CN109062030A - Thermal power unit plant load prediction PID control method based on laguerre function model - Google Patents
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Abstract
The thermal power unit plant load prediction PID control method based on laguerre function model that the invention discloses a kind of, thermal power unit plant load system includes control system and controlled system, specific steps are as follows: S1, the control of control system is exportedU (k) is defeated EnterControlled system obtains controlled outputY(k)；S2 is utilizedY(k)Carry out the identification of laguerre function prediction model parameters；S3 exports controlU(k)It is transferred to laguerre function prediction model as mode input, obtains model prediction outputY M (k+1)；S4 is utilizedY(k)Model prediction is exportedY M (k+1)Feedback correction is carried out, compensation model prediction output is obtainedY P (k+1)；S5 is exported using pid control algorithm rolling optimization compensation model, obtains new control outputU(k+ 1)；S6 repeats step S1-S5, realizes the stable operation of thermal power unit plant load system.The method of the present invention has many advantages, such as that very fast tracking velocity, strong antijamming capability, steady-state error are small, it is short to calculate the time, has stronger adaptive ability.
Description
Technical Field
The invention belongs to the technical field of load control of thermal power unit units, and mainly relates to a thermal power unit load prediction PID control method based on a Laguerre function model.
Background
With the continuous expansion of the capacities of the power grid and the unit set, the requirement of the user on the power utilization quality is continuously improved, and the research on the application of the multi-variable control strategy in the thermal engineering process becomes more and more important. The load control of the thermal power unit is a typical complex multivariable system, and the main requirements of the load control of the thermal power unit are to ensure that the main steam pressure is within a specified range and to ensure that the unit can reach the external load requirement quickly.
At present, experts propose some advanced control strategies aiming at load control of unit units of a thermal power plant, for example, a fuzzy control is proposed by Li Yi nationality and the like, but the control calculation is complex, and a fuzzy rule is difficult to determine; the Luxihong et al propose a neural network control, but this control method has a large amount of calculation and takes a long time.
Model Predictive Control (MPC) is a novel control strategy with wide application prospect developed at the end of the seventies of the twentieth century, and is successfully applied to actual industrial control due to a good control effect. Most experiments show that the performance of the MPC is obviously superior to that of the traditional PID control strategy for the thermal process with large inertia and large hysteresis. However, the traditional MPC has certain limitation, and non-parametric models are adopted for Model Algorithm Control (MAC), Dynamic Matrix Control (DMC) and the like, so that the used data are more, the calculated amount is large, and the self-adaptive control is not convenient to realize; parameterized models used in Generalized Predictive Control (GPC) and the like are sensitive to the time delay and order of the object being controlled. In recent years, Dumont et al in Canada have studied the adaptive predictive control algorithm based on Laguerre function model, and the research finds that the algorithm can obtain better control effect when applied to a diffusion furnace system. The Laguerre function model has the characteristic that the non-parametric model is insensitive to the system order and time delay variation, the parameters of the representation model are less than those of the traditional parametric model, the parameters are convenient for online identification, and the self-adaptive control strategy is easy to realize.
Disclosure of Invention
The invention provides a Laguerre (Laguerre) function model-based PID control method for load prediction of a thermal power unit, which combines adaptive prediction control based on a Laguerre function model with a PID control strategy to obtain a new control method.
In order to solve the technical problems, the invention adopts the following technical means:
the thermal power unit load prediction PID control method based on the Laguerre function model comprises the following specific operation steps:
s1, taking the control output U (k) of the control system as the input of the controlled system, and outputting Y (k) by the controlled system;
s2, utilizing the output Y (k) of the controlled system to identify Laguerre function prediction model parameters;
s3, transmitting the control output U (k) of the control system as the model input to a Laguerre function prediction model, and obtaining the model prediction output Y through the Laguerre function prediction modelM(k+1)；
S4, using the output Y (k) of the controlled system to predict the output Y of the modelM(k +1) performing feedback correction to obtain a compensation model prediction output YP(k+1)；
S5, utilizing a PID control algorithm to roll the optimization model to predict output, and obtaining new control output U (k + 1);
and S6, repeating the steps S1-S5, and realizing the stable operation of the load system of the thermal power unit.
