number that parameter calculates with thermodynamic model Root-mean-square error between according to is object function, is obtained currently by particle swarm optimization algorithm iteration optimizing The gas circuit health index of all parts, in order to the tool of the actual performance health status of evaluation object gas turbine Body step is as follows:

A () is with the gas circuit measurement data of diagnosis to be taken off-lineThe gas path parameter data calculated with thermodynamic modelIt Between root-mean-square error be object function Fitness:

$Fitness=\sqrt{\frac{\underset{i=1}{\overset{M}{\Σ}}{\[(z_{i,predicted}-z_{i,actual})/z_{i,actual}\]}^2}{M}}$

B gas circuit health that () obtains current all parts by particle swarm optimization algorithm iteration optimizing refers to Number, in order to the component capabilities health status that evaluation object gas turbine is actual,

$\hat{z}=\[z_{1,predicted},...,z_{i,predicted},...,z_{M,predicted}\]$

$\stackrel{\→}{z}=\[z_{1,actual},...,z_{i,actual},...,z_{M,actual}\]$

WhereinThe gas circuit calculated for gas turbine heating power model measures parameter vector；Position actual measurement gas path parameter to Amount；M is that gas circuit measures number of parameters；

$\mathrm{\Δ}\stackrel{\→}{SF}=\[{\mathrm{\ΔSF}}_{C,FC},{\mathrm{\ΔSF}}_{C,Eff},{\mathrm{\ΔSF}}_{B,Eff},{\mathrm{\ΔSF}}_{T,FC},{\mathrm{\ΔSF}}_{T,Eff}\]$

WhereinFor gas path component health index；

With a root-mean-square error as object function, such as following formula:

$Fitness=\sqrt{\frac{\underset{i=1}{\overset{M}{\Σ}}{\[(z_{i,predicted}-z_{i,actual})/z_{i,actual}\]}^2}{M}}$

In formula, Fitness is optimization aim, along with iteration optimizing, when Fitness level off to 0 time, the gas of calculating Drive test amount parameterGas path parameter with actual measurementMatch, now export final globally optimal solution

Present invention have an advantage that

(1) present invention is according to gas turbine aerothermodynamics characteristic, utilizes parts relative to reduced parameters, sets up Gas turbine component level thermodynamic model, wherein compressor becomes general phase doubling with turbine characteristic line data compilation Close parametric form, compared to prior art, can more be used simply and conveniently for during gas circuit diagnosis arranging each gas circuit Parts health index.

(2) the gas circuit health index that the present invention redefines compressor and turbine with relative reduced parameters (represents The skew of characteristics of components line), compared to prior art, can characterize more accurately due to component capabilities decline The change of the gas circuit health index caused, eliminates due to environmental condition (atmospheric pressure, temperature and relative humidity) The negatively influencing changed and bring to diagnostic result.

(3) present invention is from the angle of global optimization (employing particle swarm optimization algorithm), improves conventional diagnostic The characteristic of algorithm (Newton-Raphson iteration algorithm) local optimal searching, improves the accuracy of diagnostic result, and letter Change diagnosis process, can be effectively applicable to there are measurement noise and the performance diagnogtics feelings of complicated gas turbine unit Condition.

Accompanying drawing explanation

Fig. 1 is the flow chart of the present invention；

Fig. 2 is the schematic diagram of the diagnosis process of the present invention；

Fig. 3 is certain type three axle marine gas turbine gas circuit working sections mark figure；

Fig. 4 is the diagnostic result of certain type three axle marine gas turbine diagnosis case；

Fig. 5 is the inventive algorithm iterative computation search procedure of this diagnosis case.

