CN105930302B - A kind of coal pulverizer method for diagnosing faults based on Fast Genetic Algorithm and grey-box model - Google Patents
A kind of coal pulverizer method for diagnosing faults based on Fast Genetic Algorithm and grey-box model Download PDFInfo
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Abstract
The invention discloses a kind of coal pulverizer method for diagnosing faults based on Fast Genetic Algorithm and grey-box model, Fast Genetic Algorithm and grey-box model are combined, a kind of novel coal mill method for diagnosing faults is obtained, including off-line model identification and the two processes of online breakdown judge.The present invention need not obtain the fault data of coal pulverizer with regard to that can carry out the judgement of fault type.Due to considering the mechanism characteristic of coal pulverizer internal physical process, there is higher precision than conventional method for diagnosing faults.Model is recognized using Fast Genetic Algorithm, effectively reduces the time overhead of identification process.
Description
Technical field
The present invention relates to thermal power plant control fields, and Fast Genetic Algorithm and grey-box model are based on more particularly to one kind
Coal pulverizer method for diagnosing faults.
Background technology
A Rapid development stage is passed through in China's power industry at present, and the number of thermal power plant and installed capacity have reached
Very huge quantity.In July, 2015, national whole month power generation have reached 4536.85 hundred million kilowatt hours, average daily power generation 146.35 hundred million
Kilowatt hour, generate electricity maximum day 152.81 hundred million kilowatt hours (July 12), adds up power generation year up to 28089.47 hundred million kilowatt hours.Although combustion
The technology of coal unit develops constantly；But at the same time, the reliability of some key equipments of thermal power plant, availability, can repair
Property the problem of security with also becoming increasingly conspicuous.
Power station coal pulverizer is the core equipment of boiler combustion pulverized coal preparation system, and working condition runs entire power plant system
Safety and economy have important influence.Such as the pressure of milling of coal pulverizer, ventilation quantity, the load in coal pulverizer etc., they
Size and exception will all influence production efficiency.As far as possible these states to be avoided to be abnormal in process of production, the exception of state
On the one hand productivity can be influenced, the failure of equipment on the other hand will be also caused, causes the loss of bigger.Therefore timely discovering device
It is abnormal, timely maintained equipment, it appears particularly important.It for example, during the hydraulic performance decline of coal pulverizer, should be safeguarded in time, to reduce
Powder-grinding energy consumption avoids pulverizer capacity deficiency that the power of the assembling unit is caused to decline；It, should be timely when the coal pulverizer of operation breaks down
It is overhauled, avoids causing the accident of unit outage due to coal pulverizer failure.Coal pulverizer generally has to work in the presence of a harsh environment,
Its course of work is a complicated process.Therefore the condition monitoring and fault diagnosis for realizing power station coal-grinding machine equipment is a tool
Challenging work.This is also always thermal power plant's coal-grinding production urgent problem to be solved.
The coal pulverizer method for diagnosing faults studied both at home and abroad at present can be mainly divided into two classes：Kernel model based diagnosis method
With the diagnostic method based on fault mode classification.The main distinction of the two is whether need the dynamic reference mould for establishing equipment
Type.
According to the method based on fault mode classification, it is necessary to while possess the fault mode data and normal mode of coal pulverizer
Formula data could the different fault type of enough grader differentiations.The data of coal pulverizer normal mode can be readily available, but
Data when being failure must could be obtained when coal pulverizer breaks down, therefore the coal pulverizer for not yet breaking down, this
Kind method is difficult to judge out of order type.
First have to establish the reference model of coal pulverizer normal operation mode using the method for diagnosing faults based on model.Root again
Fault diagnosis is carried out according to reference model output valve and the residual error of measured value.In this way, data during even if without coal pulverizer failure
The type of residual error can be drawn according to the analysis result of residual error.At home, the method based on grey-box model is not yet applied to coal-grinding
The fault diagnosis of machine.Grey-box model combines mechanism and data, can reflect real physical process inside coal pulverizer, compare cardinar number
There is higher precision according to the method for modeling.Therefore the precision of fault diagnosis can be improved using grey-box model.
