阅读说明：本技术 一种微型燃气轮机热电联供系统抗扰预测控制方法 (Disturbance rejection prediction control method for combined heat and power system of micro gas turbine ) 是由 潘蕾 陈琛 周娣 沈炯 张俊礼 刘西陲 于 2020-05-19 设计创作，主要内容包括：本发明公开了一种微型燃气轮机热电联供系统抗扰预测控制方法,包括采集微型燃气轮机热电联供系统的运行数据,分别辨识转速系统和温度系统的状态空间模型,建立热电联供系统的整体控制模型；基于热电联供系统整体控制模型建立广义扩增状态观测器；基于广义扩增状态观测器建立稳定预测控制的预测模型；建立稳定预测控制器,调节燃料量和一次水旁路阀开度,控制转速和二次供水温度。本发明将系统非线性、模型失配和扰动集总一个扰动,利用广义扩增状态观测器对估计集总扰动和状态量,通过前馈补偿得到预测模型,然后设计稳定预测控制器,在保证系统稳定性的同时,有效提高了系统抗扰性能,具有跟踪速度快、超调量小和抗模型失配和干扰能力强的优点。(The invention discloses an anti-interference prediction control method for a combined heat and power system of a micro gas turbine, which comprises the steps of collecting operation data of the combined heat and power system of the micro gas turbine, respectively identifying state space models of a rotating speed system and a temperature system, and establishing an integral control model of the combined heat and power system; establishing a generalized amplification state observer based on a combined heat and power system overall control model; establishing a prediction model for stable prediction control based on the generalized amplification state observer; and establishing a stable prediction controller, adjusting the fuel quantity and the opening of the primary water bypass valve, and controlling the rotating speed and the secondary water supply temperature. According to the method, the system nonlinearity, the model mismatch and the disturbance are integrated into one disturbance, the generalized amplification state observer is used for estimating the integrated disturbance and the state quantity, the prediction model is obtained through feedforward compensation, and then the stable prediction controller is designed, so that the system anti-disturbance performance is effectively improved while the system stability is ensured, and the method has the advantages of high tracking speed, small overshoot and strong model mismatch and disturbance resisting capability.)

1. The disturbance rejection prediction control method of the micro gas turbine combined heat and power system is characterized by comprising the following steps of:

(1) collecting operation data of a micro gas turbine combined heat and power system, wherein the operation data comprises fuel quantity, opening of a primary water bypass valve, rotating speed and secondary water supply temperature;

(2) respectively identifying state space models of a rotating speed system and a temperature system, and then establishing an integral control model of the combined heat and power system;

(3) establishing a generalized amplification state observer based on a combined heat and power system overall control model;

(4) establishing a prediction model for stable prediction control based on the generalized amplification state observer;

(5) and establishing a stable prediction controller, adjusting the fuel quantity and the opening of the primary water bypass valve, and controlling the rotating speed and the secondary water supply temperature.

2. The disturbance rejection predictive control method for the micro gas turbine cogeneration system according to claim 1, wherein step (2) comprises the steps of:

(21) establishing a process model of the rotating speed system based on the fuel quantity and the rotating speed data, wherein the process model is in the form of:

wherein x is₁(k) Is the state quantity of the rotational speed system at discrete k instants, y₁(k) Is the rotational speed at discrete time k, u₁(k) Is the amount of fuel at discrete time k, (A)₁,B₁,C₁,a₁,b₁) A parameter indicative of a rotational speed system;

(22) establishing a process model of the temperature system based on the fuel quantity, the opening of the primary bypass valve and the secondary water supply temperature system, the form of which is as follows:

wherein x is₂(k) Is the state quantity of the temperature system at discrete time k, y₂(k) Is the temperature of the secondary water supply at discrete time k, u (k) the control quantity of the micro gas turbine cogeneration system at discrete time k, u (k) is [ u (k) ]₁(k) u₂(k)]^TT denotes a matrix transposition symbol, u₂(k) Is the opening of the primary water bypass valve at discrete time k, (A)₂,B₂,C₂,a₂,b₂) A parameter indicative of a temperature system;

(23) establishing an integral control model of the cogeneration system, wherein the integral control model is in the following form:

wherein, 0_2×1Is a zero matrix with 2 rows and 1 column, and (A, B, C, a and B) represent the parameters of the cogeneration system control model.