Detailed description of the invention

Illustrate below in conjunction with the accompanying drawings and the present invention be described in more detail:

In conjunction with Fig. 1-5, the gas turbine that the present invention combines with particle swarm optimization algorithm based on thermodynamic model from Adapt to gas path component performance diagnogtics method, comprise the following steps:

Step 1), the gas circuit measurement parameter foundation newly put into operation time (or healthy) based on object gas turbine can be complete Being all-trans and reflect the gas turbine Nonlinear Thermal model of all parts characteristic, wherein compressor is all used similar with turbine Reduced parameters form represents；

Step 2), redefine the gas circuit health index of compressor and turbine with similar reduced parameters, eliminate by Gas turbine operation performance is caused to change in environmental condition (atmospheric pressure, temperature and relative humidity) change Impact；

Step 3), the gas circuit of a certain period when gathering existing object gas turbine stable operation measures parameter, After carrying out noise reduction process, the gas circuit as diagnosis to be taken off-line measures parameter；

Step 4), environment initial conditions (atmospheric pressure, the temperature of the gas turbine heating power model set up are set Spend and relative humidity) consistent with object gas turbine operation operating mode during sampling with operation initial conditions, eliminate Owing to environmental condition (atmospheric pressure, temperature and relative humidity) and operating condition change and cause gas turbine The impact of runnability change.

Step 5), the gas path parameter data calculated with the gas circuit measurement data of diagnosis to be taken off-line and thermodynamic model it Between root-mean-square error be object function, obtained current each by particle swarm optimization algorithm iteration optimizing The gas circuit health index of individual parts (compressor, turbine and combustor), real in order to evaluation object gas turbine The performance health status on border.

The gas turbine self adaptation gas circuit combined with particle swarm optimization algorithm based on thermodynamic model as the present invention The further prioritization scheme of component capabilities diagnostic method, step 1) described in newly throw based on object gas turbine Gas circuit during fortune (or healthy) is measured parameter and is set up the gas turbine non-thread that can reflect all parts characteristic completely Specifically comprising the following steps that of property thermodynamic model

Step 1.1), according to gas turbine aerothermodynamics characteristic, utilize parts relative to reduced parameters, set up Gas turbine component level thermodynamic model, wherein compressor becomes general phase doubling with turbine characteristic line data compilation Close parametric form.

Wherein to be organized into general similar reduced parameters form as follows for compressor characteristic curves:

G_C,cor,rel=f (n_cor,rel,π_C,rel)

η_C,rel=f (n_cor,rel,π_C,rel)

WhereinFor relatively converting into rotating speed, n is actual speed,Enter for compressor Mouth stagnation temperature, R_gFor flowing through the gas constant of compressor working medium, subscript 0 represents design point；

For relative corrected flow, G_CFor actual compressor inlet stream Amount,For compressor inlet stagnation pressure,For relative pressure ratio, π_CFor actual compressor pressure ratio, η_C,rel=η_C/η_C0For relative isentropic efficiency, η_CFor actual compressor isentropic efficiency.

It is as follows that turbine characteristic line is organized into general similar reduced parameters form:

G_T,cor,rel=f (n_cor,rel,π_T,rel)

η_T,rel=f (n_cor,rel,π_T,rel)

In formula:For relatively converting into rotating speed, n is actual speed,Enter for turbine Mouth stagnation temperature, R_gFor flowing through the gas constant of turbine working medium,For phase To corrected flow, G_TFor actual turbine inlet flow rate,For relative pressure ratio, η_T,rel=η_T/η_T0For relatively Isentropic efficiency, η_TFor actual turbine isentropic efficiency, subscript 0 represents design point.

Step 1.2), measure parameter according to the gas circuit that the object gas turbine gathered newly puts into operation time (or healthy) (after noise reduction process), such as stagnation temperature, stagnation pressure, rotating speed etc., progressively revises the characteristic line data (bag of all parts Include design conditions and variable working condition), make the value of calculation of built gas turbine heating power model survey parameter phase with gas circuit Coupling, thus eliminate thermodynamic model and calculate the negatively influencing that error is brought to diagnostic result.

The gas turbine self adaptation gas circuit combined with particle swarm optimization algorithm based on thermodynamic model as the present invention The further prioritization scheme of component capabilities diagnostic method, step 2) described in again fixed with similar reduced parameters Specifically comprising the following steps that of the gas circuit health index of justice compressor and turbine

Step 2.1), Gas Turbine health status can by the gas circuit health index of each critical piece, As the discharge characteristic index of compressor and turbine and the efficiency characteristic index of efficiency characteristic index, combustor carry out table Show.Here redefine the gas circuit health index of compressor and turbine with relative reduced parameters and (represent parts The skew of characteristic line), give due to environmental condition (atmospheric pressure, temperature and relative humidity) change to eliminate The negatively influencing that diagnostic result brings.

