CN108803342B - Unit unit load quick response prediction control method - Google Patents

Unit unit load quick response prediction control method Download PDF

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CN108803342B
CN108803342B CN201810727691.3A CN201810727691A CN108803342B CN 108803342 B CN108803342 B CN 108803342B CN 201810727691 A CN201810727691 A CN 201810727691A CN 108803342 B CN108803342 B CN 108803342B
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雎刚
邵恩泽
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Southeast University
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Abstract

The invention discloses a unit load quick response prediction control method, which adopts prediction deviation of unit power and main steam pressure and corresponding prediction deviation change rate to construct a performance index, and designs a prediction control law based on the performance index. In order to enable the control system to fully utilize the boiler heat accumulation, a main steam pressure weight coefficient is set in the performance index and is designed as a function of power, when the power deviation becomes large, the weight coefficient becomes small, the main steam pressure is allowed to fluctuate greatly, the set value is quickly tracked by utilizing the boiler heat accumulation, and the response speed of the set load is effectively improved. In addition, weight coefficients of the predicted deviation change rate of the power and the main steam pressure are respectively set in the performance indexes, the control performance of the prediction control system can be conveniently and effectively adjusted through the two weight coefficients, and engineering application is facilitated.

Description

Unit unit load quick response prediction control method
Technical Field
The invention belongs to the technical field of automatic control, and particularly relates to a quick load response prediction control method for a unit set.
Background
Along with the development of wind power and solar power generation, a power grid puts higher requirements on peak regulation of a thermal power generating unit, and a load control system of the thermal power generating unit is required to have quick load response capability. For large inertia and large hysteresis processes, predictive control is an excellent control method. Because the dynamic characteristic of the steam turbine is fast and the dynamic characteristic of the boiler is slow, the power response speed of the unit can be effectively improved only by fully utilizing the heat storage of the boiler in the control process. However, the conventional predictive control method does not consider the dynamic coordination of the boiler side and the steam turbine side in the control process, and cannot utilize the heat storage of the boiler by changing the main steam pressure in a short time, so that the response speed of the unit load is limited. In addition, the control performance of the traditional prediction control method is not sensitive to the parameters of the prediction controller, and is not beneficial to engineering application.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a unit load rapid response predictive control method which can effectively improve the response speed of the unit load and can conveniently and effectively adjust the predictive control performance.
The technical scheme is as follows: the invention relates to a quick load response prediction control method for a unit set, which specifically comprises the following steps:
(1) acquiring a discrete controlled autoregressive integrated moving average (CARIMA) model of a unit load controlled process;
(2) designing a predictive control performance index;
(3) determining a unit load prediction control law;
(4) and the quick response prediction control of the unit load is realized.
The step (1) comprises the following steps:
(11) the transfer function model of the following unit load controlled process is obtained through model identification:
Figure BDA0001720084750000011
wherein, y1Is the unit power, y2Main steam pressure, u1Is the combustion rate of the boiler, u2For regulating the opening of the valve of the steam turbine G11(s) and G21(s) are each independently u1Is input, y1And y2As a process transfer function of the output, G12(s) and G22(s) are each independently u2Is input, y1And y2S is a variable on the complex plane, which is the process transfer function of the output;
(12) discretizing the formula (1) by a sampling period T to obtain a discrete controlled autoregressive integrated moving average (CARIMA) model of the following load controlled process:
Figure BDA0001720084750000021
wherein: a. thei(z-1)、Bij(z-1) (i ═ 1,2, j ═ 1,2) and Δ are for z, respectively-1The polynomial of (c):
Figure BDA0001720084750000022
Figure BDA0001720084750000023
Δ＝1-z-1，
z-1for the backward shift operator, k is the sampling instant, ε1(k) And ε2(k) White noise with an average value of 0; naiAnd nbijAre respectively polynomial Ai(z-1) And Bij(z-1) Order, ai,lAnd bij,lAre respectively Ai(z-1) And Bij(z-1) The polynomial coefficient of (1).
