CN115562033A - Thermal power generating unit coordination system prediction control method based on model set self-adaptive switching - Google Patents

Thermal power generating unit coordination system prediction control method based on model set self-adaptive switching Download PDF

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CN115562033A
CN115562033A CN202211285813.0A CN202211285813A CN115562033A CN 115562033 A CN115562033 A CN 115562033A CN 202211285813 A CN202211285813 A CN 202211285813A CN 115562033 A CN115562033 A CN 115562033A
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刘小鹏
王士博
董淑超
吕磊
胡克磊
毕建伟
张弛
刘栋
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Anhui Huadian Lu'an Power Plant Co ltd
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Abstract

The invention discloses a thermal power unit coordination system prediction control method based on model set self-adaptive switching. The invention can improve the performance of the 660MW thermal power generating unit coordinated control system and ensure the safe, stable and economic operation of the unit.

Description

Thermal power generating unit coordination system prediction control method based on model set self-adaptive switching
Technical Field
The invention relates to the field of automatic control of thermal power generating units, in particular to a thermal power generating unit coordination system prediction control method based on model set self-adaptive switching.
Background
At present, thermal power is transformed from a main body power supply to a basic security power supply and a system regulation power supply, and in order to improve the auxiliary service capacity of the thermal power unit participating in peak shaving and frequency modulation, the flexibility transformation of the thermal power unit is continuously promoted, so that the control task difficulty of a unit coordination system is increased.
At present, a 660MW grade (supercritical) power generation technology with high parameter and large capacity is a main technical flow, in an actual thermal engineering process, a control structure of PID feedback and feedforward is mainly adopted in a 660MW thermal power generating unit coordination system control strategy, the traditional linear control method cannot meet the requirement of flexible operation of a unit, when the unit operates in a large-amplitude and quick variable load mode, due to the fact that a controlled object has nonlinearity, the traditional linear control strategy and parameters are not adaptive any more, the performance of the control system is poor, and the flexibility index and the operation safety of the 660MW thermal power generating unit are influenced. In order to improve the performance of a 660MW thermal power generating unit coordination system, an optimization control method capable of adapting to nonlinearity of a controlled object of the coordination system needs to be designed.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a thermal power generating unit coordination system predictive control method based on model set self-adaptive switching, which solves the difficult problem of control of a boiler-turbine coordination system when a unit operates under a large-amplitude variable working condition, improves the performance of a 660MW thermal power generating unit coordination control system, and ensures safe, stable and economic operation of the unit.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a thermal power generating unit coordination system prediction control method based on model set self-adaptive switching comprises the following steps:
step 1, simplifying a controlled object of a 660MW thermal power unit coordination system into a multivariable model with 3 inputs and 3 outputs, wherein the input quantity of the multivariable model is a fuel quantity instruction u1, a feedwater flow instruction u2 and a steam turbine comprehensive valve position instruction u3, and the output quantity is a unit actual power y1, a machine side main steam pressure y2 and a separator outlet enthalpy value y3; performing input quantity step disturbance tests on the unit at different load points, and establishing a transfer function model of a controlled object of a 660MW thermal power unit coordination system at different load points;
step 2: model G of unit coordination system changing along with load is obtained by model set self-adaptive switching mechanism N ；
And 3, step 3: current model G based on unit coordination system N Designing a prediction controller, adopting a quadratic function of a set value and an actual value of an output quantity as a performance index, and applying a weight coefficient r of a 3 rd input quantity, namely a steam turbine comprehensive valve position instruction u3 3 And performing variable parameter processing to obtain the optimal input increment delta u at the moment k.