Step 2.2), compressor gas circuit health index is defined as follows:

SF_C,FC=G_{C,cor,rel,deg}/G_C,cor,rel

ΔSF_C,FC=(G_{C,cor,rel,deg}-G_C,cor,rel)/G_C,cor,rel

SF_C,Eff=η_C,deg/η_C

ΔSF_C,Eff=(η_C,deg-η_C)/η_C

Wherein SF_C,FCFor compressor discharge characteristic index；G_{C,cor,rel,deg}The most equivalent for compressor (during performance degradation) Flow；G_C,cor,relFor compressor (time healthy) corrected flow relatively；SF_C,EffFor compressor efficiency performance index； η_C,degFor compressor (during performance degradation) isentropic efficiency；η_CFor compressor (time healthy) isentropic efficiency.

Combustor gas circuit health index is defined as follows:

SF_B,Eff=η_B,deg/η_B

ΔSF_B,Eff=(η_B,deg-η_B)/η_B

Wherein SF_B,EffFor combustion efficiency of combustion chamber performance index；η_B,degFor combustor (during performance degradation) efficiency of combustion； η_BFor combustor (time healthy) efficiency of combustion.

Turbine gas circuit health index is defined as follows:

SF_T,FC=G_T,cor,deg/G_T,cor

ΔSF_T,FC=(G_T,cor,deg-G_T,cor)/G_T,cor

SF_T,Eff=η_T,deg/η_T

ΔSF_T,Eff=(η_T,deg-η_T)/η_T

Wherein SF_T,FCFor turbine flow performance index；G_T,cor,degFor turbine (during performance degradation) corrected flow；G_T,cor For turbine (time healthy) corrected flow；SF_T,EffFor efficiency of turbine performance index；η_T,degFor turbine, (performance declines When moving back) isentropic efficiency；η_TFor turbine (time healthy) isentropic efficiency.

The gas turbine self adaptation gas circuit combined with particle swarm optimization algorithm based on thermodynamic model as the present invention The further prioritization scheme of component capabilities diagnostic method, step 5) described in survey with the gas circuit of diagnosis to be taken off-line Root-mean-square error between the gas path parameter data that amount data and thermodynamic model calculate is object function, by grain Subgroup optimized algorithm iteration optimizing obtains current all parts (compressor, turbine and combustor) Gas circuit health index, in order to specifically comprising the following steps that of the actual performance health status of evaluation object gas turbine

Step 5.1), with the gas circuit measurement data of diagnosis to be taken off-lineThe gas path parameter number calculated with thermodynamic model According toBetween root-mean-square error be object function Fitness:

$Fitness=\sqrt{\frac{\underset{i=1}{\overset{M}{\Σ}}{\[(z_{i,predicted}-z_{i,actual})/z_{i,actual}\]}^2}{M}}$

Step 5.2), by particle swarm optimization algorithm iteration optimizing obtain current all parts (compressor, Turbine and combustor) gas circuit health index, in order to the component capabilities health shape that evaluation object gas turbine is actual Condition.The gas turbine self adaptation gas path component performance diagnogtics combined with particle swarm optimization algorithm based on thermodynamic model The schematic diagram of method is as shown in Figure 2.

Wherein,

$\hat{z}=\[z_{1,predicted},...,z_{i,predicted},...,z_{M,predicted}\]$

$\stackrel{\→}{z}=\[z_{1,actual},...,z_{i,actual},...,z_{M,actual}\]$

WhereinThe gas circuit calculated for gas turbine heating power model measures parameter vector；Position actual measurement gas path parameter to Amount；M is that gas circuit measures number of parameters；

$\mathrm{\Δ}\stackrel{\→}{SF}=\[{\mathrm{\ΔSF}}_{C,FC},{\mathrm{\ΔSF}}_{C,Eff},{\mathrm{\ΔSF}}_{B,Eff},{\mathrm{\ΔSF}}_{T,FC},{\mathrm{\ΔSF}}_{T,Eff}\]$

WhereinGas path component health index (particle as in particle swarm optimization algorithm).