For traditional genetic algorithm when recognizing grey-box model, time overhead is very big, and being often required to calculate a whole day could be to
Go out identification result.
The content of the invention
Goal of the invention：The object of the present invention is to provide it is a kind of can effectively save identification the time based on Fast Genetic Algorithm
With the coal pulverizer method for diagnosing faults of grey-box model.
Technical solution：To reach this purpose, the present invention uses following technical scheme：
Coal pulverizer method for diagnosing faults of the present invention based on Fast Genetic Algorithm and grey-box model, including offline mould
Type recognizes and online breakdown judge；
Off-line model identification based on genetic algorithm comprises the following steps：
S1：Based on the mass balance of coal inside coal pulverizer, the differential of raw coal quality and quality of pc inside coal pulverizer is established
Equation：
In formula (1), (2), mcFor the raw coal quality inside coal pulverizer, qm,cFor coal pulverizer inlet coal quality flow, mpfFor mill
Quality of pc inside coal machine, qm,pfPulverized coal mass flow is exported for coal pulverizer；
Coal pulverizer outlet pulverized coal mass flow qm,pfIt is calculated according to formula (3)：
qm,pf=K11Δpampf (3)
In formula (3), Δ paFor the pressure difference that primary air fan generates, calculated according to formula (4)；
In formula (4), tinFor air temperature of coal pulverizer inlet, qm,airFor the mass flow of coal pulverizer inlet First air；
S2：Calculate coal pulverizer internal moisture evaporation capacity
In formula (5), θCMFor raw coal moisture, and
S3：Calculate coal pulverizer internal energy balance equation：
In formula (6), toutAn air temperature is exported for coal pulverizer, I is coal pulverizer electric current, and I is calculated according to formula (7)：
I=K6mpf+K7mc+K8 (7)
S4：Calculate coal pulverizer outlet pressure equation:
More than K1~K18It is parameter to be identified；
S5：The data of following measuring point are gathered from the DCS system of power plant：Coal pulverizer inlet coal quality flow qm,c, coal pulverizer
The mass flow q of entrance First airm,air, coal pulverizer export an air temperature tout, coal pulverizer electric current I and coal pulverizer outlet pressure
P；
S6：The initial parameter of genetic algorithm is set, including generation gap, crossing-over rate, population dimension, aberration rate, maximum hereditary generation
Number and population at individual number；Then the upper and lower bound of Genetic algorithm searching matrix is set；The parameter of each individual is in population
One one-dimensional matrix, the one-dimensional matrix share 22 degree of freedom, and preceding 4 degree of freedom are the raw coal quality m inside coal pulverizerc, mill
Quality of pc m inside coal machinepf, raw coal moisture θCMAn air temperature t is exported with coal pulverizerout, rear 18 degree of freedom are to treat
Identified parameters K1~K18, the initial value of individual parameter is random in the range of the upper and lower bound of Genetic algorithm searching matrix in population
Value；
S7：The primary population of initial time genetic algorithm calculates the fitness function value of each individual of primary population：It will be from
The coal pulverizer inlet coal quality flow q of DCS acquisitionsm,c, coal pulverizer inlet First air mass flow qm,airIt is and individual in population
The initial value of parameter is input in formula (1)-formula (8), coal pulverizer electric current I and coal pulverizer outlet pressure P is obtained, then according to formula
(9) fitness function is calculated：
In formula (9), W1、W2And W3For the weights of artificial settings, N is the data sample number gathered from DCS,For tout's
Measured value,For the measured value of I,For the measured value of P,ForMaximum,ForMaximum,For's
Maximum；
S8：Selection, restructuring, mutation operator are acted on into group, obtain progeny population；
S9：Calculate the fitness function value of all individuals in progeny population；
S10：Weight insertion operation is performed to progeny population, judges whether genetic algebra reaches the maximum of setting：If not
Reach, then go to step S8；If reached, off-line identification process terminates.
