CN113076623B - Dynamic estimation method and system for coal-fired calorific value of thermal power generating unit - Google Patents
Dynamic estimation method and system for coal-fired calorific value of thermal power generating unit Download PDFInfo
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Abstract
The invention discloses a dynamic estimation method and a system for the calorific value of coal fired by a thermal power unit, wherein the dynamic estimation method for the calorific value of the coal fired by the thermal power unit comprises the following steps: establishing a static estimation model according to the functional relationship among the air amount entering the furnace, the smoke gas amount at the outlet of the hearth and the oxygen content of the smoke gas in the hearth and the heat productivity; constructing and obtaining a system mechanism model based on a dynamic transmission process of a pulverizing system, a blower and an induced draft fan of a thermal power generating unit; the system mechanism model is used for obtaining dynamic estimation values of the amount of air entering the furnace, the amount of flue gas at the outlet of the hearth and the oxygen content of the flue gas in the hearth; selecting an input variable, a state variable and a quantity measurement based on a static estimation model and a system mechanism model, establishing a filtering system discretization state space model, and obtaining a dynamic estimation model of the coal-fired calorific value; and finishing the estimation of the calorific value of the coal of the thermal power generating unit according to the dynamic estimation model. The method can realize real-time on-line estimation of the calorific value of the fire coal, and both the tracking time and the estimation precision can meet the requirements of a control system.
Description
Technical Field
The invention belongs to the technical field of industrial automation, relates to the field of thermal power unit coal-fired calorific value estimation, and particularly relates to a dynamic estimation method and system for thermal power unit coal-fired calorific value.
Background
Because coal quality coal types of the coal-fired unit have the variable problem (even the same type of coal, the coal types are different along with the difference of production areas, mining points, mining years and depths), the low-order calorific value of the coal-fired unit has larger range change; the combustion efficiency of the boiler and the stability and the economical efficiency of the unit operation can be influenced by the change of the lower calorific value of the coal. Therefore, the method has important practical significance and research value for on-line real-time dynamic estimation of the calorific value of the fuel coal.
At present, most methods only provide a static estimation method of the calorific value of the fire coal based on effective heat absorption capacity or unit load, and have two problems:
(1) The heat absorption process changes slowly, the steam-water system has large inertia and long lag time, and is easy to be disturbed by various disturbance factors such as heat exchange coefficients and the like, so that high-precision estimation cannot be obtained, and the requirement of a unit control system on real-time performance cannot be met;
(2) The static estimation method is only suitable for the working condition of the unit in a steady state and cannot reflect the change of the calorific value of the fire coal in the dynamic adjustment process of the unit.
Disclosure of Invention
The invention aims to provide a dynamic estimation method and a dynamic estimation system for the coal-fired calorific value of a thermal power generating unit, so as to solve the existing technical problems. The method can realize real-time on-line estimation of the coal-fired heating value, and the tracking time and the estimation precision can meet the requirements of a control system.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention discloses a dynamic estimation method for the coal-fired calorific power of a thermal power generating unit, which comprises the following steps of:
step 1, establishing a static estimation model according to the functional relationship among the amount of air entering a furnace, the amount of flue gas at the outlet of a hearth and the oxygen content of the flue gas in the hearth and the heat productivity;
step 2, constructing and obtaining a system mechanism model based on a dynamic transmission process of a pulverizing system, a blower and an induced draft fan of the thermal power generating unit; the system mechanism model is used for obtaining dynamic estimation values of the amount of air entering the furnace, the amount of flue gas at the outlet of the hearth and the oxygen content of the flue gas in the hearth;
step 3, selecting input variables, state variables and measurement quantities based on a static estimation model and a system mechanism model, establishing a filtering system discretization state space model, and obtaining a dynamic estimation model of the coal-fired calorific value; and finishing the estimation of the coal-fired calorific value of the thermal power generating unit according to the dynamic estimation model.