Here with a root-mean-square error as object function, such as formula:

$Fitness=\sqrt{\frac{\underset{i=1}{\overset{M}{\Σ}}{\[(z_{i,predicted}-z_{i,actual})/z_{i,actual}\]}^2}{M}}$

In formula, Fitness is optimization aim, along with iteration optimizing, when Fitness moves closer in 0 time, and the gas circuit of calculating Measure parameterGas path parameter with actual measurementMatch, now export final globally optimal solution

The core technology content of the present invention is that solving conventional gas turbine gas path component performance diagnogtics method examines Disconnected precision is easily by asking that environmental condition (atmospheric pressure, temperature and relative humidity) and operating condition change are affected Topic, and from the angle of global optimization, improve conventional diagnostic algorithm (Newton-Raphson iteration algorithm) local and seek Excellent characteristic, improves the accuracy of diagnostic result, simplifies diagnosis process, can be effectively applicable to exist and survey Amount noise and the performance diagnogtics situation of complicated gas turbine unit.According to gas turbine aerothermodynamics characteristic, Utilizing parts relative to reduced parameters, set up gas turbine component level thermodynamic model, wherein compressor and turbine are special Property line data compilation becomes general relative reduced parameters form, compared to prior art, and can be more simply and easily Each gas path component health index was set when gas circuit diagnoses；Compressor is redefined with relative reduced parameters With the gas circuit health index (representing the skew of characteristics of components line) of turbine, compared to prior art, can be more accurate Really characterize the change of the gas circuit health index caused due to component capabilities decline, eliminate due to environmental condition The negatively influencing that (atmospheric pressure, temperature and relative humidity) changes and bring to diagnostic result；From global optimization The angle of (employing particle swarm optimization algorithm), improves conventional diagnostic algorithm (Newton-Raphson iteration algorithm) The characteristic of local optimal searching, improves the accuracy of diagnostic result, and simplifies diagnosis process, can be the most applicable In there is measurement noise and the performance diagnogtics situation of complicated gas turbine unit.

As a example by the gas path component Gernral Check-up of certain type three axle marine gas turbine, its this type three axle combustion gas peculiar to vessel is taken turns Machine gas circuit working sections mark figure is as shown in Figure 3.This type three-spool gas turbine includes two compressors, and (i.e. one low Pressure compressor (LC) and a high-pressure compressor (HC)), (i.e. one high pressure is saturating for a combustor (B) and three turbines Flat (HT), a low pressure turbine (LT) and a power turbine (PT)), wherein electromotor passes through a reduction gearing Case is connected with power turbine (PT).The output work of low pressure turbine (LT) drives low-pressure compressor by low-pressure shaft (LC) compressing from air intake duct air out, the output work of high pressure turbine (HT) drives high pressure pressure by high-pressure shaft Mechanism of qi (HC) continues to compress from low-pressure compressor (LC) air out.From high-pressure compressor (HC) height out Pressure air enters combustor (B) and reacts generation high temperature, the combustion gas of high pressure with fuel generation combustion chemistry, and combustion gas depends on Secondary entrance high pressure turbine (HT), low pressure turbine (LT) and power turbine (PT) drive turbine output work.Finally, Power turbine (PT) drives electromotor to produce electrical power by reduction gear box.Meanwhile, extract from compressor Cooling air flow into hot junction gas channel and remove to cool down what stator blade before each turbine, movable vane and wheel disc.Work as combustion During gas-turbine stable operation, the rotating speed of the electrical power of electromotor and power turbine (PT) is joined usually used as major control Count and remain permanent.It is as shown in table 1 that the gas circuit of this unit measures parameter, and the health index of each gas path component is such as Shown in table 2.

