CN113110628A - Water level control method of pressurized water reactor deaerator based on PSO - Google Patents
Water level control method of pressurized water reactor deaerator based on PSO Download PDFInfo
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Abstract
The invention relates to a water level control method of a pressurized water reactor deaerator based on PSO (particle swarm optimization), which is based on operation data of a water level of a deaerator of a 900MW nuclear power station simulation unit in Bay, and system identification is carried out on the data by using an MATLAB tool box to obtain a transfer function model of a deaerator control system. The design of a traditional PID controller is carried out aiming at a mathematical model of the control system; introducing a variance into the weight coefficient of the particle swarm algorithm to enable the weight coefficient to evolve towards an expected weight, and simultaneously adopting a method of asynchronous change measures on the learning factor to obtain an improved PSO optimization PID parameter controller. The method has the advantages of high data identification precision, high algorithm convergence speed and enhanced system stability, and can improve the delay of deaerator water level regulation and improve the generating efficiency of nuclear energy to a certain extent.
Description
Technical Field
The invention relates to a control technology, in particular to a water level control method of a pressurized water reactor deaerator based on PSO.
Background
The deaerator is one of key equipment of a boiler and a heating system, and removes oxygen and other gases in boiler feed water to ensure the quality of the feed water. Meanwhile, the deaerator is a mixed heater in a water supply regenerative heating system, and plays a role in heating water supply and increasing the temperature of the water supply. The effect of oxygen-eliminating device water tank is the storage feedwater, and the difference of the oxygen-eliminating device water yield is sent into to the water supply volume of boiler and condensate pump to balanced feed-water pump, when the condensate water yield and water supply volume inconsistent, can adjust through the water level height change of oxygen-eliminating device water tank, satisfies the needs of boiler water supply volume, therefore oxygen-eliminating device water level control is an important link among the nuclear power generation process. The traditional PID control method is based on the experience of engineers to manually adjust the parameters of the PID, so that the result error is large, and the control effect is not ideal. Along with the higher and higher national requirements on nuclear safety and the higher and higher requirements on the running accuracy of a nuclear power station, the water level of a deaerator has a certain delay characteristic, and the higher requirements are provided for a nuclear power station steam turbine control system with large installed capacity and high parameters. The traditional experience-based trial and error method cannot meet the requirement of more accurate control at present, has many potential safety hazards, and does not allow an operation engineer to perform frequent trial and error in the actual operation process. In view of the fact that the automation degree of the controller is not high enough, the research and development of the water level controller of the deaerator, which is high in precision and rapid, has important application value.
The research on the process control system is usually based on a mathematical model of a transfer function, when a step response curve is a comparatively regular curve, the transfer function of the control system is easily derived by using a traditional method (an approximation method, a tangent method and a two-point method), but the methods have poor universality and the calculation accuracy depends on a surveying instrument. Compared with other traditional system identification methods, the transfer function obtained by applying the MATLAB identification toolbox to the control system is higher in precision, higher in speed and high in convenience and efficiency. In subsequent controller design, the technology of the PID controller is more mature in modern development, and is widely applied to industrial control systems due to its simple algorithm, good stability and high reliability. However, due to the time-varying uncertainty and nonlinearity in the actual industrial control system, the conventional PID controller often does not meet the requirement of high precision for the controller in the modern society.
Disclosure of Invention
In order to improve the PID control precision in the actual control process, a water level control method of a pressurized water reactor deaerator based on PSO is provided.