The novel performance indexes of the step (2) are as follows:
Figure BDA0001720084750000024
wherein N is1To a power y1Predicted time domain of, N2Is the main steam pressure y2Is predicted in the time domain, λ1、λ2Beta is a weight coefficient, alpha is a real number, e1(k + i) and ce1(k + i) are the predicted deviation of the unit load at the k + i th moment and the change rate of the predicted deviation, respectively, e2(k + i) and ce2(k + i) the predicted deviation of the main steam pressure and the rate of change of the predicted deviation at the k + i-th time, respectively, eNe(k-1) and ceNeAnd (k-1) is the deviation and the deviation change rate of the unit load at the moment of k-1 respectively.
5. The prediction control law in the step (3) is as follows:
ΔU＝[(G1)TG1+β(G2)TG2]-1[(G1)Th1+β(G2)Th2] (4)
where T is the matrix transposition operation and Δ U is the sum of U1And u2Vector of control increments at the current time k and its future times:
ΔU＝[Δu1(k) … Δu1(k+Nu1-1) … Δu2(k) … Δu2(k+Nu2-1]T
wherein Nu1And Nu2Are each u1And u2The control time domain of (2); in the formula (4), Gp(p ═ 1,2) is a matrix calculated by the following formula:
Figure BDA0001720084750000031
wherein, IpIs Np×NpOrder unit diagonal matrix, Q1、
Figure BDA0001720084750000032
Matrices of corresponding dimensions, respectively;
h in the formula (4)p(p ═ 1,2) is a matrix calculated by the following formula:
Figure BDA0001720084750000041
wherein Hp、Fp、
Figure BDA0001720084750000042
And Q2Respectively, of corresponding dimensions, Δ UbpIs composed of u1And u2Vector of control increments, Yb, at times preceding the current time kpIs composed of yp(p is 1,2) a vector of values at and before the current time k, RpIs composed of yp(p is 1,2) a vector of set values at each future time; matrix array
Figure BDA0001720084750000043
Sum matrix
Figure BDA0001720084750000044
By the element of Gpq,j(z-1) Coefficient composition of polynomials, matrices
Figure BDA0001720084750000045
Is composed of Fp,j(z-1) Coefficient composition of the polynomial:
Figure BDA0001720084750000046
Figure BDA0001720084750000047
wherein p is 1,2, q is 1,2, Ep,j(z-1) And Fp,j(z-1) To satisfy the charpy equation:
1＝Ep,j(z-1)ΔAp(z-1)+z-jFp,j(z-1) A polynomial of (c).
5. The method for controlling the unit load quick response prediction as claimed in claim 1, wherein the step (4) comprises the steps of:
(41) setting a prediction control parameter: setting a sampling period T and predicting a time domain N1And N2Selection is made only so that N1T and N2T is greater than the pure delay time of the corresponding process, the control time domain Nu1And Nu2Take 1 or 2, weight coefficient lambda1、λ2Taking 1-10, and taking 500-1000 of real number alpha;
(42) acquisition yp(p is 1,2) operation data y at each timep(k-i)，i＝0，1，…，napForm a vector Ybp＝[yp(k)yp(k-1)…yp(k-nap)]T(ii) a Collection u1Operating data u at each time1(k-i)，i＝1，2，…，max(nb11，nb21) Wherein max is the maximum operation, collect u2Operating data u at each time2(k-i)，i＝1，2，...，max(nb12，nb22) And separately calculate u1、u2The increment of each time instant constitutes a vector Δ Ubp＝[Δu1(k-1)…Δu1(k-nbp1)…Δu2(k-1)…Δu2(k-nbp2)]TP is 1,2, the set value r of the received power and the main steam pressure at each future momentp(k+i)，p＝1，2，i＝1，2，…，NpForming a vector Rp ═ rp(k+1) rp(k+2) … rp(k+Np)]T；
(43) Will (42) the medium vector Ybp、ΔUbpAnd RpCalculating a predicted control amount increment vector delta U by taking the formula (4), the formula (5) and the formula (6);
(44) to be in the vector Δ UΔu1(k) And Δ u2(k) For control, the current control amount of the predictive control is calculated as follows: u. of1(k)＝u1(k-1)+Δu1(k)，u2(k)＝u2(k-1)+Δu2(k) U to be calculated1(k)、u2(k) Acting on the controlled process;
(45) and (5) circularly executing (42) to (44) to realize quick response prediction control of unit load.