On-line fault diagnosis comprise the following steps：
S11：By individual parameter in the population of the fitness function minimum obtained during off-line identification by genetic algorithm
It brings into formula (1)-formula (8)；
S12：Sampling time is set, a coal pulverizer fan-in evidence is gathered from Power Plant DCS System every the sampling time
With fan-out evidence；Coal pulverizer fan-in is according to including coal pulverizer inlet coal quality flow qm,c, air temperature of coal pulverizer inlet
tinWith the mass flow q of coal pulverizer inlet First airm,air, coal pulverizer fan-out is according to including the air temperature in coal pulverizer outlet
tout, coal pulverizer outlet pulverized coal mass flow qm,pf, coal pulverizer electric current I and coal pulverizer outlet pressure P；
S13：The coal pulverizer fan-in evidence collected is input to formula (1)-formula to be solved in (8)；
S14：The output valve of wushu (1)-formula (8) and the coal pulverizer fan-out evidence gathered from DCS are compared, and are obtained
The residual error e of the twok, then ekIt is compared with the fault threshold set, if ekMore than the threshold value of setting, then it is assumed that system
Failure has occurred.
Further, the step S9 is：The fitness function value of each individual in progeny population is calculated successively, if calculated
It obtains some individual fitness function value and reaches upper limit value Ω as shown in formula (10), then stop calculating the individual fitness
Functional value, and using the individual fitness function value as upper limit value Ω, then proceed to calculate remaining individual fitness function
Value；
Further, in the step S5, the sampling time of data acquisition is arranged to 1s, and each measuring point gathers 10000 groups of numbers
According to.
Advantageous effect：The present invention need not obtain the fault data of coal pulverizer with regard to that can carry out the judgement of fault type.Due to
The mechanism characteristic of coal pulverizer internal physical process is considered, there is higher precision than conventional method for diagnosing faults.Using fast
Fast genetic algorithm recognizes model, effectively reduces the time overhead of identification process.
Description of the drawings
Fig. 1 is the flow chart of the off-line model identification of the present invention；
Fig. 2 is the flow chart of the online breakdown judge of the present invention；
Fig. 3 is to carry out the model output value of electric current and pair of measured value that off-line model recognizes using the method for the present invention
Than figure；
Fig. 4 is to carry out the model output value of temperature and pair of measured value that off-line model recognizes using the method for the present invention
Than figure；
Fig. 5 is to carry out the model output value of pressure and pair of measured value that off-line model recognizes using the method for the present invention
Than figure；
Fig. 6 is to carry out the model output value of electric current and pair of measured value that online breakdown judge obtains using the method for the present invention
Than figure；
Fig. 7 is between the model output value of the electric current obtained using the online breakdown judge of the method for the present invention progress and measured value
Residual sum threshold value comparison diagram；
Fig. 8 is to carry out the model output value of temperature and pair of measured value that online breakdown judge obtains using the method for the present invention
Than figure；
Fig. 9 is between the model output value of the temperature obtained using the online breakdown judge of the method for the present invention progress and measured value
Residual sum threshold value comparison diagram；
Figure 10 is the model output value and measured value that the pressure that online breakdown judge obtains is carried out using the method for the present invention
Comparison diagram；
Figure 11 be using the method for the present invention carry out the pressure that online breakdown judge obtains model output value and measured value it
Between residual sum threshold value comparison diagram.
Specific embodiment
Technical scheme is further introduced With reference to embodiment.