Further, the load system of the thermal power unit meets the mathematical model:
wherein, yiIs the ith system output, and has m system outputs, ujIs the jth system input, for a total of n system inputs, Gij(z-1) Is the transfer function of the ith output to the jth input, i ═ 1, …, m; j is 1, …, n.
Further, the control system control output u (k) satisfies the formula:
where Δ u (K) is the system input increment, K ═ diag (K)1,…,Km)m×mM，Ki(i＝1,...,m)＝[1 0 …0]1×MAnd M is a model control time domain,is the system optimal control increment.
Further, the Laguerre function prediction model parameter is a coefficient matrix C in the model, and can be identified on line by a least square method with a forgetting factor:
wherein, Δ yi(k) Is the output increment, Δ L, of the ith system output at time ki(k) Is the model state increment, λ, of the ith system output at time kiIs a forgetting factor, the value range is 0.9-0.99, Ci(0)＝[0.001,0.001,0.001]P (0) ═ 10^8I, I is identity matrix, I ═ 1, …, m.
Further, the model predicts the output YM(k +1) satisfies the formula:
YM(k+1)＝SHlΔL(k)+ΦYM(k)+SHuΔUM(k) (4)
where Δ L (K) is the model state increment, Δ UM(k) Is the increment of the state of the model input,
S＝diag(S1,…,Sm)Pm×Pm，
Φ＝diag(Φ1,…,Φm)mP×m，
Φi(i＝1,…,m)＝[1 1 …1]T P×1，
η＝1-a2，
in the above formula, a is the pole of the Laguerre function model, N is the series of the Laguerre function model, C is the coefficient matrix of the Laguerre function model, and P is the model prediction step size.
Further, the compensation model predicts the output YP(k +1) satisfies the formula:
YP(k+1)＝YM(k+1)+Hf[Y(k)-YM(k)](5)
wherein HfIs a feedback gain matrix, Hf＝diag(hf1,…,hfm)Pm×m，hfi(i＝1,…,m)＝[1 1…1]T P×1。
Further, in step S5, the prediction output is optimized by rolling using a PID control algorithm, and the specific formula is:
J＝KIE(k+1)TQE(k+1)+KpΔE(k+1)TQΔE(k+1)+ (6)
KDΔ2E(k+1)TQΔ2E(k+1)+ΔUM(k)TRΔUM(k)
wherein E (k +1) ═ Yp(k+1)-Yr(k+1)，
ΔE(k+1)＝ΔD(k+1)+SHuΔ2UM(k)，
Δ2E(k+1)＝Δ2D(k+1)+SHuΔ3UM(k)，
Δ2Um(k)＝(1-q-1)ΔUm(k)，
Δ3Um(k)＝(1-q-1)2ΔUm(k)，
ΔD(k+1)＝(1-q-1)D(k+1)，
Δ2D(k+1)＝(1-q-1)2ΔD(k+1)，
D(k+1)＝SHlΔL(k)+ΦY(k)-Yr(k+1)，
In the above formula Yr(k +1) is a reference trajectory, q-1Is a back-shift operator, Q is an error weighting matrix, R is a control weighting matrix, J is an objective function, KP、KI、KDRespectively a generalized proportional term coefficient, an integral term coefficient and a differential term coefficient.
The method loads the control quantity U (K) of a control system into an RAM of a DSP of a microprocessor in the form of an executable file, a CAP port capturing unit of the DSP reads a position signal, model output of a load system of the thermal power unit is obtained after the position signal passes through a controller of a predictive PID control algorithm based on a Laguerre function model, the model output is compared with an actual output quantity feedback value of a controlled system to obtain deviation, and the control quantity is fed back and adjusted through the deviation, so that the operation of the load system of the thermal power unit is controlled.
The following advantages can be obtained by adopting the technical means:
the invention combines a PID control system with predictive control based on a Laguerre function model to obtain a novel control method applicable to a multi-input multi-output system, introduces the method into a load control system of a unit of a thermal power plant to replace the original traditional control, and provides a novel control strategy. The method has the characteristics of small steady-state error and short rise time of PID control, and has high control precision, high tracking speed, high anti-interference capability, higher adaptability to mathematical model mismatch and external interference of a system and better control quality compared with the traditional predictive control.