The value of the main steam pressure term weight coefficient beta in the performance index formula (3) can improve the response speed of the unit load; weight coefficient lambda1、λ2The value of (2) can adjust the predictive control performance.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: 1. the boiler and the steam turbine can be dynamically coordinated by predicting the main steam pressure weight coefficient in the control performance index, when the power deviation is increased, the weight coefficient is decreased, the main steam pressure is allowed to generate large fluctuation, the boiler heat storage is fully utilized, and the response speed of the unit load is effectively improved; 2. by changing the values of the weight coefficients of the predicted deviation change rates of the power and the main steam pressure, the prediction control performance can be conveniently and effectively adjusted, and engineering application is facilitated.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a multi-variable predictive control system for unit load;
FIG. 3 is a simulation curve of the unit power under the condition of fixed weight coefficient beta and self-adaptation;
FIG. 4 is a simulation curve of main steam pressure with fixed weight coefficient β and self-adaptation;
FIG. 5 is λ2When equal to 0, different λ1Taking a simulation curve of the lower unit power;
FIG. 6 is λ2When equal to 0, different λ1Taking a simulation curve of the main steam pressure;
FIG. 7 is λ1When equal to 0, different λ2Taking a simulation curve of the lower unit power;
FIG. 8 is λ1When equal to 0, different λ2Main steam pressure under valueThe simulation curve of (1).
Detailed Description
The present invention will be further described in detail with reference to the accompanying drawings, and fig. 1 is a flow chart of the present invention, which specifically includes the following steps:
step 1: obtaining a discrete controlled autoregressive integrated moving average (CARIMA) model of a unit load controlled process
Keeping the opening u of the steam turbine regulating valve2(t) constant, boiler firing Rate u1(t) making step change to obtain power y of the unit1(t) and the main steam pressure y2(t) step response data, and according to the step response data, respectively obtaining u by using a model identification method based on process step response1(t) is input, y1(t) and y2(t) Process transfer function G as output11(s) and G21(s); in the same way, the combustion rate u of the boiler is kept1(t) constant, opening u of steam turbine governor2(t) step change, respectively obtaining u by model identification2(t) is input, y1(t) and y2(t) Process transfer function G as output12(s) and G22(s), the transfer function model of the controlled process of the unit load is as follows:
Figure BDA0001720084750000061
acquiring a unit load controlled process transfer function model through model identification according to a field test, wherein a load controlled process model of a certain 600MW subcritical drum boiler unit at a 70% load point is selected as a subsequent implementation simulation model:
Figure BDA0001720084750000071
discretizing the model with a sampling period of T-11 seconds to obtain the following load controlled process discrete CARIMA model:
Figure BDA0001720084750000072
wherein:
Figure BDA0001720084750000073
Figure BDA0001720084750000074
Δ＝1-z-1
z-1for the backward shift operator, k is the sampling instant, ε1(k) And ε2(k) White noise with an average value of 0; naiAnd nbijAre respectively polynomial Ai(z-1) And Bij(z-1) Order, ai,lAnd bij,lAre respectively Ai(z-1) And Bij(z-1) A polynomial coefficient of (d);
the calculation here is:
A1(z-1)＝1-3.8777z-1+5.9244z-2-4.4499z-3+1.6408z-4-0.2375z-5
A2(z-1)＝1-2.7922z-1+2.5989z-2-0.8063z-3
B11(z-1)＝z-3(-0.01288+0.03364z-1-0.01318z-2-0.01453z-3+0.00751z-4)
B12(z-1)＝2.2544-7.6799z-1+9.6641z-2-5.3012z-3+1.0627z-4
B21(z-1)＝z-3(-0.001139+0.003134z-1-0.001928z-2)
B22(z-1)＝-0.009455+0.01762z-1-0.008212z-2