The invention is further improved in that, in step 1, the expression of the static estimation model of the calorific value is,
in the formula, Q Mar Representing the heat contained in the mass fraction of the water; k is vq Theoretical air-to-heat ratio;
in the formula, O cp Is the oxygen content of the flue gas; w is a group of pf The amount of coal powder entering the furnace; v ba Is the volume flow of air entering the furnace; v gso The volume flow of the flue gas at the outlet of the hearth; v 0 Theoretical air consumption.
In step 2, the system mechanism model comprises:
the dynamic model of the pulverizing system, expressed as,
W pf ＝K 2 ·ΔP·M pf ，
in the formula, M c The raw coal stock is obtained; m pf The storage amount of the coal dust; delta P is the inlet-outlet pressure difference; tau. 0 Is a pure lag time; w is a group of c Is the coal feeding amount; w is a group of pf The amount of coal powder entering the furnace; k 1 、K 2 Is a proportionality coefficient; t is time;
the dynamic transmission model of the secondary fan and the induced draft fan is expressed as,
in the formula, W sa And W gsy Respectively the flow of a secondary fan and the flow of an induced draft fan; w ba And W gso Respectively the flow of secondary air entering the furnace and the flow of flue gas at the outlet of the hearth; t is sf And T yf Is a dynamic transmission time constant;
the flue gas density and hearth negative pressure model, expressed as,
W gsin ＝W ba +W pf ，
in the formula, V b Is the volume of the hearth; c b Is the flow volume coefficient; rho gs Is the density of the flue gas; w gsin The total mass of the air-powder mixture entering the hearth; p f The pressure of the hearth;
the model of the oxygen content of the furnace flue gas, expressed as,
V 0 ＝F(Q net,ar ),
in the formula, O cp The average oxygen content of the flue gas in the hearth; o is cpin Is the oxygen content of the flue gas at the inlet of the hearth; v gsin And V gso The volume flow of the flue gas at the inlet and the outlet of the hearth respectively; v ba Is the furnace inlet air volume flow; ρ is a unit of a gradient a Is the air density; v 0 Theoretical air consumption; q net,ar The low heating value of the coal is adopted; f (-) is an approximate function relation between the coal heating value and the theoretical air quantity.
A further development of the invention is that, in step 3,
the input quantity, the state quantity and the measurement quantity are respectively selected as follows:
the euler method is utilized to obtain a discretization system model of the discretization state space model of the filtering system, and the discretization system model is expressed as follows:
in the formula: a is a 11 ＝(1-K 1 T s )，a 21 ＝K 1 T s ，a 22 ＝(1-K 2 ·ΔP·T s )，a 52 ＝T s (K 2 ·ΔP·T s )/V b ，a 62 ＝T s (K 2 ·ΔP·T s )/C b ，a 72 ＝T s (K 2 ·ΔP·T s )O cpin (k)/(V b ·ρ gs (k))，a 33 ＝(1-T s /T sf )，a 53 ＝T s /V b ，a 63 ＝T s /C b ，a 73 ＝T s ·O cpin (k)/(V b ·ρ gs (k))，a 44 ＝(1-T s /T yf )，a 55 ＝(1-T s ·V gso (k)/V b )，a 65 ＝-T s ·V gso (k)/C b ，a 66 ＝1，a 77 ＝(1-T s ·V gso (k)/V b )，b 11 ＝T s ·δ(k-T 0 )，
V 0 (k)＝F(Q net，ar (k) In which T is s Sampling time for a discrete system;
the measurement equation of the discretization state space model of the filtering system is as follows:
the invention is further improved in that in the step 3, the expression of the dynamic estimation model of the coal-fired calorific value is as follows,
in the formula, K vq Is determined by the theoretical air heat ratio.
In a further development of the invention, in step 3, K vq The value is 0.2616; q Mar The value is 0.1418.
The invention is further improved in that, in step 3, the discretization state space model of the filtering system is solved by adopting a cubature Kalman filtering algorithm.