3. The disturbance rejection predictive control method for the micro gas turbine cogeneration system according to claim 1, wherein step (3) comprises the steps of:

(31) establishing the following combined heat and power system model with disturbance terms:

wherein x (k) state quantity of the micro gas turbine cogeneration system at discrete time k, y (k) output quantity of the micro gas turbine cogeneration system at discrete time k, and y (k) ([ y: [)₁(k) y₂(k)]^TD (k) is state lumped perturbation, v (k) is output lumped perturbation, B_dAnd C_vRespectively a state lumped disturbance matrix and an output lumped disturbance matrix;

(32) establishing a generalized amplification state observer in the form of:

where the symbol "^" represents the estimated value, 0 and I are zero matrix and identity matrix, L is observer gain, △ represents delta, △^sRepresents s increments;

(33) solving observer gain L ═ M^-1N, matrices M and N are obtained by solving the following feasibility problem:

where M, N and S are the matrices to be solved, S is the positive definite symmetric matrix,is a given positive definite symmetric matrix used for adjusting the estimated speed of the observer.

4. The disturbance rejection predictive control method for the micro gas turbine cogeneration system according to claim 1, wherein step (4) comprises the steps of:

(41) solving the steady state input u of the supercritical thermal power generating unit after compensating disturbance_tAnd state x_t：

Wherein y is_rIs a set value of the rotation speed and the temperature;

(42) establishing a prediction model for stable prediction control, wherein the form of the prediction model is as follows:

wherein the content of the first and second substances, andrepresenting the state of the predicted future k + i and k + i +1 times at time k,is the output of the predicted future k + i time at the current time k,is the input at time k + i in the future of time k.

5. The disturbance rejection predictive control method for the micro gas turbine cogeneration system according to claim 1, wherein step (5) comprises the steps of:

(51) establishing an infinite time domain performance index of stable predictive control, wherein the infinite time domain performance index is in the form of:

wherein Q and R are the state and control input adjustment matrix parameters, respectively;

(52) and (5) converting the infinite time domain performance index of the step (51) into the following optimization problem:

limited by:

wherein the content of the first and second substances,gamma, Y, F are the variables to be determined,n is the number of steps to be freely controlled, gamma is the upper bound of the poor time domain performance index, which represents the kronecker product of,w is the upper bound of the observation error,∏₂＝[I₂I₂… I₂]^T,W_j＝[0 …0 1 0 … 0]，u_minand u_maxRepresenting minimum and maximum values of the control input, △ u, respectively_minAnd △ u_maxRespectively representing the minimum and maximum values of the control input increment,G_A＝A^N-1，G_B＝[A^N-1B A^N-2B … A⁰B]；

(53) supercritical thermal power generating unit input for calculating discrete k momentThe fuel quantity and the opening of the primary water bypass valve are adjusted, and further the rotating speed and the secondary water supply temperature are controlled.

Technical Field

The invention relates to a control technology of a micro gas turbine combined heat and power system, in particular to an anti-interference prediction control method of the micro gas turbine combined heat and power system.

Background

Compared with the traditional centralized energy supply system, the distributed energy system can directly provide various forms of energy for users at the user side, and the cascade utilization of the energy is realized. A combined heat and power (MGT-CHP) system of a micro gas turbine is an important distributed energy form and has the characteristics of cleanness, high efficiency, flexible structure and the like. However, due to the dynamic characteristics of the cogeneration system of the micro gas turbine, such as large thermal inertia, multivariable strong coupling, input constraints, nonlinearity, unknown interference, etc., the PID method has not been able to meet the performance requirements. The predictive control system of the existing micro gas turbine combined heat and power system can not ensure the stability of a closed loop system; in addition, the control method of the existing micro gas turbine combined heat and power system cannot consider the control quantity constraint in the design stage of the controller and cannot simultaneously adjust the heat load and the electric load.

