CN102841539A - Subcritical coordinative control method based on multiple model predictive control - Google Patents

Abstract

The invention discloses a subcritical coordinative control method based on multiple model predictive control. The method comprises the following steps: determining a plurality of preset local prediction models according to a non-linear work condition range, as well as dynamic responses of main steam pressure and power of a subcriticla machine set; designing a controller for each local prediction model, selecting a control variable, and obtaining an output increment of each local prediction model through an interpolation formula; and during each control period, obtaining an actual control output increment according to an output increment of each controller in a weighing manner; and correcting a feed-forward channel. By adopting the method disclosed by the invention, control output during a transition period can be smoothed, as a result, load and pressure response characteristics of the machine set can be improved effectively; and the subcritical coordinative control method based on multiple model predictive control can adapt to large-scale change in work conditions.

Description

Subcritical control method for coordinating based on the multi-model PREDICTIVE CONTROL

Technical field

The present invention relates to the industrial process control technology, particularly relate to subcritical control method for coordinating based on the multi-model PREDICTIVE CONTROL.

Background technology

Fired power generating unit load variations will be with boiler and steam turbine by a Steady-State Control to another stable state.Existing ripe MPC technology adopts linear model more; The non-linear process modeling is very difficult with the application that is controlled at real process control; Therefore adopting the polyteny model to solve the non-linear of process is a kind of practicable method, but difficult point be how to set up linear model with non-linear process the dynamic perfromance between dynamic.And after setting up good local dynamic model, design is applicable to that the multi-model PREDICTIVE CONTROL strategy of load variations is also extremely important; Because model is local; The DYNAMIC PROCESS characteristic but is overall, when predicting on a large scale when using a model, distortion largely will occur.

Coordinate control for the power plant boiler tradition; Mostly adopt conventional PID+ feedforward control, its advantage is that control structure is simple and have certain robustness, but practical application control effect is unsatisfactory; Can not adapt to the significantly variation of load, normal load and the bigger situation of pressure divergence of occurring.

Summary of the invention

Based on this, being necessary provides a kind of subcritical control method for coordinating based on the multi-model PREDICTIVE CONTROL to the problems referred to above, can multi-model be combined with PREDICTIVE CONTROL, solves fired power generating unit and coordinates control problem.

A kind of subcritical control method for coordinating based on the multi-model PREDICTIVE CONTROL comprises:

According to the dynamic response of the non-linear operating mode scope of Subcritical Units and main steam pressure, power, confirm preset several local prediction models;

To each local prediction modelling controller, select control variable, obtain the output increment of each local prediction model through interpolation formula;

In each control cycle, obtain the working control output increment through weighted type, feedforward path is proofreaied and correct according to the output increment of each controller.

Embodiment of the present invention has following beneficial effect:

The researcher has proposed the various control strategy to overcome this difficult point of coordinating control, like fuzzy control, robust control, internal model control, Active Disturbance Rejection Control and PREDICTIVE CONTROL etc.Wherein multi-model control is a kind of effective ways that solve dynamic perfromance with the complex industrial process control problem of working conditions change.The present invention combines multi-model with PREDICTIVE CONTROL, solve fired power generating unit and coordinate control problem.Multi-model PREDICTIVE CONTROL (MMPC) is a kind of citation form of nonlinear prediction control, adopts a plurality of linearizing partial models to describe same non-linear object.MMPC control is to the partial model CONTROLLER DESIGN, to switch or the output of the weighted type acquisition overall situation.The multi-model PREDICTIVE CONTROL designs predictive controller respectively to a plurality of partial models; Obtain many group control output increments in each control cycle and obtain the working control output increment through weighted type; Can seamlessly transit the control output in period like this; Effectively improve the load and the pressure response characteristic of unit, adapt to large-scale working conditions change.

Description of drawings

Fig. 1 is the process flow diagram that the present invention is based on the subcritical control method for coordinating of multi-model PREDICTIVE CONTROL;

Fig. 2 is the synoptic diagram of different load section multi-model switching principle of the present invention;

Fig. 3 is the step response matrix relationship between control variable of the present invention and the controlled variable;

Fig. 4 is the embodiment synoptic diagram that the present invention is based on the subcritical control method for coordinating of multi-model PREDICTIVE CONTROL.