The invention discloses a dynamic estimation system for the coal-fired calorific power of a thermal power generating unit, which comprises:
the static estimation model acquisition module is used for establishing a static estimation model according to the functional relationship among the amount of air entering the furnace, the amount of flue gas at the outlet of the hearth and the oxygen content of the flue gas in the hearth and the heat productivity;
the system mechanism model acquisition module is used for constructing and acquiring a system mechanism model according to the dynamic transmission process of a pulverizing system, a blower and an induced draft fan of the thermal power generating unit; the system mechanism model is used for obtaining dynamic estimation values of the amount of air entering the furnace, the amount of flue gas at the outlet of the hearth and the oxygen content of the flue gas in the hearth;
the dynamic estimation model acquisition module is used for selecting input variables, state variables and quantity measurement according to the static estimation model and the system mechanism model, establishing a filtering system discretization state space model and acquiring a dynamic estimation model of the coal-fired calorific value; and finishing the estimation of the calorific value of the coal of the thermal power generating unit according to the dynamic estimation model.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, through the real-time changes of the secondary air quantity entering the furnace, the outlet flue gas quantity and the flue gas oxygen content, fusion estimation is carried out based on the iterative prediction of a system dynamic model and an actual measurement value, the real-time dynamic change of the coal-fired heating value can be quickly reflected, and meanwhile, the filter has better filtering performance, and noise disturbance in a unit system is filtered, so that the filter has important significance for improving the control stability and rapidity of the unit.
In the prior art, a static estimation model of the calorific value of the fire coal is mainly established, and the static estimation model is only suitable for the condition that the input and the output are not changed because a power plant has the characteristics of large delay, large inertia, strong coupling and the like, namely the static estimation model can have better estimation accuracy only when a boiler operates under a steady working condition. Aiming at the problem, the method is based on a dynamic model of a boiler system, uses the volumetric Kalman filtering to carry out real-time dynamic estimation on the calorific value of the coal, can well track the real-time dynamic change process of the calorific value of the coal, greatly improves the estimation precision of the calorific value of the coal, improves the combustion efficiency of the boiler, effectively reduces the pollutant discharge, realizes the full and reasonable utilization of energy, and improves the economical efficiency of unit operation.
The method in the prior art approximately estimates the calorific value of the fire coal based on the effective heat absorption capacity or the ratio of the unit load to the fuel charging quantity, but the heat transfer has larger inertia and delay, the change is slow, the method is easily influenced by the disturbance of steam-water measurement such as heat exchange coefficients, and the like, and the method has larger limitation in real-time. Aiming at the problem, the invention establishes a calorific capacity estimation model based on the secondary air volume entering the furnace, the oxygen content of the flue gas and the flue gas volume at the outlet, can track the real-time change of the calorific capacity more quickly, has better real-time performance compared with the prior method, reduces the fluctuation range of the unit coordination control, and improves the rapidity of a control system and the load variation capacity of the unit.
The method is based on the cubature Kalman filtering method, can well inhibit system model noise and measurement noise, and improves the stability of unit control.
Drawings
The drawings in the following description are examples of the invention and it will be clear to a person skilled in the art that other drawings can be derived from them without inventive step.
Fig. 1 is a schematic flow chart of a thermal power generating unit coal-fired calorific value estimation method based on volumetric kalman filtering according to an embodiment of the present invention;
FIG. 2 is a graph showing a fitting function curve between theoretical air consumption and lower calorific value of coal in an example of the present invention;
FIG. 3 is a graph illustrating a comparison between an actual value and an estimated value of an amount of air introduced into a furnace according to an embodiment of the present invention;
FIG. 4 is a graph showing a comparison curve between an actual value and an estimated value of an outlet flue gas amount in an embodiment of the present invention;
FIG. 5 is a diagram illustrating a comparison curve between an actual value and an estimated value of a smoke density according to an embodiment of the present invention;
FIG. 6 is a graph illustrating the comparison between the actual value and the estimated value of the furnace pressure according to the embodiment of the present invention;
FIG. 7 is a diagram illustrating a comparison curve between an actual value and an estimated value of an oxygen content in a flue gas of a furnace according to an embodiment of the present invention;
FIG. 8 is a graph illustrating the comparison between the actual value of the lower calorific value of the coal and the real-time estimated value in the embodiment of the present invention.