The invention discloses a kind of coal pulverizer method for diagnosing faults based on Fast Genetic Algorithm and grey-box model, including from
Line model recognizes and online breakdown judge；
Off-line model identification comprises the following steps：
S1：Based on the mass balance of coal inside coal pulverizer, the differential of raw coal quality and quality of pc inside coal pulverizer is established
Equation：
In formula (1), (2), mcFor the raw coal quality inside coal pulverizer, qm,cFor coal pulverizer inlet coal quality flow, mpfFor mill
Quality of pc inside coal machine, qm,pfPulverized coal mass flow is exported for coal pulverizer；
Coal pulverizer outlet pulverized coal mass flow qm,pfIt is calculated according to formula (3)：
qm,pf=K11Δpampf (3)
In formula (3), Δ paFor the pressure difference that primary air fan generates, calculated according to formula (4)；
In formula (4), tinFor air temperature of coal pulverizer inlet, qm,airFor the mass flow of coal pulverizer inlet First air；
S2：Calculate coal pulverizer internal moisture evaporation capacity
In formula (5), θCMFor raw coal moisture, and
S3：Calculate coal pulverizer internal energy balance equation：
In formula (6), toutA temperature is exported for coal pulverizer, I is coal pulverizer electric current, and I is calculated according to formula (7)：
I=K6mpf+K7mc+K8 (7)
S4：Calculate coal pulverizer outlet pressure equation:
More than K1~K18It is parameter to be identified；
S5：The data of following measuring point are gathered from the DCS system of power plant：Coal pulverizer inlet coal quality flow qm,c, coal pulverizer
The mass flow q of entrance First airm,air, coal pulverizer export an air temperature tout, coal pulverizer electric current I and coal pulverizer outlet pressure
P；
S6：The initial parameter of genetic algorithm is set：
Generation gap is 0.8；
Crossing-over rate is 1；
Population dimension is 22, wherein initial state value of preceding 4 dimension for grey-box model, rear 15 dimension is genetic algorithm parameter；
Aberration rate is 1/22；
Maximum genetic algebra is 40；
Individual amount is 20；
Then the upper and lower bound of Genetic algorithm searching matrix is set：
The algorithm search matrix upper limit is：
[50,20,100,15,0.0035,0.25,0.015,0.0055,0.12,0.3,1,30,0.05,0.55,
0.008,-0.045,0.01,0.3,0.2,6,0.075,0.025]；
Algorithm search matrix lower limit is：
[10,5,30,5,0.002,0.05,0.006,0.0008,0.07,0.1,0.7,5,0.01,0.3,0.0004,-
0.09,0.006,0.05,0.05,4,0.055,0.01]；
The parameter of each individual is an one-dimensional matrix in population, which shares 22 degree of freedom, preceding 4 freedom
Degree is the raw coal quality m inside coal pulverizerc, quality of pc m inside coal pulverizerpf, raw coal moisture θCMWith coal pulverizer outlet one
Secondary air temperature tout, rear 18 degree of freedom are parameter K to be identified1~K18, the initial value of individual parameter is searched in genetic algorithm in population
Random value in the range of the upper and lower bound of rope matrix；
S7：The primary population of initial time genetic algorithm calculates the fitness function value of each individual of primary population：It will be from
The coal pulverizer inlet coal quality flow q of DCS acquisitionsm,c, coal pulverizer inlet First air mass flow qm,airIt is and individual in population
The initial value of parameter is input in formula (1)-formula (8), coal pulverizer electric current I and coal pulverizer outlet pressure P is obtained, then according to formula
(9) fitness function is calculated：
In formula (9), W1、W2And W3For the weights of artificial settings, N is the data sample number gathered from DCS,For toutReality
Measured value,For the measured value of I,For the measured value of P,ForMaximum,ForMaximum,ForMost
Big value；
S8：Selection, restructuring, mutation operator are acted on into group, obtain progeny population；
S9：Calculate the fitness function value of all individuals in progeny population；
S10：Weight insertion operation is performed to progeny population, judges whether genetic algebra reaches the maximum of setting：If not
Reach, then go to step S8；If reached, terminate.
The results are shown in Table 1 for Model Distinguish.