Embodiment

For making the object of the invention, technical scheme and advantage clearer, the present invention is done to describe in detail further below in conjunction with accompanying drawing.

Fig. 1 is the process flow diagram that the present invention is based on the subcritical control method for coordinating of multi-model PREDICTIVE CONTROL, comprising:

S101:, confirm preset several local prediction models according to the dynamic response of the non-linear operating mode scope of Subcritical Units and main steam pressure, power;

S102: to each local prediction modelling controller, select control variable, obtain the output increment of each local prediction model through interpolation formula;

S103: in each control cycle, obtain the working control output increment through weighted type, feedforward path is proofreaied and correct according to the output increment of each controller.

With load L (k) expression control variable, interpolation formula can be defined as:

$\mathrm{MV}\left(k\right)=\underset{i=1}{\overset{S}{\mathrm{\Σ}}}\mathrm{\ω}\left(i\right)\mathrm{MV}\left(i\right)+\mathrm{\ω}(k+1)\mathrm{MV}(k+1)$

$\mathrm{\ω}\left(i\right)=1-\frac{L\left(k\right)-L\left(i\right)}{L\left(\mathrm{Max}\right)-L\left(\mathrm{Min}\right)}---\left(1\right)$

Wherein S is the segments of whole working condition, in this patent local prediction model preset several be 4, so here S is 4; L (Max) and L (Min) are respectively the upper and lower bound of the segmentation of operating mode, in this patent, are respectively 25% and 100%.

Fig. 2 is the synoptic diagram of different load section multi-model switching principle of the present invention.R is the input setting value among the figure, and Y is output, measures after the correction of reference locus for the input setting value, is generally first-order filtering, makes the actual set value with the close setting value of certain speed R, and big fluctuation appears in anti-locking system.Forecast model and rolling optimization are the link of fundamental forecasting control algolithm, obtain following output of system through forecast model, obtain next optimum control amount constantly through rolling optimization.CV among the figure (Controlled Variable) is a controlled variable; Being some key parameter of controlled device, is pressure and unit load etc. before the machine of unit among the present invention, and MV (Manipulated Variable) is a performance variable; Be to make controlled variable maintain the variable of the required adjusting of setting value; Be the fuel quantity and the main steam speed governing valve opening of unit in this patent, DV (Disturbed Variable) is a disturbance variable, middle finger ature of coal change of the present invention and network load fluctuation.

Employing model prediction technology; Effectively solve delaying greatly of controlling object, the large time delay characteristic, Studies of Multi-variable Model Predictive Control technology and intelligent logical carry out state and judge; The unperturbed of realizing mixture model switches, and solves the control problem of the big operating mode of fired power generating unit through the intellectual status optimizing.

In certain operating mode segment limit, the model of unit can be approximated to be linear model, can set up like the control variable of Fig. 3 and the step response matrix relationship between the controlled variable through applying step disturbance.

Said control variable comprises: coal-supplying amount, valve opening bias.

In certain operating mode segment limit; The model of unit can be approximated to be linear model; Can set up corresponding relation through applying step disturbance like the step response of the multiple-input and multiple-output of Fig. 3, when the curve among the figure is the unit's of being input as increment, the response curve of corresponding output.With the sampling period is length; Get the point value of selected prediction step length; Can form the vector of describing the dynamic response characteristic between the input and output, these vectors are arranged in together, just form the dynamic response matrix of prediction total system input and output dynamic perfromances.Suc as formula (2):

Y _t+1|t=Y _t+1|t-1+AΔU _t （2）

T _T+1|tBe t model prediction constantly output, Y _T+1|t-1For t zero imports free response vector, Δ U constantly _tT is the control increment vector constantly, and A is a dynamic matrix.