Detailed Description
In order to make the purpose, technical effect and technical solution of the embodiments of the present invention clearer, the following clearly and completely describes the technical solution of the embodiments of the present invention with reference to the drawings in the embodiments of the present invention; it is to be understood that the described embodiments are only some of the embodiments of the present invention. Other embodiments, which can be derived by one of ordinary skill in the art from the disclosed embodiments without inventive faculty, are intended to be within the scope of the invention.
Referring to fig. 1, a method for real-time dynamic estimation of coal-fired calorific power based on cubature kalman filtering according to an embodiment of the present invention includes the following steps:
step 1, different from the conventional coal-fired calorific value estimation method based on the ratio of effective heat absorption capacity to fuel quantity, the invention establishes a static estimation model through the functional relationship among the air quantity entering the furnace, the flue gas quantity at the outlet of the hearth, the oxygen content of the flue gas and the calorific value, can track the calorific value change more quickly, reduces the fluctuation of unit coordinated control, and improves the load regulation performance of the unit.
Wherein, by measuring the content of coal components such as carbon, sulfur, hydrogen, oxygen and moisture, the lower calorific value of the coal-received base is calculated by using a Mendeleev formula as follows:
Q net，ar ＝0.339C ar +0.109(S ar -O ar )+1.032H ar -0.025M ar ， (1)
in the formula, Q net，ar Representing the low calorific value of the coal; c ar Representing the mass fraction of received base carbon; s. the ar Representing the mass fraction of sulfur received; o is ar Represents the mass fraction of received oxygen; h ar Represents the received hydrogen mass fraction; m ar Indicating the mass fraction of the base moisture received.
The theoretical amount of air required for complete combustion of 1kg of coal can be expressed as:
V 0 ＝0.0889(C ar +0.375S ar )+0.265(H ar -0.125O ar )， (2)
in the formula, V 0 Representing the theoretical air consumption.
Defining:
R ar ＝(C ar +0.375S ar )， (3)
B ar ＝(H ar -0.125O ar )， (4)
supposing that the coal receives the base moisture mass fraction M after the coal blending combustion in the actual power plant ar Approximately constant, generally the sulfur content of coal is not more than 2%, the oxygen content is not more than 10%, so the calorific value is approximately:
Q net，ar ＝0.339R ar +1.032B ar ， (5)
by taking the ratio of the formula (2) to the formula (5), the theoretical air-to-heat ratio can be obtained:
in the formula, K vq Is the theoretical air heat ratio.
The calculation model of the oxygen content of the flue gas is expressed as follows:
in the formula, O cp Is the oxygen content of the flue gas; 21 represents the percentage of oxygen in air; w pf The amount of coal powder entering the furnace; v ba Is charged into a furnace cavityVolumetric flow rate of gas; v gso The volume flow of the flue gas at the outlet of the hearth; v 0 Theoretical air consumption.
The functional relation between the coal-fired heat productivity and the air quantity, the flue gas quantity and the oxygen content can be obtained by substituting the formula (6) into the formula (7):
in the formula, Q Mar Q represents the amount of heat contained in the water mass fraction, and Q is a constant value of the water mass fraction of the mixed coal Mar Can be simplified to constant compensation.
And 2, establishing a system dynamic model, namely a dynamic transmission process of a pulverizing system, a blower and an induced draft fan, wherein the dynamic transmission process is different from the static estimation of the oxygen content of the flue gas in the past.
(2.1) the dynamic model of the pulverizing system was,
W pf ＝K 2 ·ΔP·M pf ， (11)
wherein M is c The raw coal stock is obtained; m pf The storage amount of the coal dust; delta P is the inlet-outlet pressure difference; tau is 0 Pure lag time for coal feeders, coal transports, etc.; w c The coal feeding amount is used; w pf The amount of coal powder entering the furnace; k 1 、K 2 Is a proportionality coefficient; t is time.
(2.2) the dynamic transmission model of the secondary fan and the induced draft fan is that,
wherein, W sa And W gsy Respectively the flow of a secondary fan and the flow of an induced draft fan; w ba And W gso Respectively the flow of secondary air entering the furnace and the flow of flue gas at the outlet of the hearth; t is sf And T yf Is a dynamic transmission time constant.