1 Model Distinguish result of table
| K1=0.0034 | K2=0.2345 | K3=0.0137 | K4=0.0049 | K5=0.0821 | K6=0.2232 |
| K7=0.7053 | K8=20.0000 | K9=0.0465 | K10=0.3570 | K11=0.0032 | K12=-0.0464 |
| K13=0.0073 | K14=0.1759 | K15=0.0758 | K16=4.6200 | K17=0.0701 | K18=0.0214 |
| mc=41.24 | mpf=13.68 | θCM=8.61 | tout=78.21 |
The verification result of off-line model identification is as shown in Fig. 3,4 and 5, it is seen then that carries out off-line model identification using the present invention
Precision it is very high, it is basic to coincide with Power Plant DCS measured data.
On-line fault diagnosis comprise the following steps：
S11：By individual parameter in the population of the fitness function minimum obtained during off-line identification by genetic algorithm
It brings into formula (1)-formula (8)；
S12：Sampling time is set, a coal pulverizer fan-in evidence is gathered from Power Plant DCS System every the sampling time
With fan-out evidence；Coal pulverizer fan-in is according to including coal pulverizer inlet coal quality flow qm,c, air temperature of coal pulverizer inlet
tinWith the mass flow q of coal pulverizer inlet First airm,air, coal pulverizer fan-out is according to including the air temperature in coal pulverizer outlet
tout, coal pulverizer outlet pulverized coal mass flow qm,pf, coal pulverizer electric current I and coal pulverizer outlet pressure P；
S13：The coal pulverizer fan-in evidence collected is input to formula (1)-formula to be solved in (8)；
S14：The output valve of wushu (1)-formula (8) and the coal pulverizer fan-out evidence gathered from DCS are compared, and are obtained
The residual error e of the twok, then ekIt is compared with the fault threshold set, if ekMore than the threshold value of setting, then it is assumed that system
Failure has occurred.
In coal pulverizer normal operation, the residual error of coal pulverizer outputting measurement value and model output value becomes in smaller scope
It is dynamic.When a failure occurs, which can exceed the threshold value of setting.Fault type may determine that according to the feature of residual error.It gives below
The most common three kinds of catastrophic discontinuityfailure diagnostic methods of coal pulverizer are gone out, have been respectively：Coal pulverizer inlet breaks coal failure, such as Fig. 6 and Fig. 7
It is shown；Coal pulverizer coal powder ignition failure, as shown in Figure 8 and Figure 9；Coal pulverizer blocks up powder failure, as shown in Figure 10 and Figure 11.
It can be seen from the figure that when an error occurs, residual error generates tendency variation, as long as setting certain residual error threshold
Value, it is possible to the generation of troubleshooting, and according to the type of type of residual error failure judgement.If occur there are many failure or unknown
Failure occurs, and can be judged according to residual error.Therefore the present invention can effectively diagnose the fault type of coal pulverizer.