Further, the step disturbance test in step 1 specifically includes the following steps:
step 1-1. Determining load points to be tested, the maximum load being raised to 100 Pe considering that the minimum load of the unit is reduced to 30 Pe in daily operation, wherein Pe is the rated load of the fossil power unit, for 660MW fossil power units, pe is 660MW, 30 Pe is taken as the lower limit of the load point, 100 Pe is taken as the upper limit of the load point, the adjacent test load points differ by 10 Pe, in that the load points of the test are 30 Pe, 40 Pe, 50 Pe, 60 Pe, 70 Pe, 80 Pe, 90 Pe and 100 Pe;
step 1-2. Carrying out an input quantity step disturbance experiment at a load point of 30% Pe, wherein the specific method comprises the following steps: keeping a water supply flow instruction u2 and a steam turbine comprehensive valve position instruction u3 unchanged, giving a 5% step quantity to a fuel quantity instruction u1, and recording test data of real generating power y1 of a unit with 3 output quantities, main steam pressure y2 at a machine side and an enthalpy value y3 at an outlet of a separator; keeping a fuel quantity instruction u1 and a steam turbine comprehensive valve position instruction u3 unchanged, giving a 5% step quantity to a water supply flow instruction u2, and recording test data of real generating power y1 of a unit with 3 output quantities, main steam pressure y2 at a machine side and an enthalpy value y3 at an outlet of a separator; keeping the fuel quantity instruction u1 and the water supply flow instruction u2 unchanged, giving a 5% step quantity to the steam turbine comprehensive valve position instruction u3, and recording test data of real generating power y1 of a unit, main machine side steam pressure y2 and an outlet enthalpy value y3 of a separator with 3 output quantities; based on the above test data, a mathematical model G between the input and output of the 660MW thermal power generating unit at the 30-% Pe load point is established 30 Wherein G is 30 The following quantitative relationships exist with the input quantity and the output quantity:
[y1 y2 y3] T ＝G 30 [u1 u2 u3] T (1)
wherein superscript T represents the transpose of the matrix;
steps 1-3. Repeating the above step perturbation test at 40% Pe, 50% Pe, 60% Pe, 70% Pe, 80% Pe, 90% Pe and 100% Pe load points, respectively, to finally obtain the model G at different load points 30 、G 40 、G 50 、G 60 、G 70 、G 80 、G 90 、G 100 Wherein G is 40 Represents the model of the unit coordination system at the load point of 40% Pe, and so on.
Further, the specific method of step 2 is: a model set self-adaptive switching mechanism is provided, which comprises the following steps:
the current model G of the unit coordination system when the unit actual power satisfies 30% Pe ≦ y1 < 35% N Comprises the following steps:
G N ＝G 30 (2)
the current model G of the unit coordination system when the unit actual power satisfies 35% Pe ≦ y1 < 45% N Comprises the following steps:
Figure BDA0003899826940000021
the current model G of the unit coordination system when the unit actual power satisfies 45% Pe ≦ y1 < 55% N Comprises the following steps:
Figure BDA0003899826940000022
the current model G of the unit coordination system when the unit actual power satisfies 55% Pe ≦ y1 < 65% Pe N Comprises the following steps:
Figure BDA0003899826940000031
the current model G of the unit coordination system when the unit actual power satisfies 65% Pe ≦ y1 < 75% Pe N Comprises the following steps:
Figure BDA0003899826940000032
the current model G of the unit coordination system when the unit actual power satisfies 75% Pe ≦ y1 < 85% N Comprises the following steps:
Figure BDA0003899826940000033
the current model G of the unit coordination system when the unit actual power satisfies 85% Pe ≦ y1 < 95% N Comprises the following steps:
Figure BDA0003899826940000034
the current model G of the unit coordination system when the unit actual power satisfies 95% Pe ≦ y1 ≦ 100 ≦ Pe N Comprises the following steps:
G N ＝G 100 (9)
in conclusion, model G of the unit coordination system changing along with the load is obtained by the model set self-adaptive switching mechanism N 。
Further, the specific method of step 3 is: current model G of unit coordination system obtained based on step 2 N Designing a prediction controller, adopting a quadratic function about a set value and an actual value of output quantity as a performance index, wherein a k moment algorithm performance index J (k) is as follows:
J(k)＝[y-y r ] T Q[y-y r ]+Δu T RΔu (10)
in the formula, y is an actual output quantity value and comprises a predicted value of the output quantity at a future moment; y is r Is an output quantity set value; q is an output quantity weight coefficient matrix; r is an input quantityA weight coefficient matrix; Δ u is the increment of the input quantity to be solved, where
Q＝diag(Q 1 ,Q 2 ,Q 3 ),Q i ＝diag(q i )
R＝diag(R 1 ,R 2 ,R 3 ),R i ＝diag(r i )
In the formula, q i Weight coefficient, Q, corresponding to the ith output quantity i Is q i A diagonal matrix of (a); r is i Weight coefficient, R, corresponding to the ith input quantity i Is r of i A diagonal matrix of (a);
in order to improve the load tracking capability of the unit, the weight coefficient r of the 3 rd input quantity, namely the steam turbine comprehensive valve position instruction u3 3 Performing variable parameter processing, wherein the processing method comprises the following steps:
r 3 ′＝k·r 3 (11)
in the formula, k is a variable parameter coefficient, and when a steam turbine comprehensive valve position instruction u3 meets the condition that u3 is more than or equal to 50% and less than or equal to 100%, the value of the coefficient k is 1.2; when the steam turbine comprehensive valve position instruction u3 meets the condition that u3 is more than or equal to 0% and less than 50%, the value of the coefficient k is 1.0,
order to
Figure BDA0003899826940000041
And solving the optimal input increment delta u at the moment k.