The gas circuit of this type gas turbine unit of table 1 measures parameter

The health index of the main gas path component of table 2

The gas circuit measurement parameter foundation newly put into operation time (or healthy) based on this type gas turbine can reflect each completely The gas turbine Nonlinear Thermal model of individual characteristics of components, wherein relative reduced parameters all used by compressor with turbine Form represents.

Wherein to be organized into general similar reduced parameters form as follows for compressor characteristic curves:

G_C,cor,rel=f (n_cor,rel,π_C,rel)

η_C,rel=f (n_cor,rel,π_C,rel)

WhereinFor relatively converting into rotating speed, n is actual speed,Enter for compressor Mouth stagnation temperature, R_gFor flowing through the gas constant of compressor working medium, subscript 0 represents design point；

For relative corrected flow, G_CFor actual compressor inlet stream Amount,For compressor inlet stagnation pressure,For relative pressure ratio, π_CFor actual compressor pressure ratio, η_C,rel=η_C/η_C0For relative isentropic efficiency, η_CFor actual compressor isentropic efficiency.

It is as follows that turbine characteristic line is organized into general similar reduced parameters form:

G_T,cor,rel=f (n_cor,rel,π_T,rel)

η_T,rel=f (n_cor,rel,π_T,rel)

In formula:For relatively converting into rotating speed, n is actual speed,For thoroughly Flat entrance stagnation temperature, R_gFor flowing through the gas constant of turbine working medium,For relative corrected flow, G_TFor actual turbine inlet flow rate,For relative pressure ratio, η_T,rel=η_T/η_T0For relative isentropic efficiency, η_TImitate for actual turbine constant entropy Rate, subscript 0 represents design point.

The thermodynamic model of object gas turbine is set up on Matlab emulation platform.The initial conditions of thermodynamic model is Environmental condition (atmospheric temperature t₀, pressure P₀, relative humidity RH), generated output power Ne (as operating condition), Fuel element, fuel Lower heat value, gas path component health index(for vibration analysis,)。 The calculating of thermodynamic model is output as fuel flow rate G_f, all parts import and export gas circuit section thermal parameter (as always Pressure, stagnation temperature) and rotating speed etc..

The gas circuit health index redefining compressor and turbine with relative reduced parameters (represents characteristics of components The skew of line), eliminate and cause combustion gas due to environmental condition (atmospheric pressure, temperature and relative humidity) change The impact of turbine runnability change.

Compressor gas circuit health index is defined as follows:

SF_C,FC=G_{C,cor,rel,deg}/G_C,cor,rel

ΔSF_C,FC=(G_{C,cor,rel,deg}-G_C,cor,rel)/G_C,cor,rel

SF_C,Eff=η_C,deg/η_C

ΔSF_C,Eff=(η_C,deg-η_C)/η_C

Wherein SF_C,FCFor compressor discharge characteristic index；G_{C,cor,rel,deg}The most equivalent for compressor (during performance degradation) Flow；G_C,cor,relFor compressor (time healthy) corrected flow relatively；SF_C,EffFor compressor efficiency performance index； η_C,degFor compressor (during performance degradation) isentropic efficiency；η_CFor compressor (time healthy) isentropic efficiency.

Combustor gas circuit health index is defined as follows:

SF_B,Eff=η_B,deg/η_B

ΔSF_B,Eff=(η_B,deg-η_B)/η_B

Wherein SF_B,EffFor combustion efficiency of combustion chamber performance index；η_B,degFor combustor (during performance degradation) efficiency of combustion； η_BFor combustor (time healthy) efficiency of combustion.

Turbine gas circuit health index is defined as follows:

SF_T,FC=G_T,cor,deg/G_T,cor

ΔSF_T,FC=(G_T,cor,deg-G_T,cor)/G_T,cor

SF_T,Eff=η_T,deg/η_T

ΔSF_T,Eff=(η_T,deg-η_T)/η_T

Wherein SF_T,FCFor turbine flow performance index；G_T,cor,degFor turbine (during performance degradation) corrected flow；G_T,cor For turbine (time healthy) corrected flow；SF_T,EffFor efficiency of turbine performance index；η_T,degFor turbine, (performance declines When moving back) isentropic efficiency；η_TFor turbine (time healthy) isentropic efficiency.