(2.3) flue gas density and negative pressure of furnace
Wherein, the first and the second end of the pipe are connected with each other,
W gsin ＝W ba +W pf ， (16)
in the formulae (14) to (16), V b Is the volume of the hearth; c b Is the flow volume coefficient; rho gs Is the density of the flue gas; w gsin The total mass of the air-powder mixture entering the hearth; p f Is the furnace pressure.
(2.4) oxygen content of hearth flue gas
Wherein the content of the first and second substances,
V 0 ＝F(Q net，ar )， (22)
in formulae (17) to (22), O cp The average oxygen content of the flue gas in the hearth; o is cpin The oxygen content of the flue gas at the inlet of the hearth; v gsin And V gso The volume flow of the flue gas at the inlet and the outlet of the hearth respectively; v ba Is the furnace inlet air volume flow; rho a Is the air density; v 0 Theoretical air consumption; q net，ar The low heating value of the coal is adopted; f (-) is an approximate function relation between the coal heating value and the theoretical air quantity.
Referring to fig. 2, in the embodiment of the present invention, different coal types are selected to observe the relationship between the calorific value and the theoretical air consumption, and it can be seen that the two have a better linear relationship, and the functional relationship can be expressed as follows:
V 0 ＝0.2578×Q net，ar +0.1146， (23)
the root mean square error of the formula is 0.0356, and the precision can basically meet the requirement.
And 3, selecting input variables, state variables and measurement quantities according to the system mechanism model, and establishing a filtering system discretization state space model and a dynamic estimation model of coal-fired calorific value.
The input quantity, the state quantity and the measured quantity are respectively selected as follows:
according to the model, a discretization system model is obtained by using an Euler method:
in the above formula: a is 11 ＝(1-K 1 T s )，a 21 ＝K 1 T s ，a 22 ＝(1-K 2 ·ΔP·T s )，a 52 ＝T s (K 2 ·ΔP·T s )/V b ，a 62 ＝T s (K 2 ·ΔP·T s )/C b ，a 72 ＝T s (K 2 ·ΔP·T s )O cpin (k)/(V b ·ρ gs (k))，a 33 ＝(1-T s /T sf )，a 53 ＝T s /V b ，a 63 ＝T s /C b ，a 73 ＝T s ·O cpin (k)/(V b ·ρ gs (k))，a 44 ＝(1-T s /T yf )，a 55 ＝(1-T s ·V gso (k)/V b )，a 65 ＝-T s ·V gso (k)/C b ，a 66 ＝1，a 77 ＝(1-T s ·V gso (k)/V b )，b 11 ＝T s ·δ(k-T 0 )，
V 0 (k)＝F(Q net，ar (k) Wherein T) is s The time is sampled for a discrete system. The measurement equation is as follows:
based on the model, a coal-fired calorific value estimation model can be obtained:
in the formula K vq For the theoretical air-heat ratio, the components of different coal types need to be sampled and calibrated by an actual power plant; in the embodiment of the invention, 15 coal types with different component contents are selected, and K is found by calculation vq The variation ranges are [0.2614,0.2618]It can be approximated as a constant, K in the example vq Taking the median value of 0.2616; q Mar The variation range is [0.0316,0.3106 ]]Example arithmetic mean 0.1418.
In the embodiment of the invention, a Cubature Kalman Filtering (CKF) algorithm is established to realize the real-time estimation of the calorific value of the fire coal.
Since the system model described above contains non-linear equations, there is a coupling between the various state quantities.
The general form of the filter model is written as:
x k+1 ＝f(x k ，u k )+ω k (30)
z k+1 ＝h(x k+1 ，u k )+v k+1 (31)
in the formula x k+1 Is a system state quantity, z k+1 Quantitative measurement, ω k Is process noise, v k+1 For measuring noise, they are independent of each other and are omega k ～N(0，Q k )，v k+1 ～N(0，R k+1 ). Since the specific values are related to various equipment parameters of the specific coal-fired power plant, identification and calibration need to be performed in combination with actual operating equipment, and the values of the model parameters are only given as examples, please refer to table 1.