Disclosure of Invention

The purpose of the invention is as follows: the invention aims to provide an anti-interference prediction control method for a combined heat and power system of a micro gas turbine, which is used for improving the anti-interference performance of the system and further improving the control quality of the combined heat and power system of the micro gas turbine.

The technical scheme is as follows: the invention discloses an anti-interference prediction control method of a micro gas turbine combined heat and power system, which comprises the following steps:

(1) collecting operation data of a micro gas turbine combined heat and power system, wherein the operation data comprises fuel quantity, opening of a primary water bypass valve, rotating speed and secondary water supply temperature;

(2) respectively identifying state space models of a rotating speed system and a temperature system, and then establishing an integral control model of the combined heat and power system;

(3) establishing a generalized amplification state observer based on a combined heat and power system overall control model;

(4) establishing a prediction model for stable prediction control based on the generalized amplification state observer;

(5) and establishing a stable prediction controller, adjusting the fuel quantity and the opening of the primary water bypass valve, and controlling the rotating speed and the secondary water supply temperature.

Further, the step (2) comprises the following steps:

(21) establishing a process model of the rotating speed system based on the fuel quantity and the rotating speed data, wherein the process model is in the form of:

wherein x is₁(k) Is the state quantity of the rotational speed system at discrete k instants, y₁(k) Is the rotational speed at discrete time k, u₁(k) Is the amount of fuel at discrete time k, (A)₁,B₁,C₁,a₁,b₁) A parameter indicative of a rotational speed system;

(22) establishing a process model of the temperature system based on the fuel quantity, the opening of the primary bypass valve and the secondary water supply temperature system, the form of which is as follows:

wherein x is₂(k) Is the state quantity of the temperature system at discrete time k, y₂(k) Is the temperature of the secondary water supply at discrete time k, u (k) the control quantity of the micro gas turbine cogeneration system at discrete time k, u (k) is [ u (k) ]₁(k) u₂(k)]^TT denotes a matrix transposition symbol, u₂(k) Is the opening of the primary water bypass valve at discrete time k, (A)₂,B₂,C₂,a₂,b₂) A parameter indicative of a temperature system;

(23) establishing an integral control model of the cogeneration system, wherein the integral control model is in the following form:

wherein, 0_2×1Is a zero matrix with 2 rows and 1 column, and (A, B, C, a and B) represent the parameters of the cogeneration system control model.

Further, the step (3) comprises the following steps:

(31) establishing the following combined heat and power system model with disturbance terms:

wherein x (k) state quantity of the micro gas turbine cogeneration system at discrete time k, y (k) output quantity of the micro gas turbine cogeneration system at discrete time k, and y (k) ([ y: [)₁(k) y₂(k)]^TD (k) is state lumped perturbation, v (k) is output lumped perturbation, B_dAnd C_vRespectively a state lumped disturbance matrix and an output lumped disturbance matrix;

(32) establishing a generalized amplification state observer in the form of:

where the symbol "^" represents the estimated value, 0 and I are zero matrix and identity matrix, L is observer gain, △ represents delta, △^sRepresents s increments;

(33) solving observer gain L ═ M^-1N, matrices M and N are obtained by solving the following feasibility problem:

where M, N and S are the matrices to be solved, S is the positive definite symmetric matrix,is a given positive definite symmetric matrix used for adjusting the estimated speed of the observer.

Further, the step (4) comprises the following steps:

(41) solving the steady state input u of the supercritical thermal power generating unit after compensating disturbance_tAnd state x_t：

Wherein y is_rIs a set value of the rotation speed and the temperature;

(42) establishing a prediction model for stable prediction control, wherein the form of the prediction model is as follows:

wherein the content of the first and second substances,andrepresenting the state of the predicted future k + i and k + i +1 times at time k,is the output of the predicted future k + i time at the current time k,is the input at time k + i in the future of time k.