Has the advantages that:
the invention provides a model set self-adaptive switching-based 660MW thermal power generating unit coordination system prediction control method, and variable parameter processing is carried out on related parameters in a prediction control algorithm, so that the load regulation performance and the operation stability of a unit are obviously improved.
Detailed Description
In order to achieve the technical aim, the invention provides a model set self-adaptive switching-based 660MW thermal power generating unit coordination system prediction control method, which is used for adapting to large change of dynamic characteristics of a unit under large-amplitude variable working conditions and improving the performance of a 660MW thermal power generating unit coordination control system.
Firstly, a controlled object of a 660MW thermal power generating unit coordination system is simplified into a multivariable model with 3 inputs and 3 outputs, wherein the input quantity of the model is a fuel quantity instruction u1, a feedwater flow instruction u2 and a steam turbine comprehensive valve position instruction u3, and the output quantity is a unit actual power y1, a machine side main steam pressure y2 and a separator outlet enthalpy value y3. And performing input quantity step disturbance tests on the units at different load points, and establishing transfer function models of controlled objects of the 660MW thermal power generating unit coordination system at different load points. The step disturbance test comprises the following specific steps:
step 1-1: the load point at which the test is to be performed is determined. Considering that the unit daily operation minimum load is reduced to 30% Pe, the maximum load is increased to 100% Pe (where Pe is the rated load of the thermal power generating unit and Pe is 660MW for 660MW thermal power generating units), in order to ensure that the test load point covers all the operation conditions of the unit, 30% Pe is taken as the lower load point limit, 100% Pe is taken as the upper load point limit, the adjacent test load points are different by 10% Pe, so that the test load points are 30% Pe, 40% Pe, 50% Pe, 60% Pe, 70% Pe, 80% Pe, 90% Pe and 100% Pe.
Step 1-2: subsequently, an input amount step disturbance experiment was performed at the 30% pe load point. Keeping a water supply flow instruction u2 and a steam turbine comprehensive valve position instruction u3 unchanged, giving a 5% step quantity to a fuel quantity instruction u1, and recording test data of 3 output quantities (unit actual power y1, machine side main steam pressure y2 and separator outlet enthalpy value y 3); keeping a fuel quantity instruction u1 and a steam turbine comprehensive valve position instruction u3 unchanged, giving a 5% step quantity to a water supply flow instruction u2, and recording test data of 3 output quantities (unit actual power y1, machine side main steam pressure y2 and separator outlet enthalpy value y 3); keeping the fuel quantity command u1 and the water supply flow command u2 unchanged, and recording test data of 3 output quantities (unit actual power y1, machine side main steam pressure y2 and separator outlet enthalpy value y 3) by giving a 5% step quantity to a steam turbine comprehensive valve position command u 3. According to the above testExperimental data to establish a mathematical model G between the input and output of the 660MW thermal power generating unit at a load point of 30% Pe 30 Wherein G is 30 The following quantitative relationships exist with the input amount and the output amount:
[y1 y2 y3] T ＝G 30 [u1 u2 u3] T (1)
where the superscript T denotes the transpose of the matrix.
Step 1-3: repeating the above step perturbation test at 40% Pe, 50% Pe, 60% Pe, 70% Pe, 80% Pe, 90% Pe and 100% Pe load points, respectively, to finally obtain model G at different load points 30 、G 40 、G 50 、G 60 、G 70 、G 80 、G 90 、G 100 Wherein G is 40 Represents the model of the unit coordination system at the load point of 40% Pe, and so on.