TABLE 1 model parameter value selection
In the embodiment of the invention, the CKF algorithm is specifically realized as follows:
(1) Algorithm initialization
Initializing filter parameters including state quantity, input quantity and initial value x of covariance of state quantity 0 、u 0 And P 0 Process noise and measurement noise covariance Q k And R k+1 . Similarly, the specific values are related to the specific coal-fired power plant equipment parameters and the work load, and the state quantity values, the process noise and the measurement noise of different work loads or different operating equipment need to be identified and calibrated by combining actual operating data, and the specific values are only given as examples:
(2) Time updating
1) Calculating square root of variance S k And volume point X j,k
S k ＝chol{P k } (37)
In which chol represents the Cholesky decomposition of the matrix, n represents the dimension of the state quantity, P k In order to filter the covariance,
for filtering value, xi, at time k j Is the fundamental volume point, i.e.:
in the formula (I)]Representing a set of points resulting from a full permutation of the unit vectors and a change of the element sign, called the complete fully symmetric set of points, [ I] j Representing the jth point in the set of points.
2) Calculating volume points propagated through a nonlinear equation of state model
Wherein f (-) is a nonlinear system state equation model.
3) Calculating one-step predicted value of state quantity according to propagation volume point
And one-step prediction covariance P k+1|k
In the formula of omega j Representing the weight corresponding to the volume point, namely:
(3) Measurement update
1) Calculating the square root of the predicted variance S according to the one-step predicted value and the one-step predicted covariance k+1|k And predicted value volume point X j ， k+1
S k+1 | k ＝chol{P k+1|k } (43)
2) Calculating the volume point Z propagated through the model of the nonlinear metrology equation j，k+1
Z j，k+1 ＝h(X j，k+1 ) (45)
3) Calculating the measurement prediction value
Covariance of measurement error P zz，k+1 Cross covariance P xz，k+1
4) Computing kalman gain, state quantity estimate and covariance
Based on the state estimate
Namely, the dynamic real-time estimation value Q of the coal-fired heat productivity can be obtained according to the formula (29) net，ar (k+1)。
Referring to fig. 3 to 8, in the embodiment of the present invention, a slow change experiment of the coal-fired calorific value is simulated, and a change process of each state quantity filter value and the coal-fired calorific value estimation value is observed.
Fig. 3 to 7 represent the variation curves of the amount of inlet air, the amount of outlet flue gas, the flue gas density, the furnace pressure and the oxygen content of the flue gas, respectively, and it can be seen that the state quantity filtering value can well track the actual value and effectively suppress noise, so that the state quantity changes more smoothly, and the estimation performance indexes of each state quantity are shown in table 2.
Fig. 8 represents a variation curve of coal-fired calorific value estimation, and it can be seen that the calorific value estimation value can quickly track the actual value variation and keep high estimation accuracy, and each performance index is shown in table 3. The quick and accurate real-time estimation of the calorific value of the fire coal can greatly improve the variable load capacity of the unit coordination control system, realize the full utilization of energy, reduce the pollutant emission and improve the economical efficiency of the unit operation.
TABLE 2 State quantity Filter Performance indicators
TABLE 3 coal-fired calorific power estimation Performance index
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.