step 2: designing a predictive control performance index
The design principle of the predictive control performance index is that the boiler and the steam turbine can be dynamically coordinated, the heat storage of the boiler can be fully utilized, the control performance of the predictive control system can be conveniently and effectively adjusted, and the following novel performance indexes are specifically adopted:
Figure BDA0001720084750000081
in the formula N1For predicting the time domain of power, N2Is a predicted time domain of the main steam pressure, lambda1、λ2Beta is a weight coefficient, alpha is a real number, e1(k + i) and ce1(k + i) are the predicted deviation of the unit load at the k + i th moment and the change rate of the predicted deviation, respectively, e2(k + i) and ce2(k + i) are the predicted deviation of the main steam pressure at the k + i-th time and the change rate of the predicted deviation, respectively, and are calculated by the following formulas:
Figure BDA0001720084750000082
wherein r is1(k + i) and
Figure BDA0001720084750000083
respectively a set value and a predicted value r of the unit load at the moment of k + i2(k + i) and
Figure BDA0001720084750000084
respectively is a set value and a predicted value of the main steam pressure at the moment k + i.
And step 3: determining a unit load prediction control law, which comprises the following specific steps:
solving for yp(p 1,2) charpy equation 1Ep,j(z-1)ΔAp(z-1)+z-jFp,j(z-1) Obtaining a polynomial Ep,j(z-1) And Fp,j(z-1)：
Ep，j(z-1)＝ep，j，0+ep，j，1z-1+…+ep，j，j-1z-(j-1)
Figure BDA0001720084750000085
Then y isp(p ═ 1,2) predicted value at future time k + j
Figure BDA0001720084750000086
Comprises the following steps:
Figure BDA0001720084750000087
wherein:
Figure BDA0001720084750000088
will be provided with
Figure BDA0001720084750000089
Substituting into the calculation formula of the performance index J, minimizing the performance index J, and passing the necessary condition of extreme value
Figure BDA0001720084750000091
The following predictive control law was obtained:
Figure BDA0001720084750000092
in the formula, T is a matrix transposition operation,
ΔU＝[Δu1(k) … Δu1(k+Nu1-1) … Δu2(k) … Δu2(k+Nu2-1)]Twherein Nu1And Nu2Are each u1And u2Of the control time domain, matrix Gp(p ═ 1,2) was calculated from the following formula:
Figure BDA0001720084750000093
Ipis Np×NpOrder unit diagonal matrix, matrix hp(p ═ 1,2) was calculated from the following formula:
Figure BDA0001720084750000101
and 4, step 4: realize quick response predictive control of unit load
(1) Setting the predictive controller parameters: setting a sampling period T and predicting a time domain N1And N2Selection is made only so that N1T and N2T is greater than the pure delay time of the corresponding process, the control time domain Nu1And Nu2Generally 1 or 2, the weight coefficient lambda1、λ2Generally 1 to 10, the real number alpha is generally 500 to 1000,
in the simulation, the sampling period is taken as T-11 seconds, and the prediction time domains are respectively N1＝15、N2Taking Nu in control time domain as 15 respectively1＝2、Nu 22, taking alpha as 800;
(2) acquisition yp(p is 1,2) operation data y at each timep(k-i)，i＝0，1，…，napForm a vector Ybp＝[yp(k) yp(k-1) … yp(k-nap)]T(ii) a Collection u1Operating data u at each time1(k-i)，i＝1，2，…，max(nb11，nb21) Wherein max is the maximum operation, collect u2Operating data u at each time2(k-i)，i＝1，2，…，max(nb11，nb21) And separately calculate u1、u2Increment of each time instant constitutes a vector
ΔUbp＝[Δu1(k-1) … Δu1(k-nbp1) … Δu2(k-1) … Δu2(k-nbp2)]T P 1,2, received power and master
Set value r of steam pressure at each future momentp(k+i)，p＝1，2，i＝1，2，…，NpForm a vector Rp＝[rp(k+1) rp(k+2) … rp(k+Np)]T；
Here na1＝5，na2＝3，nb11＝7，nb12＝4，nb21＝5，nb22The power set point in the simulation is changed in 10MW steps, and the future set point is kept unchanged, i.e. 2 MW steps are performed
r1(k+1)＝r1(k+2)＝…＝r1(k+N1)＝10
The main steam pressure set point is 0 and the future set point is kept unchanged, i.e.