Drawings
FIG. 1 is a schematic block diagram of a predictive PID control method based on a Laguerre function model.
FIG. 2 is a simplified block diagram of a mathematical model of a thermal power unit load system.
FIG. 3 is a graph of a thermal power unit load system response.
FIG. 4 is a graph of thermal power unit load system response to a disturbance.
FIG. 5 is a graph of thermal power unit load system response with model mismatch.
The DMC-PID is a control system based on a dynamic matrix control-PID algorithm, and the LMPC-PID is a control system based on a prediction PID algorithm of a Laguerre function model.
Detailed Description
The technical scheme of the invention is further explained by combining the accompanying drawings as follows:
the invention provides a thermal power unit load prediction PID control method based on a Laguerre function model, as shown in figure 1, the control output U (k) of a control system based on a Laguerre function model prediction PID algorithm acts on a controlled system and is used as the input of the controlled system, in addition, the U (k) is also used as the model input to obtain a model prediction output Y through the Laguerre function modelM(k +1), the output value Y (k) of the controlled system is used for model parameter identification and model output feedback correction respectively, and the model parameter identification and the model output feedback correction are both used for further optimizing the control system and ensuring the control effect. Obtaining a new model output value Y through correction compensationP(k +1), introducing a PID control algorithm, adding a reference trajectory, a proportional coefficient, an integral coefficient and a differential coefficient, and performing rolling optimization to obtain new system control output. The invention ensures the normal operation of the thermal power unit in the required range through the cyclic regulation.
The load system of the thermal power unit comprises a control system and a controlled system, and meets the mathematical model:
wherein, yiIs the ith system output, and has m system outputs, ujIs the jth system input, for a total of n system inputs, Gij(z-1) Is the transfer function of the ith output to the jth input, i ═ 1, …, m; j is 1, …, n.
In the following, it is assumed that the load system of the thermal power unit is an m × m multivariable system, and public derivation is performed.
The transfer function matrix of the thermal power unit load system is as follows:
the transfer function G of each channel is expressed by adopting a Laguerre function approximate model with the same structureij(z-1) (i ═ 1, …, m; j ═ 1, …, m), the incremental state space model approximated for the Laguerre function for the entire multivariate system is:
where Δ L (k) is the model state increment, Δ U (k) is the system input increment, Δ YM(k) In order to output the increments for the model,
Ai(i＝1,…m)＝diag(A1,…,Am)mN×mN，
C＝[c1,c2,c3,...,cN]T，
η＝1-a2，
in the above formula, a is the pole of the Laguerre function model, N is the series of the Laguerre function model, and C is the coefficient matrix of the Laguerre function model.
Model output Y of Laguerre function modelM(k +1) satisfies the following formula:
YM(k+1)＝SHlΔL(k)+ΦYM(k)+SHuΔUM(k) (10)
whereinS＝diag(S1,…,Sm)Pm×Pm，
Φ＝diag(Φ1,…,Φm)mP×m，
Φi(i＝1,…,m)＝[1 1…1]T P×1，
In the above formula, Δ UM(k) Is the model input state increment, P is the model prediction step size, and M is the model control time domain.
Using the output y (k) of the controlled system to feed back the predicted output of the correction model, the compensated Laguerre function model output satisfies the following formula:
YP(k+1)＝YM(k+1)+Hf[Y(k)-YM(k)](11)
wherein HfIs a feedback gain matrix, Hf＝diag(hf1,…,hfm)Pm×m，hfi(i＝1,…,m)＝[1 1…1]T P×1。
The formula (11) can be obtained after the finishing simplification:
YP(k+1)＝SHlΔL(k)+ΦY(k)+SHuΔUM(k) (12)
in the multivariable control system, PID control and Laguerre function model predictive control are combined, and a new formula of adding proportion, integration and differentiation is adopted:
J＝KIE(k+1)TQE(k+1)+KpΔE(k+1)TQΔE(k+1)+ (13)KDΔ2E(k+1)TQΔ2E(k+1)+ΔUM(k)TRΔUM(k)
wherein E (k +1) ═ Yp(k+1)-Yr(k+1)，Yr(K +1) is the reference trajectory, Q is the error weighting matrix, R is the control weighting matrix, J is the objective function, KP、KI、KDRespectively a generalized proportional term coefficient, an integral term coefficient and a differential term coefficient.