Claims (2)
1. a kind of coal pulverizer method for diagnosing faults based on Fast Genetic Algorithm and grey-box model, it is characterised in that：Including offline
Model Distinguish and online breakdown judge；
Off-line model identification based on genetic algorithm comprises the following steps：
S1：Based on the mass balance of coal inside coal pulverizer, the differential equation of raw coal quality and quality of pc inside coal pulverizer is established：
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In formula (1), (2), mcFor the raw coal quality inside coal pulverizer, qm,cFor coal pulverizer inlet coal quality flow, mpfFor coal pulverizer
Internal quality of pc, qm,pfPulverized coal mass flow is exported for coal pulverizer；
Coal pulverizer outlet pulverized coal mass flow qm,pfIt is calculated according to formula (3)：
qm,pf=K11Δpampf (3)
In formula (3), Δ paFor the pressure difference that primary air fan generates, calculated according to formula (4)；
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In formula (4), tinFor air temperature of coal pulverizer inlet, qm,airFor the mass flow of coal pulverizer inlet First air；
S2：Calculate coal pulverizer internal moisture evaporation capacity
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In formula (5), toutAn air temperature, θ are exported for coal pulverizerCMFor raw coal moisture, and
S3：Calculate coal pulverizer internal energy balance equation：
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<mi>m</mi>
<mo>,</mo>
<mi>c</mi>
</mrow>
</msub>
<mo>-</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>K</mi>
<mn>4</mn>
</msub>
<msub>
<mi>t</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>K</mi>
<mn>5</mn>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<mo>(</mo>
<msub>
<mi>q</mi>
<mrow>
<mi>m</mi>
<mo>,</mo>
<mi>a</mi>
<mi>i</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>q</mi>
<mrow>
<mi>m</mi>
<mo>,</mo>
<mi>c</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>K</mi>
<mn>9</mn>
</msub>
<mi>I</mi>
<mo>-</mo>
<msub>
<mi>K</mi>
<mn>14</mn>
</msub>
<msubsup>
<mi>W</mi>
<mrow>
<mi>f</mi>
<mi>r</mi>
<mi>e</mi>
<mi>e</mi>
</mrow>
<mrow>
<mi>w</mi>
<mi>a</mi>
<mi>t</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msubsup>
<mo>+</mo>
<msub>
<mi>K</mi>
<mn>12</mn>
</msub>
<msub>
<mi>t</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (6), toutAn air temperature is exported for coal pulverizer, I is coal pulverizer electric current, and I is calculated according to formula (7)：
I=K6mpf+K7mc+K8 (7)
S4：Calculate coal pulverizer outlet pressure equation:
<mrow>
<mi>P</mi>
<mo>=</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>K</mi>
<mn>16</mn>
</msub>
<mo>+</mo>
<msub>
<mi>K</mi>
<mn>17</mn>
</msub>
<msub>
<mi>m</mi>
<mi>c</mi>
</msub>
<mo>)</mo>
</mrow>
<msubsup>
<mi>q</mi>
<mrow>
<mi>m</mi>
<mo>,</mo>
<mi>a</mi>
<mi>i</mi>
<mi>r</mi>
</mrow>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<msub>
<mi>K</mi>
<mn>18</mn>
</msub>
<msub>
<mi>m</mi>
<mrow>
<mi>p</mi>
<mi>f</mi>
</mrow>
</msub>
<msub>
<mi>q</mi>
<mrow>
<mi>m</mi>
<mo>,</mo>
<mi>a</mi>
<mi>i</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
More than K1~K18It is parameter to be identified；
S5：The data of following measuring point are gathered from the DCS system of power plant：Coal pulverizer inlet coal quality flow qm,c, coal pulverizer inlet
The mass flow q of First airm,air, coal pulverizer export an air temperature tout, coal pulverizer electric current I and coal pulverizer outlet pressure P；
S6：The initial parameter of genetic algorithm is set, including generation gap, crossing-over rate, population dimension, aberration rate, maximum genetic algebra and
Population at individual number；Then the upper and lower bound of Genetic algorithm searching matrix is set；The parameter of each individual is one in population
One-dimensional matrix, the one-dimensional matrix share 22 degree of freedom, and preceding 4 degree of freedom are the raw coal quality m inside coal pulverizerc, coal pulverizer
Internal quality of pc mpf, raw coal moisture θCMAn air temperature t is exported with coal pulverizerout, rear 18 degree of freedom are to be identified
Parameter K1~K18, the initial value of individual parameter takes at random in the range of the upper and lower bound of Genetic algorithm searching matrix in population