The accuracy of the controlled object model of the thermal power generating unit is the basis of designing an optimization control method, and due to the fact that the controlled object of the 660MW thermal power generating unit coordination system has strong nonlinearity, each load point model G 30 、G 40 、G 50 、G 60 、G 70 、G 80 、G 90 、G 100 The dynamic characteristics of the unit coordination system in the vicinity of the corresponding load point can only be described more accurately, e.g. when the unit is operated to 50-100 Pe% 30 The accuracy of the quantitative relation between the input quantity and the output quantity of the described model is reduced, so that the model of a single load point cannot be used as a model for describing the dynamic characteristics of the full load section of the unit. In order to improve the accuracy of the model, a model set adaptive switching mechanism is proposed, which comprises the following steps:
the current model G of the unit coordination system when the unit actual power satisfies 30% Pe ≦ y1 < 35% N Comprises the following steps:
G N ＝G 30 (2)
the current model G of the unit coordination system when the unit actual power satisfies 35% Pe ≦ y1 < 45% N Comprises the following steps:
Figure BDA0003899826940000051
the current model G of the unit coordination system when the unit actual power satisfies 45% Pe ≦ y1 < 55% N Comprises the following steps:
Figure BDA0003899826940000052
the current model G of the unit coordination system when the unit actual power satisfies 55% Pe ≦ y1 ≦ 65% N Comprises the following steps:
Figure BDA0003899826940000053
the current model G of the unit coordination system when the unit actual power satisfies 65% Pe ≦ y1 < 75% Pe N Comprises the following steps:
Figure BDA0003899826940000054
Figure BDA0003899826940000061
the current model G of the unit coordination system when the unit actual power satisfies 75% Pe ≦ y1 < 85% N Comprises the following steps:
Figure BDA0003899826940000062
when the unit actual power satisfies 85% Pe ≦ y1 < 95% N Comprises the following steps:
Figure BDA0003899826940000063
working as a machine setWhen the power generation rate is 95% Pe ≦ y1 ≦ 100% Pe, the current model G of the unit coordination system N Comprises the following steps:
G N ＝G 100 (9)
in conclusion, model G of the unit coordination system changing along with the load is obtained by the model set self-adaptive switching mechanism N 。
Current model G based on unit coordination system N Designing a predictive controller, wherein the predictive controller is used for calculating the optimal input increment of the system in real time to ensure that the coordination system has good tracking capability of the set value of the output quantity (actual power, main steam pressure at the machine side and enthalpy value at the outlet of the separator), so that a quadratic function of the set value and the actual value of the output quantity is used as a performance index, and the k-time algorithm performance index J (k) is as follows:
J(k)＝[y-y r ] T Q[y-y r ]+Δu T RΔu (10)
in the formula, y is an actual output quantity value (including a predicted value of the output quantity at a future moment); y is r Is an output quantity set value; q is an output quantity weight coefficient matrix; r is an input quantity weight coefficient matrix; Δ u is the increment of the input quantity to be solved, where
Q＝diag(Q 1 ,Q 2 ,Q 3 ),Q i ＝diag(q i )
R＝diag(R 1 ,R 2 ,R 3 ),R i ＝diag(r i )
In the formula, q i Weight coefficient, Q, corresponding to the ith output quantity i Is q i A diagonal matrix of (a); r is i Weight coefficient, R, corresponding to the ith input quantity i Is r i The diagonal matrix of (a).
In order to improve the load tracking capability of the unit, the weight coefficient r of the 3 rd input quantity (the steam turbine comprehensive valve position instruction u 3) 3 Performing variable parameter processing, wherein the processing method comprises the following steps:
r 3 ′＝k·r 3 (11)
in the formula, k is a parameter-variable coefficient. When the comprehensive valve position instruction u3 of the steam turbine meets the condition that u3 is more than or equal to 50% and less than or equal to 100%, the value of the coefficient k is 1.2; and when the steam turbine comprehensive valve position instruction u3 meets the condition that u3 is more than or equal to 0% and less than 50%, the value of the coefficient k is 1.0.
Order to
Figure BDA0003899826940000064
The optimal input increment deltau at time k can be found.
It should be noted that the above-mentioned contents only illustrate the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and it is obvious to those skilled in the art that several modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations fall within the protection scope of the claims of the present invention.