The gas circuit of a certain period when gathering existing object gas turbine stable operation measures parameter, carries out at noise reduction After reason, the gas circuit as diagnosis to be taken off-line measures parameter.

Environment initial conditions (atmospheric temperature t of the gas turbine heating power model set up is set₀, pressure P₀, phase To humidity RH) and operate initial conditions (generated output power Ne) and object gas turbine operation during sampling Operating mode is consistent, eliminates the shadow changing due to environmental condition and operating condition and causing gas turbine operation performance to change Ring.

Gas circuit measurement data with diagnosis to be taken off-lineThe gas path parameter data calculated with thermodynamic modelBetween Root-mean-square error is object function Fitness, obtains current by particle swarm optimization algorithm iteration optimizing The gas circuit health index of all parts (compressor, turbine and combustor) (all parts, right in order to assess As the component capabilities health status that gas turbine is actual.Combine with particle swarm optimization algorithm based on thermodynamic model The diagnosis process schematic of gas turbine self adaptation gas path component performance diagnogtics method is as shown in Figure 1.

Wherein,

$\hat{z}=\[z_{1,predicted},...,z_{i,predicted},...,z_{M,predicted}\]$

$\stackrel{\→}{z}=\[z_{1,actual},...,z_{i,actual},...,z_{M,actual}\]$

WhereinThe gas circuit calculated for gas turbine heating power model measures parameter vector；Position actual measurement gas path parameter to Amount；M is that gas circuit measures number of parameters；

$\mathrm{\Δ}\stackrel{\→}{SF}=\[{\mathrm{\ΔSF}}_{C,FC},{\mathrm{\ΔSF}}_{C,Eff},{\mathrm{\ΔSF}}_{B,Eff},{\mathrm{\ΔSF}}_{T,FC},{\mathrm{\ΔSF}}_{T,Eff}\]$

WhereinGas path component health index (particle as in particle swarm optimization algorithm).

Here with a root-mean-square error as object function, such as formula:

$Fitness=\sqrt{\frac{\underset{i=1}{\overset{M}{\Σ}}{\[(z_{i,predicted}-z_{i,actual})/z_{i,actual}\]}^2}{M}}$

In formula, Fitness is optimization aim, along with iteration optimizing, when Fitness moves closer in 0 time, and the gas circuit of calculating Measure parameterGas path parameter with actual measurementMatch, now export final globally optimal solution

Arranging as shown in table 3 of particle swarm optimization algorithm relevant parameter, evolutionary generation is 80 here, population scale It is 60, for searching for the parts health index of optimum(as shown in table 3).

Choosing of table 3 particle swarm optimization algorithm relevant parameter

Parameter	Value
		Population scale	60
Evolutionary generation	80

By the above-mentioned gas turbine self adaptation gas circuit combined with particle swarm optimization algorithm based on thermodynamic model After the diagnosis algorithm of component capabilities diagnostic method, final diagnostic result can be obtained as shown in Figure 4.

Wherein, PSO-GPA is the gas turbine combined with particle swarm optimization algorithm based on thermodynamic model carried Self adaptation gas path component performance diagnogtics method, GPA is traditional diagnosis method.This diagnosis case based on heating power mould The iteration meter of the gas turbine self adaptation gas path component performance diagnogtics method that type combines with particle swarm optimization algorithm Calculate search procedure such as Fig. 5.

As can be seen from Figure 4, due to traditional diagnosis method core algorithm (Newton-Raphson iteration algorithm) substantially It is a kind of local iteration optimization method, and the combustion gas wheel combined with particle swarm optimization algorithm based on thermodynamic model The core algorithm of machine self adaptation gas path component performance diagnogtics method is substantially a kind of global iterative optimization method, Therefore the gas turbine self adaptation gas path component performance combined with particle swarm optimization algorithm based on thermodynamic model is examined Disconnected method can more effectively eliminate blurring effect, identification exactly, isolation performance decline than traditional diagnosis method Parts.