Claims (5)
1. A dynamic estimation method for the coal-fired calorific power of a thermal power generating unit is characterized by comprising the following steps:
step 1, establishing a static estimation model according to the functional relationship among the amount of air entering a furnace, the amount of flue gas at the outlet of a hearth and the oxygen content of the flue gas in the hearth and the heat productivity;
step 2, constructing and obtaining a system mechanism model based on a dynamic transmission process of a pulverizing system, a blower and an induced draft fan of the thermal power generating unit; the system mechanism model is used for obtaining dynamic estimated values of the amount of air entering the furnace, the amount of flue gas at the outlet of the hearth and the oxygen content of the flue gas in the hearth;
step 3, selecting input variables, state variables and quantity measurement based on a static estimation model and a system mechanism model, establishing a filtering system discretization state space model, and obtaining a dynamic estimation model of the coal-fired calorific value; finishing the estimation of the calorific value of the fire coal of the thermal power generating unit according to the dynamic estimation model;
in step 2, the system mechanism model includes:
the dynamic model of the pulverizing system, expressed as,
W pf ＝K 2 ·ΔP·M pf ，
in the formula, M c The raw coal stock is obtained; m is a group of pf The storage amount of the coal dust; delta P is the inlet-outlet pressure difference; tau is 0 Is a pure lag time; w c Is the coal feeding amount; w pf The amount of coal powder entering the furnace; k is 1 、K 2 Is a proportionality coefficient; t is time;
the dynamic transmission model of the secondary fan and the induced draft fan is expressed as,
in the formula, W sa And W gsy Respectively the flow of a secondary fan and the flow of an induced draft fan; w ba And W gso Respectively the flow of secondary air entering the furnace and the flow of flue gas at the outlet of the hearth; t is sf And T yf Is a dynamic transmission time constant;
the flue gas density and hearth negative pressure model, expressed as,
W gsin ＝W ba +W pf ，
in the formula, V b Is the volume of the hearth; c b Is the flow volume coefficient; ρ is a unit of a gradient gs Is the density of the flue gas; w is a group of gsin The total mass of the air-powder mixture entering the hearth; p is f The pressure of the hearth;
the model of the oxygen content of the furnace flue gas, expressed as,
in the formula, O cp Is the oxygen content of the flue gas; o is cpin The oxygen content of the flue gas at the inlet of the hearth; v gsin And V gso The volume flow of the flue gas at the inlet and the outlet of the hearth respectively; v ba Is the volume flow of air entering the furnace; rho a Is the air density; v 0 Theoretical air consumption; q net,ar The coal is low-grade heating value; f (-) is an approximate functional relation between the coal-fired heating value and the theoretical air quantity;
in step 3, the input quantity, the state quantity and the measurement quantity are respectively selected as follows:
obtaining a discretization system model of the discretization state space model of the filtering system by using an Euler method, wherein the discretization system model is expressed as follows:
in the formula: a is 11 ＝(1-K 1 T s )，a 21 ＝K 1 T s ，a 22 ＝(1-K 2 ·ΔP·T s )，a 52 ＝T s (K 2 ·ΔP·T s )/V b ，a 62 ＝T s (K 2 ·ΔP·T s )/C b ，a 72 ＝T s (K 2 ·ΔP·T s )O cpin (k)/(V b ·ρ gs (k))，a 33 ＝(1-T s /T sf )，a 53 ＝T s /V b ，a 63 ＝T s /C b ，a 73 ＝T s ·O cpin (k)/(V b ·ρ gs (k))，a 44 ＝(1-T s /T yf )，a 55 ＝(1-T s ·V gso (k)/V b )，a 65 ＝-T s ·V gso (k)/C b ，a 66 ＝1，a 77 ＝(1-T s ·V gso (k)/V b )，b 11 ＝T s ·δ(k-T 0 )
V 0 (k)＝F(Q net，ar (k) Wherein, T s Sampling time for a discrete system;
the measurement equation of the discretization state space model of the filtering system is as follows:
in step 3, the expression of the dynamic estimation model of the coal-fired calorific value is as follows,
in the formula, K vq Determining the theoretical air heat ratio; q Mar Representing the amount of heat contained in the moisture mass fraction.
2. The dynamic estimation method according to claim 1, wherein in step 1, the expression of the static estimation model of the calorific value is,
in the formula, Q Mar Representing the heat contained in the mass fraction of the water; k is vq Theoretical air-to-heat ratio;
in the formula, O cp Is the oxygen content of the flue gas; w is a group of pf The amount of coal powder entering the furnace; v ba Is the volume flow of air entering the furnace; v gso The volume flow of the flue gas at the outlet of the hearth; v 0 Theoretical air consumption.
3. The dynamic estimation method according to claim 1, wherein in step 3, K is vq The value is 0.2616; q Mar The value is 0.1418.
4. The dynamic estimation method according to claim 1, wherein in step 3, the discretized state space model of the filtering system is solved by using a cubature kalman filtering algorithm.