T is dynamic matrix constantly:

Inscribe step response FIR (finite impulse response (FIR)) model of system's output during t to i control input:

${[a_i^{\left(1\right)},a_i^{\left(2\right)},...,a_i^{\left(N\right)},...,a_i^{\left(P\right)},...,a_i^{\left(N\right)}]}^T$

Make the minimum deviation between prediction output and the output setting value, the PREDICTIVE CONTROL controller of single model is output as:

$\underset{\mathrm{\ΔU}}{\min }J\left(t\right)={\left|\right|W\left(t\right)-Y_M\left(t\right)\left|\right|}_Q^2+{\left|\right|\mathrm{\ΔU}\left(t\right)\left|\right|}_R^2---\left(5\right)$

Wherein,

The mathematics implication of weighted norm square is:

System's output setting value vector: W (t)=[w (t+1), w (t+2) ..., w (t+P)] ^T

Error weight matrix: Q=diag [Q ⁽¹⁾, Q ⁽²⁾..., Q ^(P)];

T is error power constantly: $Q^{\left(t\right)}=\mathrm{Diag}[q_1^{\left(t\right)},q_2^{\left(t\right)},...,q_m^{\left(t\right)}];$

Control matrix: R=diag [R ⁽¹⁾, R ⁽²⁾..., R ^(M)];

K is control constantly: $R^{\left(t\right)}=\mathrm{Diag}[r_1^{\left(t\right)},r_2^{\left(t\right)},...,r_n^{\left(t\right)}];$

Rolling optimization strategy under the nothing constraint of inscribing in the time of can obtaining k thus:

ΔU(k)=(A ^TQA+R) ^-1A ^TQ[W(k)-Y ₀(k)](6)

Error weight matrix Q has characterized in a following P prediction step the error control degree, and control matrix R has then characterized the degree of restraint to control increment.On the basis of (6) formula, the control law u (t) that DMC inscribes when system is applied t:

Δu(k)=Δu(k|k)=d ^T[W(k)-Y ₀(k)]；(7)

u(k)=u(k-1)+Δu(k) (8)

Wherein, d=[c ^T(A ^TQA+R) ^-1A ^TQ] ^T, c=[I, 0 ..., 0] ^T

Setting is based on the local prediction model of 25% boiler maximum continuous rating load section;

Setting is based on the local prediction model of 50% boiler maximum continuous rating load section;

Setting is based on the local prediction model of 75% boiler maximum continuous rating load section;

Setting is based on the local prediction model of 100% boiler maximum continuous rating load section.

Obtain the controlled quentity controlled variable of local prediction controller through predictive control algorithm after, set up the model that the unit input is coordinated four load sections of 50%, 70%, 95% and 100%MCR of control, the switching between the model adopts interpolation method to carry out, and guarantees the accuracy of MPC control.

Fig. 4 is the embodiment synoptic diagram that the present invention is based on the subcritical control method for coordinating of multi-model PREDICTIVE CONTROL.Below in conjunction with Fig. 4, enumerate a specific embodiment, the present invention is done further introduce in detail.

Except the MPC optimal controller, be the rough schematic of the present fired power generating unit coordinated control system of using always among Fig. 4.The coordinated control system of Subcritical Units adopts one two input two output systems, and control variable (MV) is: fuel quantity M (%), steam turbine pitch aperture μ T (%); Controlled variable (CV) is pressure P T (MPa) before the machine, and its mutual interactively of unit load NE (MW) is as shown in Figure 4.

In design in the early stage of multi-model predictive controller, need be familiar with the technological process of process object, and then accomplish the selection of control variable.Establishment for multi-model zone segmentation and switching law; In the scope of operation operating mode; The non-linear dynamic characteristic piece-wise linearization of object is handled, guaranteed after the segmentation, in each sectional area, can be similar to process is treated to linear process and does not influence the quiet run of system.After sectional area is clear and definite, can be according to technology and on-the-spot actual conditions.

Test phase at the process object data; Related numerous sensor of multi-model predictive controller and topworks; Like the related transmitter of input/output variable or valve if fault all to repair, to guarantee the smooth input of multi-model predictive controller.If confirm that multi-model predictive controller input related sensor and topworks are all normal, then can further carry out the test of process object.Test is through each input variable is carried out upset test, record simultaneously, the data of gatherer process; Concrete condition according to sampling period and image data is carried out filtering to data; Remove data noise, set up input and output step response corresponding relation, if the model curve of obviously running counter to operational characteristic is arranged like Fig. 3; Then need look into test again, up to measuring satisfied model.