Claims (4)

1. the gas turbine self adaptation gas path component performance combined with particle swarm optimization algorithm based on thermodynamic model Diagnostic method, is characterized in that:

(1) gas circuit when newly putting into operation based on object gas turbine or be healthy is measured parameter and is set up gas turbine non-thread Property thermodynamic model, wherein compressor all represents by similar reduced parameters form with turbine；

(2) redefine the gas circuit health index of compressor and turbine with similar reduced parameters, eliminate due to ring The impact that border condition changes and causes gas turbine operation performance to change；

(3) gas circuit of period when gathering existing object gas turbine stable operation measures parameter, carries out After noise reduction process, the gas circuit as diagnosis to be taken off-line measures parameter；

(4) the environment initial conditions of the gas turbine heating power model set up is set and operates initial conditions and adopt Object gas turbine operation operating mode during sample is consistent, eliminates owing to environmental condition and operating condition change and cause The impact of gas turbine operation performance change；

(5) measure with the gas circuit of diagnosis to be taken off-line between the gas path parameter data of parameter and thermodynamic model calculating Root-mean-square error is object function, obtains each current portion by particle swarm optimization algorithm iteration optimizing The gas circuit health index of part, in order to the performance health status that evaluation object gas turbine is actual.

The combustion gas wheel combined with particle swarm optimization algorithm based on thermodynamic model the most according to claim 1 Machine self adaptation gas path component performance diagnogtics method, is characterized in that: set up gas turbine in step (1) non-linear Specifically comprising the following steps that of thermodynamic model

A () utilizes parts relative to reduced parameters, set up gas turbine component level thermodynamic model, wherein compressor General relative reduced parameters form is become with turbine characteristic line data compilation:

It is as follows that compressor characteristic curves is organized into general similar reduced parameters form:

G_C,cor,rel=f (n_cor,rel,π_C,rel)

η_C,rel=f (n_cor,rel,π_C,rel)

WhereinFor relatively converting into rotating speed, n is actual speed,Enter for compressor Mouth stagnation temperature, R_gFor flowing through the gas constant of compressor working medium, subscript 0 represents design point；

For relative corrected flow, G_CFor actual compressor inlet stream Amount,For compressor inlet stagnation pressure,For relative pressure ratio, π_CFor actual compressor pressure ratio, η_C,rel=η_C/η_C0For relative isentropic efficiency, η_CFor actual compressor isentropic efficiency；

It is as follows that turbine characteristic line is organized into general similar reduced parameters form:

G_T,cor,rel=f (n_cor,rel,π_T,rel)

η_T,rel=f (n_cor,rel,π_T,rel)

In formula:For relatively converting into rotating speed, n is actual speed,For thoroughly Flat entrance stagnation temperature, R_gFor flowing through the gas constant of turbine working medium,For relative corrected flow, G_TFor actual turbine inlet flow rate,For relative pressure ratio, η_T,rel=η_T/η_T0For relative isentropic efficiency, η_TImitate for actual turbine constant entropy Rate, subscript 0 represents design point；

Gas circuit when () is newly put into operation according to the object gas turbine gathered or be healthy b measures parameter, progressively revises The characteristic line data of all parts, make the value of calculation of built gas turbine heating power model survey parameter with gas circuit Match, thus eliminate thermodynamic model and calculate the negatively influencing that error is brought to diagnostic result.

The combustion gas wheel combined with particle swarm optimization algorithm based on thermodynamic model the most according to claim 2 Machine self adaptation gas path component performance diagnogtics method, is characterized in that: the similar reduced parameters of step (2) are again fixed Specifically comprising the following steps that of the gas circuit health index of justice compressor and turbine

A () Gas Turbine health status is represented by the gas circuit health index of each parts, use phase Reduced parameters are redefined the gas circuit health index of compressor and turbine, to eliminate due to changes in environmental conditions And the negatively influencing brought to diagnostic result；

B () compressor gas circuit health index is defined as follows:

SF_C,FC=G_{C,cor,rel,deg}/G_C,cor,rel

ΔSF_C,FC=(G_{C,cor,rel,deg}-G_C,cor,rel)/G_C,cor,rel

SF_C,Eff=η_C,deg/η_C

ΔSF_C,Eff=(η_C,deg-η_C)/η_C

Wherein SF_C,FCFor compressor discharge characteristic index；G_{C,cor,rel,deg}For Capability of Compressor decline phase doubling interflow Amount；G_C,cor,relFor compressor health phase to corrected flow；SF_C,EffFor compressor efficiency performance index；η_C,deg Isentropic efficiency when failing for Capability of Compressor；η_CFor isentropic efficiency during compressor health；

Combustor gas circuit health index is defined as follows:

SF_B,Eff=η_B,deg/η_B

ΔSF_B,Eff=(η_B,deg-η_B)/η_B

Wherein SF_B,EffFor combustion efficiency of combustion chamber performance index；η_B,degEfficiency of combustion when failing for chamber performance；η_B For efficiency of combustion during combustor health；

Turbine gas circuit health index is defined as follows:

SF_T,FC=G_T,cor,deg/G_T,cor

ΔSF_T,FC=(G_T,cor,deg-G_T,cor)/G_T,cor

SF_T,Eff=η_T,deg/η_T

ΔSF_T,Eff=(η_T,deg-η_T)/η_T

Wherein SF_T,FCFor turbine flow performance index；G_T,cor,degCorrected flow when failing for Turbine Performance； G_T,corFor corrected flow during turbine health；SF_T,EffFor efficiency of turbine performance index；η_T,degDecline for Turbine Performance Isentropic efficiency when moving back；η_TFor isentropic efficiency during turbine health.

The combustion gas wheel combined with particle swarm optimization algorithm based on thermodynamic model the most according to claim 3 Machine self adaptation gas path component performance diagnogtics method, is characterized in that: step (5) is surveyed with the gas circuit of diagnosis to be taken off-line Root-mean-square error between the gas path parameter data that amount parameter and thermodynamic model calculate is object function, by grain Subgroup optimized algorithm iteration optimizing obtains the gas circuit health index of current all parts, right in order to assess As specifically comprising the following steps that of the actual performance health status of gas turbine

A () is with the gas circuit measurement data of diagnosis to be taken off-lineThe gas path parameter data calculated with thermodynamic modelIt Between root-mean-square error be object function Fitness:

$Fitness=\sqrt{\frac{\underset{i=1}{\overset{M}{\Σ}}{\[(z_{i,predicted}-z_{i,actual})/z_{i,actual}\]}^2}{M}}$

B gas circuit health that () obtains current all parts by particle swarm optimization algorithm iteration optimizing refers to Number, in order to the component capabilities health status that evaluation object gas turbine is actual,

$\hat{z}=\[z_{1,predicted},...,z_{i,predicted},...,z_{M,predicted}\]$

$\stackrel{\→}{z}=\[z_{1,actual},...,z_{i,actual},...,z_{M,actual}\]$

WhereinThe gas circuit calculated for gas turbine heating power model measures parameter vector；Position actual measurement gas path parameter to Amount；M is that gas circuit measures number of parameters；

$\mathrm{\Δ}\stackrel{\→}{SF}=\[{\mathrm{\ΔSF}}_{C,FC},{\mathrm{\ΔSF}}_{C,Eff},{\mathrm{\ΔSF}}_{B,Eff},{\mathrm{\ΔSF}}_{T,FC},{\mathrm{\ΔSF}}_{T,Eff}\]$

WhereinFor gas path component health index；

With a root-mean-square error as object function, such as following formula:

$Fitness=\sqrt{\frac{\underset{i=1}{\overset{M}{\Σ}}{\[(z_{i,predicted}-z_{i,actual})/z_{i,actual}\]}^2}{M}}$

In formula, Fitness is optimization aim, along with iteration optimizing, when Fitness level off to 0 time, the gas of calculating Drive test amount parameterGas path parameter with actual measurementMatch, now export final globally optimal solution