5. A dynamic estimation system for the coal-fired calorific power of a thermal power generating unit is characterized by comprising:
the static estimation model acquisition module is used for establishing a static estimation model according to the functional relationship among the amount of air entering the furnace, the amount of flue gas at the outlet of the hearth and the oxygen content of the flue gas in the hearth and the heat productivity;
the system mechanism model acquisition module is used for constructing and acquiring a system mechanism model according to the dynamic transmission process of a pulverizing system, a blower and an induced draft fan of the thermal power generating unit; the system mechanism model is used for obtaining dynamic estimated values of the amount of air entering the furnace, the amount of flue gas at the outlet of the hearth and the oxygen content of the flue gas in the hearth;
the dynamic estimation model acquisition module is used for selecting input variables, state variables and quantity measurement according to the static estimation model and the system mechanism model, establishing a filtering system discretization state space model and acquiring a dynamic estimation model of the coal-fired calorific value; finishing the estimation of the calorific value of the fire coal of the thermal power generating unit according to the dynamic estimation model;
the system mechanism model comprises:
the dynamic model of the pulverizing system, expressed as,
W pf ＝K 2 ·ΔP·M pf ，
in the formula, M c The raw coal stock is obtained; m pf The storage amount of the coal dust; delta P is the inlet-outlet pressure difference; tau is 0 Is a pure lag time; w c Is the coal feeding amount; w pf The amount of coal powder entering the furnace; k is 1 、K 2 Is a proportionality coefficient; t is time;
the dynamic transmission model of the secondary fan and the induced draft fan is expressed as,
in the formula, W sa And W gsy Respectively the flow of a secondary fan and the flow of an induced draft fan; w is a group of ba And W gs Respectively the flow of secondary air entering the furnace and the flow of flue gas at the outlet of the hearth; t is sf And T yf Is a dynamic transmission time constant;
the flue gas density and hearth negative pressure model, expressed as,
W gsin ＝W ba +W pf ，
in the formula, V b Is the volume of the hearth; c b Is the flow volume coefficient; ρ is a unit of a gradient gs Is the density of the flue gas; w is a group of gsin The total mass of the air-powder mixture entering the hearth; p f The furnace pressure is used as the furnace pressure;
the model of the oxygen content of the furnace flue gas, expressed as,
V 0 ＝F(Q net，ar )，
in the formula, O cp Is flue gasOxygen content; o is cpin The oxygen content of the flue gas at the inlet of the hearth; v gsin And V gso The volume flow of the flue gas at the inlet and the outlet of the hearth respectively; v ba Is the volume flow of air entering the furnace; rho a Is the air density; v 0 Theoretical air consumption; q net，ar The coal is low-grade heating value; f (-) is an approximate functional relation between the coal heating value and the theoretical air quantity;
the input quantity, the state quantity and the measured quantity are respectively selected as follows:
obtaining a discretization system model of the discretization state space model of the filtering system by using an Euler method, wherein the discretization system model is expressed as follows:
in the formula: a is 11 ＝(1-K 1 T s )，a 21 ＝K 1 T s ，a 22 ＝(1-K 2 ·ΔP·T s )，a 52 ＝T s (K 2 ·ΔP·T s )/V b ，a 62 ＝T s (K 2 ·ΔP·T s )/C b ，a 72 ＝T s (K 2 ·ΔP·T s )O cpin (k)/(V b ·ρ gs (k))，a 33 ＝(1-T s /T sf )，a 53 ＝T s /V b ，a 63 ＝T s /C b ，a 73 ＝T s ·O cpin (k)/(V b ·ρ gs (k))，a 44 ＝(1-T s /T yf )，a 55 ＝(1-T s ·V gso (k)/V b )，a 65 ＝-T s ·V gso (k)/C b ，a 66 ＝1，a 77 ＝(1-T s ·V gso (k)/V b )，b 11 ＝T s ·δ(k-T 0 )
V 0 (k)＝F(Q net，ar (k) In which T is s Sampling time for a discrete system;
the measurement equation of the discretization state space model of the filtering system is as follows:
the expression of the dynamic estimation model of the coal-fired heating value is as follows,
in the formula, K vq Determining the theoretical air heat ratio; q Mar Representing the amount of heat contained in the moisture mass fraction.
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