r2(k+1)＝r2(k+2)＝…＝r2(k+N2)＝0
(3) The vector Yb in (2)p、ΔUbpAnd RpCalculating a predicted control amount increment vector delta U by taking the formula (4), the formula (5) and the formula (6);
under the predictive control parameters set in (1), the polynomials are:
E1,15(z-1)＝1+4.8777z-1+13.9902z-2+30.8026z-3+57.6252z-4+96.4575z-5+148.9128z-6+216.1989z-7+299.1320z-8+398.1689z-9+513.4488z-10+644.8377z-11+791.9716z-12+954.2977z-13+1131.1109z-14
E2,15(z-1)＝1+3.7922z-1+8.9900z-2+17.0531z-3+28.3103z-4+42.9792z-5+61.1836z-6+82.9688z-7+108.3151z-8+137.1493z-9+169.3552z-10+204.7821z-11+243.2527z-12+284.5694z-13+328.5205z-14
F1,15(z-1)＝1321.5874-4921.5503z-1+7256.5675z-2-5284.8531z-3+1897.9008z-4-268.6523z-5
F2,15(z-1)＝374.8844-998.2204z-1+889.2179z-2-364.8188z-3
G11,15(z-1)＝-0.0129z-3-0.0292z-4-0.0294z-5-0.0052z-6+0.0456z-7+0.1225z-8+0.2232z-9+0.3451z-10+0.4852z-11+0.6409z-12+0.8095z-13+0.9885z-14+1.1760z-15+1.3698z-16+1.5682z-17+18.7982z-18-22.8218z-19-9.2684z-20+8.4929z-21
G12,15(z-1)＝2.2544+3.3165z-1+3.7430z-2+3.8353z-3+3.7563z-4-3.5934z-5-3.3936z-6+3.1818z-7+2.9709z-8+2.7673z-9+2.5741z-10+2.3924z-11+2.2225z-12+2.0641z-13+1.9167z-14+2.9776z-15+6.7138-16-4.9821z-17+1.2020z-18
G21,15(z-1)＝-0.0011z-3-0.0012z-4-0.0002z-5+0.0014z-6+0.0039z-7+0.0069z-8+0.0104z-9+0.0144z-10+0.0187z-11+0.0233z-12+0.0281z-13+0.0331z-14+0.0382z-15+0.0435z-16+0.0487z-17+0.04811z-18-0.6335z-19
G22,15(z-1)＝-0.0095-0.0182z-1-0.0264z-2-0.0339z-3-0.0410z-4-0.0475z-5-0.0535z-6-0.0592z-7-0.0644z-8-0.0692z-9-0.0737z-10-0.0779z-11-0.0817z-12-0.0854z-13-0.0887z-14+3.4528z-15-2.6977z-16
matrices in the formulae (5) and (6)
Figure BDA0001720084750000121
And
Figure BDA0001720084750000122
from G11,15(z-1)、G12,15(z-1)、G21,15(z-1) And G22,15(z-1) Four polynomial coefficients, matrix Fp,NpFrom F1,15(z-1) And F2,15(z-1) Two polynomial coefficients.
(4) Will vector Δ U1(k) And Δ u2(k) For control, the current control amount of the predictive control is calculated as follows: u. of1(k)＝u1(k-1)+Δu1(k)，u2(k)＝u2(k-1)+Δu2(k) U to be calculated1(k)、u2(k) Acting on the controlled process;
(5) and (4) performing the steps (1) to (4) in a circulating manner to realize quick response prediction control of the unit set load.