Value；
S7：The primary population of initial time genetic algorithm calculates the fitness function value of each individual of primary population：It will be from DCS
The coal pulverizer inlet coal quality flow q of acquisitionm,c, coal pulverizer inlet First air mass flow qm,airAnd individual ginseng in population
Several initial values is input in formula (1)-formula (8), obtains coal pulverizer electric current I and coal pulverizer outlet pressure P, then according to formula (9)
Calculate fitness function：
<mrow>
<mi>F</mi>
<mi>i</mi>
<mi>t</mi>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mi>&Sigma;</mi>
<mn>1</mn>
<mi>N</mi>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>W</mi>
<mn>1</mn>
</msub>
<mo>|</mo>
<mrow>
<mfrac>
<msub>
<mi>t</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msub>
<mrow>
<msup>
<msub>
<mover>
<mi>t</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msub>
<mi>max</mi>
</msup>
</mrow>
</mfrac>
<mo>-</mo>
<mfrac>
<msub>
<mover>
<mi>t</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msub>
<mrow>
<msup>
<msub>
<mover>
<mi>t</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msub>
<mi>max</mi>
</msup>
</mrow>
</mfrac>
</mrow>
<mo>|</mo>
<mo>+</mo>
<msub>
<mi>W</mi>
<mn>2</mn>
</msub>
<mo>|</mo>
<mrow>
<mfrac>
<mi>I</mi>
<msup>
<mover>
<mi>I</mi>
<mo>^</mo>
</mover>
<mi>max</mi>
</msup>
</mfrac>
<mo>-</mo>
<mfrac>
<mover>
<mi>I</mi>
<mo>^</mo>
</mover>
<msup>
<mover>
<mi>I</mi>
<mo>^</mo>
</mover>
<mi>max</mi>
</msup>
</mfrac>
</mrow>
<mo>|</mo>
<mo>+</mo>
<msub>
<mi>W</mi>
<mn>3</mn>
</msub>
<mo>|</mo>
<mrow>
<mfrac>
<mi>P</mi>
<msup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mi>max</mi>
</msup>
</mfrac>
<mo>-</mo>
<mfrac>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<msup>
<mover>
<mi>P</mi>
<mo>^</mo>
</mover>
<mi>max</mi>
</msup>
</mfrac>
</mrow>
<mo>|</mo>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (9), W1、W2And W3For the weights of artificial settings, N is the data sample number gathered from DCS,For toutActual measurement
Value,For the measured value of I,For the measured value of P,ForMaximum,ForMaximum,ForMaximum
Value；
S8：Selection, restructuring, mutation operator are acted on into group, obtain progeny population；
S9：Calculate the fitness function value of all individuals in progeny population；
S10：Weight insertion operation is performed to progeny population, judges whether genetic algebra reaches the maximum of setting：If not up to,
Then go to step S8；If reached, off-line identification process terminates；
Online breakdown judge comprises the following steps：
S11：Individual parameter in the population of the fitness function minimum obtained during off-line identification by genetic algorithm is brought into
In formula (1)-formula (8)；
S12：Sampling time is set, a coal pulverizer fan-in evidence and defeated is gathered from Power Plant DCS System every the sampling time
Go out end data；Coal pulverizer fan-in is according to including coal pulverizer inlet coal quality flow qm,c, air temperature t of coal pulverizer inletinWith
The mass flow q of coal pulverizer inlet First airm,air, coal pulverizer fan-out is according to including the air temperature t in coal pulverizer outletout, mill
Coal machine outlet pulverized coal mass flow qm,pf, coal pulverizer electric current I and coal pulverizer outlet pressure P；
S13：The coal pulverizer fan-in evidence collected is input to formula (1)-formula to be solved in (8)；
S14：Both the output valve of wushu (1)-formula (8) and the coal pulverizer fan-out evidence gathered from DCS are compared, be obtained
Residual error ek, then ekIt is compared with the fault threshold set, if ekMore than the threshold value of setting, then it is assumed that system occurs
Failure.
2. the coal pulverizer method for diagnosing faults according to claim 1 based on Fast Genetic Algorithm and grey-box model, special
Sign is：In the step S5, the sampling time of data acquisition is arranged to 1s, and each measuring point gathers 10000 groups of data.
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| CN111780149B (en) * | 2020-06-01 | 2022-12-27 | 大唐东北电力试验研究院有限公司 | Remote diagnosis method for equipment state of pulverizing system of thermal power plant |
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