Further, the step (5) comprises the following steps:

(51) establishing an infinite time domain performance index of stable predictive control, wherein the infinite time domain performance index is in the form of:

wherein Q and R are the state and control input adjustment matrix parameters, respectively;

(52) and (5) converting the infinite time domain performance index of the step (51) into the following optimization problem:

limited by:

wherein the content of the first and second substances,gamma, Y, F are the variables to be determined,n is the number of steps to be freely controlled, gamma is the upper bound of the poor time domain performance index,which represents the kronecker product of,w is the upper bound of the observation error,∏₂＝[I₂I₂… I₂]^T，W_j＝[0 … 0 1 0 … 0]， u_minand u_maxRepresenting minimum and maximum values of the control input, △ u, respectively_minAnd △ u_maxRespectively representing the minimum and maximum values of the control input increment,G_B＝[A^N-1B A^N-2B … A⁰B]；

(53) supercritical thermal power generating unit input for calculating discrete k momentThe fuel quantity and the opening of the primary water bypass valve are adjusted, and further the rotating speed and the secondary water supply temperature are controlled.

Has the advantages that: compared with the prior art, the invention has the following beneficial effects:

(1) according to the disturbance rejection prediction control method, the nonlinearity, model mismatch and disturbance of the system are aggregated into one disturbance, a generalized amplification state observer is used for estimating the aggregated disturbance and state quantity, a prediction model is obtained through feedforward compensation, and then a stable prediction controller is designed, so that the disturbance rejection performance of the system is effectively improved while the stability of the system is ensured;

(2) simulation experiments show that the anti-interference predictive control method effectively solves the control difficulty existing in the control of the combined heat and power system of the micro gas turbine, has better control effect than the conventional predictive control and PID control, and has the advantages of high tracking speed, small overshoot and strong anti-model mismatch and interference capability.

Drawings

FIG. 1 is a flow chart of the method steps of the present invention;

FIG. 2 is a schematic diagram of a combined heat and power system of a micro gas turbine according to an embodiment of the present invention;

FIG. 3(a) is the result of controlling the rotation speed of the cogeneration system of the micro gas turbine in experiment 1 according to the method of the present invention;

FIG. 3(b) is the temperature control result of the combined heat and power system of the micro gas turbine in experiment 1 according to the method of the present invention;

FIG. 3(c) is a graph showing the fuel quantity variation in experiment 1 according to the method of the present invention;

FIG. 3(d) is a graph showing the valve variation in experiment 1 according to the method of the present invention;

FIG. 4(a) is the result of controlling the rotation speed of the cogeneration system of the micro gas turbine in experiment 2 according to the method of the present invention;

FIG. 4(b) is the temperature control result of the combined heat and power system of the micro gas turbine in experiment 2 according to the method of the present invention;

FIG. 5 shows the control results of the rotational speed and temperature of the cogeneration system of the micro gas turbine in experiment 3 according to the method of the present invention;

FIG. 6(a) is the result of controlling the rotation speed of the cogeneration system of the micro gas turbine in experiment 4 by the method of the present invention;

FIG. 6(b) is the temperature control result of the micro gas turbine cogeneration system in experiment 4 according to the method of the present invention.

Detailed Description

The technical solution of the present invention is further described with reference to the accompanying drawings and specific embodiments.

The combined heat and power system of the micro gas turbine is a two-input two-output system, and the input quantity of the system comprises fuel quantity u₁And opening u of primary water bypass valve₂The output includes the speed y of the micro gas turbine₁And secondary feed water temperature y₂. The rotation speed of the micro gas turbine is only influenced by the fuel quantity, and the temperature of the secondary water supply is simultaneously influenced by the fuel quantity and the opening degree of the primary water bypass valve, so the micro gas turbine can be divided into a rotation speed system and a temperature system according to the specific physical structure of the micro gas turbine.