In order to reflect the influence of the pressure term weight coefficient beta on the performance of the predictive control, the predictive control under the conditions that the beta is fixed to be 800 and the beta is self-adaptive (namely the beta is automatically adjusted according to the power) is simulated, and the lambda is adopted in the simulation1＝0.5，λ2The simulation results are shown in fig. 3 and fig. 4 as 1; to reflect λ1Taking the influence of value on predictive control performance, maintaining lambda2Each of which is 0 and is taken as1The simulation was performed with β of 800 at 0, 1, and 2, and the results are shown in fig. 5 and 6; to reflect λ2Taking the influence of value on predictive control performance, maintaining lambda1Each of which is 0 and is taken as2The simulation was performed at 0, 3, and 6, and β was 800, and the results are shown in fig. 7 and 8.
As can be seen from fig. 3 and 4, the beta self-adaptive load response is significantly faster than a fixed beta value, it can be seen that the main steam pressure term weight coefficient beta in the predictive control performance index is designed as a function of power, the boiler and the steam turbine can be dynamically coordinated, when the power deviation becomes large, the coefficient beta becomes small, the main steam pressure is allowed to fluctuate greatly, the boiler heat storage is fully utilized, and the response speed of the unit load is effectively improved; as can be seen from FIGS. 5 to 8, λ1、λ2The value of the fetch has a large influence on the predictive control effect, lambda1、λ2The smaller the workThe faster the rate response speed, the greater the main steam pressure dynamic deviation and vice versa, indicating by varying the weight factor λ1、λ2The value of (2) can conveniently and effectively adjust the predictive control performance.

Claims (5)

1. A unit set load quick response prediction control method is characterized by comprising the following steps:
(1) acquiring a discrete controlled autoregressive integral sliding average model of a unit load controlled process;
(2) designing a predictive control performance index;
(3) determining a unit load prediction control law;
(4) the quick response prediction control of the unit load is realized;
the performance indexes of the step (2) are as follows:
Figure FDA0002979701300000011
wherein N is1To a power y1Predicted time domain of, N2Is the main steam pressure y2Is predicted in the time domain, λ1、λ2Beta is a weight coefficient, alpha is a real number, e1(k + i) and ce1(k + i) are the predicted deviation of the unit load at the k + i th moment and the change rate of the predicted deviation, respectively, e2(k + i) and ce2(k + i) the predicted deviation of the main steam pressure and the rate of change of the predicted deviation at the k + i-th time, respectively, eNe(k-1) and ceNeAnd (k-1) is the deviation and the deviation change rate of the unit load at the moment of k-1 respectively.
2. The method for controlling the unit load with the rapid response prediction as claimed in claim 1, wherein the step (1) comprises the steps of:
(11) the transfer function model of the following unit load controlled process is obtained through model identification:
Figure FDA0002979701300000012
wherein, y1Is the unit power, y2Main steam pressure, u1Is the combustion rate of the boiler, u2For regulating the opening of the valve of the steam turbine G11(s) and G21(s) are each independently u1Is input, y1And y2As a process transfer function of the output, G12(s) and G22(s) are each independently u2Is input, y1And y2S is a variable on the complex plane, which is the process transfer function of the output;
(12) discretizing the formula (1) by a sampling period T to obtain a discrete controlled autoregressive integrated moving average (CARIMA) model of the following load controlled process:
Figure FDA0002979701300000013
wherein: a. thei(z-1)、Bij(z-1) (i ═ 1,2, j ═ 1,2) and Δ are for z, respectively-1The polynomial of (c):
Figure FDA0002979701300000021
Figure FDA0002979701300000022
Δ＝1-z-1，
z-1for the backward shift operator, k is the sampling instant, ε1(k) And ε2(k) Is white noise, na, with a mean value of 0iAnd nbijAre respectively polynomial Ai(z-1) And Bij(z-1) Order, ai，lAnd bij，lAre respectively Ai(z-1) And Bij(z-1) The polynomial coefficient of (1).