Claims (4)

1. A thermal power generating unit coordination system prediction control method based on model set self-adaptive switching is characterized by comprising the following steps:
step 1, simplifying a controlled object of a 660MW thermal power unit coordination system into a multivariable model with 3 inputs and 3 outputs, wherein the input quantity of the multivariable model is a fuel quantity instruction u1, a feedwater flow instruction u2 and a steam turbine comprehensive valve position instruction u3, and the output quantity is a unit actual power y1, a machine side main steam pressure y2 and a separator outlet enthalpy value y3; performing input quantity step disturbance tests on the unit at different load points, and establishing a transfer function model of a controlled object of a 660MW thermal power unit coordination system at different load points;
step 2: model G of unit coordination system changing along with load is obtained by model set self-adaptive switching mechanism N ；
And step 3: current model G based on unit coordination system N Designing a prediction controller, adopting a quadratic function of a set value and an actual value of an output quantity as a performance index, and applying a weight coefficient r of a 3 rd input quantity, namely a steam turbine comprehensive valve position instruction u3 3 And performing variable parameter processing to obtain the optimal input increment delta u at the moment k.
2. The thermal power generating unit coordination system predictive control method based on model set adaptive switching according to claim 1, characterized in that the step disturbance test in step 1 specifically comprises the following steps:
step 1-1. Determining load points to be tested, the maximum load being raised to 100 Pe considering that the minimum load of the unit is reduced to 30 Pe in daily operation, wherein Pe is the rated load of the fossil power unit, for 660MW fossil power units, pe is 660MW, 30 Pe is taken as the lower limit of the load point, 100 Pe is taken as the upper limit of the load point, the adjacent test load points differ by 10 Pe, in that the load points of the test are 30 Pe, 40 Pe, 50 Pe, 60 Pe, 70 Pe, 80 Pe, 90 Pe and 100 Pe;
step 1-2. Carrying out an input quantity step disturbance experiment at a load point of 30% Pe, wherein the specific method comprises the following steps: keeping a water supply flow instruction u2 and a steam turbine comprehensive valve position instruction u3 unchanged, giving a 5% step quantity to a fuel quantity instruction u1, and recording test data of real generating power y1 of a unit with 3 output quantities, main steam pressure y2 at a machine side and an enthalpy value y3 at an outlet of a separator; keeping a fuel quantity instruction u1 and a steam turbine comprehensive valve position instruction u3 unchanged, giving a 5% step quantity to a water supply flow instruction u2, and recording test data of real generating power y1 of a unit with 3 output quantities, main steam pressure y2 at a machine side and an enthalpy value y3 at an outlet of a separator; keeping a fuel quantity instruction u1 and a water supply flow instruction u2 unchanged, giving a steam turbine comprehensive valve position instruction u3 with 5% step quantity, and recording test data of real generating power y1 of a unit with 3 output quantities, main steam pressure y2 at a machine side and an enthalpy value y3 at an outlet of a separator; establishing a mathematical model G between the input and output quantities of the 660MW thermal power generating unit at the 30% Pe load point based on the above test data 30 Wherein G is 30 The following quantitative relationships exist with the input quantity and the output quantity:
[y1 y2 y3] T ＝G 30 [u1 u2 u3] T (1)
wherein superscript T represents the transpose of the matrix;
steps 1-3. Repeating the above step perturbation test at 40% Pe, 50% Pe, 60% Pe, 70% Pe, 80% Pe, 90% Pe and 100% Pe load points, respectively, to finally obtain the model G at different load points 30 、G 40 、G 50 、G 60 、G 70 、G 80 、G 90 、G 100 Wherein G is 40 Represents the model of the unit coordination system at the load point of 40% Pe, and so on.