The E (k +1) is simplified to obtain:
E(k+1)＝YP(k+1)-Yr(k+1)
＝SHlΔL(k)+ΦY(k)+SHuΔUM(k)-Yr(k+1) (14)
＝D(k+1)+SHuΔUM(k)
wherein D (k +1) ═ SHlΔL(k)+ΦY(k)-Yr(k+1)。
Reference trajectoryThe following formula is satisfied:
wherein i ═ 1, …, m, j ═ 1, …, P), αiA softening factor, riIs a set value.
According to the recursive principle, it can be obtained from equation (14):
ΔE(k+1)＝ΔD(k+1)+SHuΔ2UM(k) (16)
Δ2E(k+1)＝Δ2D(k+1)+SHuΔ3UM(k) (17)
introducing a post-shift operator q-1Then, there are:
Δ2Um(k)＝(1-q-1)ΔUm(k) (18)
Δ3Um(k)＝(1-q-1)2ΔUm(k) (19)
ΔD(k+1)＝(1-q-1)D(k+1) (20)
Δ2D(k+1)＝(1-q-1)2ΔD(k+1) (21)
order toFrom formula (13):
the formula (22) is simplified to obtain:
[KI+(1-q-1)KP+(1-q-1)2KD]·[Hu TSTQD(k+1)+Hu TSTQSHuΔUm(k)]+RΔUm(k)＝0(23)
let W be KI+(1-q-1)Kp+(1-q-1)2KDThen the optimal control quantity of the system is increasedCan be expressed as:
ΔU* m(k)＝-[WHu TSTQSHu+R]-1·WHu TSTQD(k+1) (24)
adopting rolling optimization means to obtain delta Um(k) The first element of the system is used as the current control increment of the system, and the control output U (k) of the predictive PID control algorithm based on the Laguerre function model can be obtained and meets the following formula:
wherein, K is diag (K)1,…,Km)m×mM，Ki(i＝1,...,m)＝[1 0…0]1×M。
In order to verify the control effect of the method of the present invention, simulation experiments were performed in this example, and the method of the present invention was compared with a dynamic matrix control-PID (DMC-PID) algorithm. It should be noted that the simulation experiment is only for better explaining the method of the present invention, and does not limit the content of the method of the present invention.
As shown in fig. 2, the load system of a 2 × 2 thermal power unit includes a boiler, a steam turbine, a generator, a control system, and the like, and the mathematical model is as follows:
wherein, u1is a steam turbine throttle command, u2Is a boiler combustion command, NeIs the actual power, PtIs the main steam pressure u1And u2Is two inputs to the system, NeAnd PtAre two outputs of the system.
The Laguerre function prediction model has a series N of 3, a pole a of 0.8, a sampling time T of 10s, and a softening factor αi0.92, the prediction step P is 10, the control time domain M is 1, and the forgetting factor λ is obtainedi0.99, set point The simulation time duration is 8000 s.
As shown in FIG. 3, the system response curves of the two control algorithms can stably reach a set value, no oscillation occurs in power and pressure output, and no overshoot occurs, so that the two control algorithms can meet the performance requirements of the control system, but the regulation time of the thermal power unit load control coefficient response curve based on the Laguerre function model prediction PID control algorithm is shorter than that based on the DMC-PID algorithm, and the method can realize system control more quickly.
To further test the control effect of both control algorithms, the power set point was suddenly changed at 3000s in the simulation, and in addition, a step disturbance with an amplitude of 0.01 was added at 6000 s. As shown in fig. 4, when the set value of the power output is changed, the power output curve of the load control coefficient of the thermal power unit based on Laguerre function model prediction PID control algorithm still reaches the set value quickly and stably, and the steam pressure response curve does not change greatly, which indicates that the method of the present invention can play a certain decoupling role and has excellent tracking performance. After disturbance is added, the control system based on the DMC-PID algorithm generates oscillation, and the system based on the Laguerre function model prediction PID control algorithm has no oscillation when disturbance is solved, and is stably controlled, so that the method has better anti-interference capability.