3. The method for controlling the unit set load quick response prediction as claimed in claim 1, wherein the prediction control law in the step (3) is as follows:
ΔU＝[(G1)TG1+β(G2)TG2]-1[(G1)Th1+β(G2)Th2] (4)
where T is the matrix transposition operation and Δ U is the sum of U1And u2Vector of control increments at the current time k and its future times:
ΔU＝[Δu1(k) … Δu1(k+Nu1-1) … Δu2(k) … Δu2(k+Nu2-1)]T
wherein Nu1And Nu2Are each u1And u2The control time domain of (2); in the formula (4), Gp(p ═ 1,2) is a matrix calculated by the following formula:
Figure FDA0002979701300000023
wherein, IpIs Np×NpOrder unit diagonal matrix, Q1、
Figure FDA0002979701300000024
Matrices of corresponding dimensions, respectively;
h in the formula (4)p(p ═ 1,2) is a matrix calculated by the following formula:
Figure FDA0002979701300000031
wherein Hp、Fp、
Figure FDA0002979701300000032
And Q2Respectively, of corresponding dimensions, Δ UbpIs composed of u1And u2Vector of control increments, Yb, at times preceding the current time kpIs composed of yp(p is 1,2) a vector of values at and before the current time k, RpIs composed of yp(p is 1,2) a vector of set values at each future time; matrix array
Figure FDA0002979701300000033
Sum matrix
Figure FDA0002979701300000034
By the element of Gpq,j(z-1) Coefficient composition of polynomials, matrices
Figure FDA0002979701300000035
Is composed of Fp,j(z-1) Coefficient composition of the polynomial:
Figure FDA0002979701300000036
Figure FDA0002979701300000037
wherein p is 1,2, q is 1,2, Ep,j(z-1) And Fp,j(z-1) To satisfy the charpy equation:
1＝Ep，j(z-1)ΔAp(z-1)+z-jFp，j(z-1) A polynomial of (c).
4. The method for controlling the unit load quick response prediction as claimed in claim 1, wherein the step (4) comprises the steps of:
(41) setting a prediction control parameter: setting a sampling period T and predicting a time domain N1And N2Selection is made only so that N1T and N2T is greater than the pure delay time of the corresponding process, controlTime domain Nu1And Nu2Take 1 or 2, weight coefficient lambda1、λ2Taking 1-10, and taking 500-1000 of real number alpha;
(42) acquisition yp(p is 1,2) operation data y at each timep(k-i)，i＝0，1，…，napForm a vector Ybp＝[yp(k) yp(k-1) … yp(k-nap)]TCollection u1Operating data u at each time1(k-i)，i＝1，2，…，max(nb11，nb21) Wherein max is the maximum operation, collect u2Operating data u at each time2(k-i)，i＝1，2，…，max(nb12，nb22) And separately calculate u1、u2The increment of each time instant constitutes a vector Δ Ubp＝[Δu1(k-1) … Δu1(k-nbp1) … Δu2(k-1) … Δu2(k-nbp2)]TP is 1,2, the set value r of the received power and the main steam pressure at each future momentp(k+i)，p＝1，2，i＝1，2，…，NpForm a vector Rp＝[rp(k+1) rp(k+2) … rp(k+Np)]T；
(43) Will (42) the medium vector Ybp、ΔUbpAnd RpCalculating a predicted control amount increment vector delta U by taking the formula (4), the formula (5) and the formula (6);
(44) will vector Δ U1(k) And Δ u2(k) For control, the current control amount of the predictive control is calculated as follows: u. of1(k)＝u1(k-1)+Δu1(k)，u2(k)＝u2(k-1)+Δu2(k) U to be calculated1(k)、u2(k) Acting on the controlled process;
(45) and (5) circularly executing (42) to (44) to realize quick response prediction control of unit load.
5. The method for unit set load rapid response predictive control as claimed in claim 1, wherein the value of the weight coefficient β of the main steam pressure term in the performance index formula (3) is extractableResponse speed of high unit load; weight coefficient lambda1、λ2The value of (2) can adjust the predictive control performance.
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