As shown in fig. 1, the disturbance rejection prediction control method of the cogeneration system of a micro gas turbine of the invention comprises the following steps:

(1) collecting operation data of a micro gas turbine combined heat and power system, wherein the operation data comprises fuel quantity, opening of a primary water bypass valve, rotating speed and secondary water supply temperature;

(2) respectively identifying state space models of a rotating speed system and a temperature system, and then establishing an integral control model of a micro gas turbine combined heat and power system;

(21) establishing a process model of the rotating speed system based on the fuel quantity and the rotating speed data, wherein the process model is in the form of:

wherein x is₁(k) Is the state quantity of the rotational speed system at discrete k instants, y₁(k) Is the rotational speed at discrete time k, u₁(k) Is the amount of fuel at discrete time k, (A)₁,B₁,C₁,a₁,b₁) A parameter indicative of a rotational speed system;

(22) establishing a process model of the temperature system based on the fuel quantity, the opening of the primary bypass valve and the secondary water supply temperature system, the form of which is as follows:

wherein x is₂(k) Is the state quantity of the temperature system at discrete time k, y₂(k) Is the temperature of the secondary water supply at discrete time k, u (k) the control quantity of the micro gas turbine cogeneration system at discrete time k, u (k) is [ u (k) ]₁(k) u₂(k)]^TT denotes a matrix transposition symbol, u₂(k) Is the opening of the primary water bypass valve at discrete time k, (A)₂,B₂,C₂,a₂,b₂) A parameter indicative of a temperature system;

(23) establishing an integral control model of the micro gas turbine combined heat and power system, wherein the form of the integral control model is as follows:

wherein, 0_2×1Is a zero matrix with 2 rows and 1 column, and (A, B, C, a and B) represent the parameters of the cogeneration system control model.

(3) Establishing a generalized amplification state observer based on an integral control model of a micro gas turbine combined heat and power system;

(31) establishing the following combined heat and power system model with disturbance terms:

wherein x (k) state quantity of the micro gas turbine cogeneration system at discrete time k, y (k) output quantity of the micro gas turbine cogeneration system at discrete time k, and y (k) ([ y: [)₁(k) y₂(k)]^TD (k) is state lumped perturbation, v (k) is output lumped perturbation, B_dAnd C_vRespectively a state lumped disturbance matrix and an output lumped disturbance matrix;

(32) establishing a generalized amplification state observer in the form of:

where the symbol "^" represents the estimated value, 0 and I are zero matrix and identity matrix, L is observer gain, △ represents delta, △^sRepresents s increments;

(33) solving observer gain L ═ M^-1N, matrices M and N are obtained by solving the following feasibility problem:

where M, N and S are the matrices to be solved, S is the positive definite symmetric matrix,is a given positive definite symmetric matrix used for adjusting the estimated speed of the observer.

(4) Establishing a prediction model for stable prediction control based on the generalized amplification state observer;

(41) solving the steady state input u of the supercritical thermal power generating unit after compensating disturbance_tAnd state x_t：

Wherein y is_rIs a set value of the rotation speed and the temperature;

(42) establishing a prediction model for stable prediction control, wherein the form of the prediction model is as follows:

wherein the content of the first and second substances,andrepresenting the state of the predicted future k + i and k + i +1 times at time k,is the output of the predicted future k + i time at the current time k,is the input at time k + i in the future of time k.

(5) And (4) establishing a stable prediction controller based on the prediction model obtained in the step (4), adjusting the fuel quantity and the opening of the primary water bypass valve, and controlling the rotating speed and the secondary water supply temperature.

(51) Establishing an infinite time domain performance index of stable predictive control, wherein the infinite time domain performance index is in the form of:

wherein Q and R are the state and control input adjustment matrix parameters, respectively;

(52) and (5) converting the infinite time domain performance index of the step (51) into the following optimization problem:

limited by:

wherein the content of the first and second substances,gamma, Y, F are the variables to be determined,n is the step of free control, gamma is the upper bound of the infinite time domain performance index,which represents the kronecker product of,w is the upper bound of the observation error,∏₂＝[I₂I₂… I₂]^T，W_j＝[0 … 0 1 0 … 0]，u_minand u_maxRepresenting minimum and maximum values of the control input, △ u, respectively_minAnd △ u_maxRespectively representing the minimum and maximum values of the control input increment,G_B＝[A^N-1B A^N-2B … A⁰B]；

(5-3) calculating input of supercritical thermal power generating unit at discrete k momentThe fuel quantity and the opening of the primary water bypass valve are adjusted, and further the rotating speed and the secondary water supply temperature are controlled.