When the model is greatly mismatched, the parameter setting is not changed, the response curves before and after the model is mismatched are compared, as shown in fig. 5, when the model is mismatched, the output curve of the method only has a slight overshoot, still has a good control effect, and further verifies the effectiveness of the method.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (7)
1. A thermal power unit load prediction PID control method based on a Laguerre function model is characterized in that the method comprises the following specific operation steps:
s1, taking the control output U (k) of the control system as the input of the controlled system, and outputting Y (k) by the controlled system;
s2, identifying parameters of the Laguerre function prediction model by utilizing the output Y (k) of the controlled system;
s3, transmitting the control output U (k) of the control system as the model input to the LaguerreIn the function prediction model, the model prediction output Y is obtained by the Laguerre function prediction modelM(k+1)；
S4, using the output Y (k) of the controlled system to predict the output Y of the modelM(k +1) performing feedback correction to obtain a compensation model prediction output YP(k+1)；
S5, utilizing a PID control algorithm to roll the optimization model to predict output, and obtaining new control output U (k + 1);
and S6, repeating the steps S1-S5, and realizing the stable operation of the load system of the thermal power unit.
2. The laguerre function model-based thermal power unit load prediction PID control method according to claim 1, characterized in that the thermal power unit load system satisfies a mathematical model:
wherein, yiIs the ith system output, and has m system outputs, ujIs the jth system input, for a total of n system inputs, Gij(z-1) Is the transfer function of the ith output to the jth input, i ═ 1, …, m; j is 1, …, n.
3. The laguerre function model-based thermal power unit load prediction PID control method according to claim 1, characterized in that the control system control output u (k) satisfies the formula:
where Δ u (K) is the system input increment, K ═ diag (K)1,…,Km)m×mM，Ki(i＝1,...,m)＝[1 0… 0]1×MAnd M is a model control time domain,is the system optimal control increment.
4. The thermal power unit load prediction PID control method based on the Laguerre function model according to claim 1, wherein the Laguerre function prediction model parameter is a coefficient matrix C in the model, and can be identified on line by a least square method with forgetting factor:
wherein, Δ yi(k) Is the output increment, Δ L, of the ith system output at time ki(k) Is the model state increment, λ, of the ith system output at time kiIs a forgetting factor, the value range is 0.9-0.99, Ci(0)＝[0.001,0.001,0.001]P (0) ═ 10^8I, I is identity matrix, I ═ 1, …, m.
5. The laguerre function model-based thermal power unit load prediction PID control method according to claim 1, characterized in that the model prediction output Y isM(k +1) satisfies the formula:
YM(k+1)＝SHlΔL(k)+ΦYM(k)+SHuΔUM(k)
where Δ L (K) is the model state increment, Δ UM(k) Is the increment of the state of the model input,
S＝diag(S1,…,Sm)Pm×Pm，
Φ＝diag(Φ1,…,Φm)mP×m，
Φi(i＝1,…,m)＝[1 1 … 1]T P×1，
η＝1-a2，
in the above formula, a is the pole of the Laguerre function model, N is the series of the Laguerre function model, C is the coefficient matrix of the Laguerre function model, and P is the model prediction step size.