Model through identification; Utilize the emulation platform of unit to carry out the emulation and the parameter adjustment of controller; Thereby the variation of bound of test disturbance variable, CV setting value, MV and CV or the like, assess the performance of controller simultaneously after, further adjust the performance that parameter obtains expecting.

Whether normally multi-model predictive controller On-line Control program comes check program operation, the accuracy of testing model simultaneously at first with pretest operational mode operation one to two day.Under this pattern, controller will be accomplished various computings, but the output of controller is not added on the controlled device.All MV bounds will be fixed on the very approaching scope of current setting value in, simultaneously controller can only have very little action.Start the multi-model predictive controller, control variable is applied on the object,, adjust again in case of necessity through the control performance of evaluation and test controller.

After the multi-model predictive controller puts into operation, need certain maintenance to guarantee that the performance of controller maintains optimum.Can to confirm them in allowed limits, remove unessential bound simultaneously through detecting MV and CV operation bound.

When main vapour pressure or load parameter depart from setting value in Preset Time, said control variable is adjusted according to the local preset model under current.

To Subcritical Units multi-model PREDICTIVE CONTROL, it is the problem of overriding concern that the unperturbed between the model switches.Therefore, need to adopt the controller output smoothing and switch scheduling.When output filtering is used for hand/automatic switchover and model blocked operation, make controlled quentity controlled variable more level and smooth, reduce disturbance system; And to switching scheduling; Only when getting into new operating mode above the time span of crossing the border (such as 10 minutes); Just can carry out model switches; Thereby avoided having strengthened the stability of system because operating mode is unstable or the frequent model of the controller that when two operating mode critical zones work, possibly cause switches.

Surpass preset crossing the border during time span when getting into another operating mode, carry out the switching scheduling between each local prediction model.

When the non-shutdown peak regulation of unit; Continuous adjustment by a relatively large margin takes place in the load meeting, at this moment is responsible for to the supervision level module of load changing rate, when the internal loading variation surpassed a certain setting value in three minutes (like 9MW/min); Unit design Maximum speed limit 12MW/min; Consider by its 75% speed), this supervision grade module can trigger and according to affiliated load section coal amount, the water yield etc. revised in advance, reacts (mistake) accent problem of owing that possibly cause slowly at this moment thereby remedy owing to switch scheduling logic.

The above embodiment has only expressed several kinds of embodiments of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to claim of the present invention.Should be pointed out that for the person of ordinary skill of the art under the prerequisite that does not break away from the present invention's design, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with accompanying claims.

Claims (5)

1. the subcritical control method for coordinating based on the multi-model PREDICTIVE CONTROL is characterized in that, comprising:

According to the dynamic response of the non-linear operating mode scope of Subcritical Units and main steam pressure, power, confirm preset several local prediction models;

To each local prediction modelling controller, select control variable, obtain the output increment of each local prediction model through interpolation formula;

In each control cycle, obtain the working control output increment through weighted type, feedforward path is proofreaied and correct according to the output increment of each controller.

2. the subcritical control method for coordinating based on the multi-model PREDICTIVE CONTROL according to claim 1 is characterized in that said control variable comprises: coal-supplying amount, valve opening bias.

3. the subcritical control method for coordinating based on the multi-model PREDICTIVE CONTROL according to claim 1 and 2 is characterized in that, the step that preset several local prediction models are set comprises:

Setting is based on the local prediction model of 25% boiler maximum continuous rating load section;

Setting is based on the local prediction model of 50% boiler maximum continuous rating load section;

Setting is based on the local prediction model of 75% boiler maximum continuous rating load section;

Setting is based on the local prediction model of 100% boiler maximum continuous rating load section.

4. the subcritical control method for coordinating based on the multi-model PREDICTIVE CONTROL according to claim 3 is characterized in that:

When main vapour pressure or load parameter depart from setting value in Preset Time, said control variable is adjusted according to the local preset model under current.

5. the subcritical control method for coordinating based on the multi-model PREDICTIVE CONTROL according to claim 4 is characterized in that:

Surpass preset crossing the border during time span when getting into another operating mode, carry out the switching scheduling between each local prediction model.

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