3. The thermal power generating unit coordination system predictive control method based on model set adaptive switching according to claim 1, characterized in that: the specific method of the step 2 is as follows: a model set self-adaptive switching mechanism is provided, which comprises the following steps:
the current model G of the unit coordination system when the unit actual power satisfies 30% Pe ≦ y1 < 35% N Comprises the following steps:
G N ＝G 30 (2)
the current model G of the unit coordination system when the unit actual power satisfies 35% Pe ≦ y1 < 45% N Comprises the following steps:
Figure FDA0003899826930000021
the current model G of the unit coordination system when the unit actual power satisfies 45% Pe ≦ y1 ≦ 55% N Comprises the following steps:
Figure FDA0003899826930000022
the current model G of the unit coordination system when the unit actual power satisfies 55% Pe ≦ y1 < 65% Pe N Comprises the following steps:
Figure FDA0003899826930000023
the current model G of the unit coordination system when the unit actual power satisfies 65% Pe ≦ y1 < 75% Pe N Comprises the following steps:
Figure FDA0003899826930000024
the current model G of the unit coordination system when the unit actual power satisfies 75% Pe ≦ y1 < 85% N Comprises the following steps:
Figure FDA0003899826930000025
the current model G of the unit coordination system when the unit actual power satisfies 85% Pe ≦ y1 < 95% N Comprises the following steps:
Figure FDA0003899826930000026
the current model G of the unit coordination system when the unit actual power satisfies 95% Pe ≦ y1 ≦ 100 ≦ Pe N Comprises the following steps:
G N ＝G 100 (9)
in conclusion, model G of the unit coordination system changing along with the load is obtained by the model set self-adaptive switching mechanism N 。
4. The thermal power generating unit coordination system predictive control method based on model set adaptive switching according to claim 1, characterized in that: the specific method of the step 3 is as follows: current model G of unit coordination system obtained based on step 2 N Designing a prediction controller, adopting a quadratic function of a set value and an actual value of an output quantity as a performance index, wherein the performance index J (k) of the k-time algorithm is as follows:
J(k)＝[y-y r ] T Q[y-y r ]+Δu T RΔu (10)
in the formula, y is an actual output quantity value and comprises a predicted value of the output quantity at a future moment; y is r Is an output quantity set value; q is an output quantity weight coefficient matrix; r is an input quantity weight coefficient matrix; Δ u is the increment of the input quantity to be solved, where
Q＝diag(Q 1 ,Q 2 ,Q 3 ),Q i ＝diag(q i )
R＝diag(R 1 ,R 2 ,R 3 ),R i ＝diag(r i )
In the formula, q i Weight coefficient, Q, corresponding to the ith output quantity i Is q i A diagonal matrix of (a); r is i Weight coefficient, R, corresponding to the ith input quantity i Is r i A diagonal matrix of (a);
in order to improve the load tracking capability of the unit, the weight coefficient r of the 3 rd input quantity, namely the comprehensive valve position instruction u3 of the steam turbine 3 Performing variable parameter processing, wherein the processing method comprises the following steps:
r 3 ′＝k·r 3 (11)
in the formula, k is a variable parameter coefficient, and when a steam turbine comprehensive valve position instruction u3 meets the condition that u3 is more than or equal to 50% and less than or equal to 100%, the value of the coefficient k is 1.2; when the comprehensive valve position instruction u3 of the steam turbine meets the condition that u3 is more than or equal to 0% and less than 50%, the value of the coefficient k is 1.0,
order to
Figure FDA0003899826930000031
And solving the optimal input increment delta u at the moment k.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101328836A (en) * 2008-07-04 2008-12-24 东南大学 Multi-model self-adapting generalized forecast control method of gas turbine rotary speed system
CN102854797A (en) * 2012-09-10 2013-01-02 广东电网公司电力科学研究院 Advanced control multi-model switching method for thermal power generating unit
CN107515598A (en) * 2017-09-06 2017-12-26 东南大学 Fired power generating unit distributed and coordinated control system based on multi-parameter dynamic matrix control
CN112526882A (en) * 2020-11-30 2021-03-19 国家电投集团东北电力有限公司本溪热电分公司 Supercritical unit coordination control method based on hierarchical model predictive control algorithm
CN113238479A (en) * 2021-05-17 2021-08-10 安徽华电六安电厂有限公司 660MW thermal power generating unit multi-model predictive control method based on nearby principle weighting

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101328836A (en) * 2008-07-04 2008-12-24 东南大学 Multi-model self-adapting generalized forecast control method of gas turbine rotary speed system
CN102854797A (en) * 2012-09-10 2013-01-02 广东电网公司电力科学研究院 Advanced control multi-model switching method for thermal power generating unit
CN107515598A (en) * 2017-09-06 2017-12-26 东南大学 Fired power generating unit distributed and coordinated control system based on multi-parameter dynamic matrix control
CN112526882A (en) * 2020-11-30 2021-03-19 国家电投集团东北电力有限公司本溪热电分公司 Supercritical unit coordination control method based on hierarchical model predictive control algorithm
CN113238479A (en) * 2021-05-17 2021-08-10 安徽华电六安电厂有限公司 660MW thermal power generating unit multi-model predictive control method based on nearby principle weighting

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
于国强 等: "全工况下的1 000 MW 超超临界机组协调控制系统 多模型广义预测控制方法及其工程应用", 《热能动力工程》, 31 May 2020 (2020-05-31), pages 9 - 16 *
黄达 等: "700MW超超临界火电机组协调系统全工况多模型预测控制及其工程应用", 《工业控制计算机》, 25 January 2020 (2020-01-25), pages 114 - 118 *

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