6. The laguerre function model-based thermal power unit load prediction PID control method according to claim 1, characterized in that the compensation model predicts an output YP(k +1) satisfies the formula:
YP(k+1)＝YM(k+1)+Hf[Y(k)-YM(k)]
wherein HfIs a feedback gain matrix, Hf＝diag(hf1,…,hfm)Pm×m，hfi(i＝1,…,m)＝[1 1 … 1]T P×1。
7. The thermal power unit load prediction PID control method based on the Laguerre function model according to claim 5, wherein the step S5 is to use a PID control algorithm to roll and optimize the prediction output, and the specific formula is as follows:
J＝KIE(k+1)TQE(k+1)+KpΔE(k+1)TQΔE(k+1)+
KDΔ2E(k+1)TQΔ2E(k+1)+ΔUM(k)TRΔUM(k)
wherein E (k +1) ═ Yp(k+1)-Yr(k+1)，
ΔE(k+1)＝ΔD(k+1)+SHuΔ2UM(k)，
Δ2E(k+1)＝Δ2D(k+1)+SHuΔ3UM(k)，
Δ2Um(k)＝(1-q-1)ΔUm(k)，
Δ3Um(k)＝(1-q-1)2ΔUm(k)，
ΔD(k+1)＝(1-q-1)D(k+1)，
Δ2D(k+1)＝(1-q-1)2ΔD(k+1)，
D(k+1)＝SHlΔL(k)+ΦY(k)-Yr(k+1)，
In the above formula Yr(k +1) is a reference trajectory, q-1Is a back-shift operator, Q is an error weighting matrix, R is a control weighting matrix, J is an objective function, KP、KI、KDRespectively a generalized proportional term coefficient, an integral term coefficient and a differential term coefficient.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110488601A (en) * | 2019-09-26 | 2019-11-22 | 山东和信智能科技有限公司 | Fired power generating unit load control system optimization system and method based on Real-time Monitoring Data |
| CN110515304A (en) * | 2019-09-25 | 2019-11-29 | 南京信息工程大学 | Overheating steam temperature PID forecast Control Algorithm based on ARX-Laguerre function model |
| CN111520701A (en) * | 2020-04-28 | 2020-08-11 | 南京信息工程大学 | CFB boiler main steam pressure control method based on improved predictive control |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104154635A (en) * | 2014-08-14 | 2014-11-19 | 河海大学常州校区 | Variable air volume room temperature control method based on fuzzy PID and prediction control algorithm |
| CN105700357A (en) * | 2016-02-25 | 2016-06-22 | 南京信息工程大学 | Boiler combustion system control method based on multivariable PID-PFC |
| CN106483870A (en) * | 2016-09-30 | 2017-03-08 | 国电科学技术研究院 | A kind of control method for coordinating of the fired power generating unit with bounded control output |
| CN107037842A (en) * | 2017-05-15 | 2017-08-11 | 济南大学 | A kind of method of the enthalpy difference laboratory temperature switching control based on fuzzy control and PID control |
| JP2017153224A (en) * | 2016-02-24 | 2017-08-31 | 株式会社アドテックス | Motor control device and motor control method |
-
2018
- 2018-07-27 CN CN201810843019.0A patent/CN109062030A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104154635A (en) * | 2014-08-14 | 2014-11-19 | 河海大学常州校区 | Variable air volume room temperature control method based on fuzzy PID and prediction control algorithm |
| JP2017153224A (en) * | 2016-02-24 | 2017-08-31 | 株式会社アドテックス | Motor control device and motor control method |
| CN105700357A (en) * | 2016-02-25 | 2016-06-22 | 南京信息工程大学 | Boiler combustion system control method based on multivariable PID-PFC |
| CN106483870A (en) * | 2016-09-30 | 2017-03-08 | 国电科学技术研究院 | A kind of control method for coordinating of the fired power generating unit with bounded control output |
| CN107037842A (en) * | 2017-05-15 | 2017-08-11 | 济南大学 | A kind of method of the enthalpy difference laboratory temperature switching control based on fuzzy control and PID control |
Non-Patent Citations (5)
| Title |
|---|
| 乔东东: "基于Laguerre函数模型的改进型预测控制及应用研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 郭 伟，张鹏程，李 涛，乔东东: "多变量Laguerre函数分数阶PID预测控制及其在火电机组负荷控制中的应用", 《中国科技论文》 * |
| 郭伟，乔东东，李涛，李明家: "CFB 锅炉床温Laguerre 函数模型分数阶PID 预测控制", 《热力发电》 * |
| 雎 刚, 韦红旗, 陈绍炳, 徐治皋: "单元机组负荷多变量模型预测控制", 《中国电机工程学报》 * |
| 龚海涛，何健: "基于预测函数PID 控制的混凝投药控制系统", 《自动化与仪表》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110515304A (en) * | 2019-09-25 | 2019-11-29 | 南京信息工程大学 | Overheating steam temperature PID forecast Control Algorithm based on ARX-Laguerre function model |
| CN110488601A (en) * | 2019-09-26 | 2019-11-22 | 山东和信智能科技有限公司 | Fired power generating unit load control system optimization system and method based on Real-time Monitoring Data |
| CN111520701A (en) * | 2020-04-28 | 2020-08-11 | 南京信息工程大学 | CFB boiler main steam pressure control method based on improved predictive control |
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