The technical scheme of the invention is as follows: a water level control method of a pressurized water reactor deaerator based on PSO specifically comprises the following steps:
1) obtaining water level operation data of a deaerator:
performing a step disturbance experiment on the water level of a deaerator on a 900MW PWR simulator in the great Asia bay to obtain operation data, and taking the simulation data as the actual operation data when the error of the data is less than 1% compared with the actual operation data of the PWR nuclear power station in the great Asia bay;
2) establishing a water level control system simulation system of a deaerator of a nuclear power plant:
the simulation system comprises a step response signal unit, a PSO algorithm fitness function module consisting of a time module t, a product operation module x and an integral module 1/s, a system transfer function module, a traditional PID controller and an oscilloscope display unit for signal comparison;
taking the step response signal v1 output by the step response signal unit as a system input signal, sequentially passing the input signal through a traditional PID controller and a system transfer function module to generate an output signal, taking the output signal y as a negative feedback signal, and making a difference between the negative feedback signal and the system input signal v1 to form an error signal e and then sending the error signal e to the traditional PID controller to form a closed-loop traditional PID control system;
the output signal y of the other same closed-loop traditional PID control system is differed from the step response signal v1 output by the step response signal unit and is simultaneously sent to a PSO algorithm fitness function module, the PID parameters are optimized through the improved PSO algorithm, and the optimized parameters are sent to a traditional PID controller, so that the control system for optimizing the PID parameters through the improved PSO algorithm is formed;
the output y of the closed-loop traditional PID control system and the output y1 of the control system for optimizing PID parameters by improving the PSO algorithm are connected with a comparative oscilloscope display unit to form the whole simulation control system;
3) the system transfer function module collects actual input and output data of deaerator water level control and sends the data to MATLAB by using a second-order control system model, an identification tool box is adopted for data fitting, and the second-order control system model is solved to obtain a control system transfer function mathematical model;
4) the mathematical model of the transfer function of the control system obtained in the step 3) is sent into a system transfer function module of the control system for optimizing PID parameters by using the improved PSO algorithm, the parameters of a PID controller in the control system are optimized by using the improved PSO algorithm, the improved PSO algorithm takes an ITAE index formed by the difference between a PID system output signal and a step output signal as an evaluation function, namely a fitness function, of the particle swarm optimization algorithm, the value ranges of three parameters of the PID are limited, then the three parameters of the PID are adjusted according to the minimum fitness of the function, an optimal value is searched and sent into a PID controller of the control system for optimizing the PID parameters by using the improved PSO algorithm;
5) comparing output signals of a closed-loop traditional PID control system and a control system for optimizing PID parameters by improving a PSO algorithm, repeating the step 4) to change the particle setting parameters of the improved PSO algorithm, calculating for multiple times to obtain the control effect with the minimum ITAE error index, manually setting the traditional PID for multiple times to obtain the optimal control effect, analyzing the steady-state error and the dynamic deviation of the two control schemes, verifying the control system for optimizing the PID parameters by improving the PSO algorithm, and using the control system for optimizing the PID parameters by improving the PSO algorithm for the water level control of the deaerator of the nuclear power station.
Preferably, the fitness F and the speed updating formula v of the improved PSO algorithmis(t +1), position update formula xis(t +1) are respectively:
vis(t+1)＝ωvis(t)+c1r1|pis-xis(t)|+c2r2|pgs-xis(t)|；
xis(t+1)＝xis(t)+vis(t+1)；
ω＝ωmin+(ωmax-ωmin)*rand()+σ*randn()；
wherein r is1And r2Is a random number in the range of (0,1), vis(t) and vis(t +1) the particle velocities at times t and t +1, respectively; x is the number ofis(t) and xis(t +1) is the particle position at times t and t +1, respectively; pisThe optimal position searched for by the particle so far; pgsThe optimal position searched so far for the whole particle swarm; omegaminAnd ωmaxThe minimum value and the maximum value of the inertia weight factor omega of the PSO are respectively; σ is the variance of the weight coefficient; rand () is uniformly distributed [0,1 ]](ii) a randn () is normally distributed [0,1 ]]；c1ini、c1finAnd c2ini、c2finAcceleration factor c of PSO respectively1、c2Minimum and maximum values of; iter is the current iteration number; MAXiter is the maximum number of iterations.
The invention has the beneficial effects that: the water level control method of the pressurized water reactor deaerator based on the PSO has the advantages of high identification precision, good stability of system control, quick response time, good robustness of the system and the like, and is suitable for controlling the deaerator water level of a pressurized water reactor nuclear power station.
Drawings
FIG. 1 is an overall simulation structure diagram of a water level control method of a pressurized water reactor deaerator based on PSO (particle swarm optimization);
FIG. 2 is a schematic diagram of a PID control system;
FIG. 3 is a flow chart of the improved particle swarm optimization PID parameter algorithm main function of the invention;
FIG. 4 is a graph of the change of the improved PSO optimized PID parameters of the invention;
FIG. 5 is a graph of the change of the improved PSO optimization fitness function of the present invention;
FIG. 6 is an error map of a PID controller based on the improved PSO algorithm of the present invention;
FIG. 7 is a graph of the output of the PID controller of the invention and a PID controller based on the improved PSO algorithm.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The water level control method of the pressurized water reactor deaerator based on the PSO comprises the following steps:
1) obtaining water level operation data of a deaerator:
step disturbance experiment is carried out on the deaerator water level on a 900MW PWR simulator in Bay, operation data is obtained, and compared with actual operation data of the PWR nuclear power station in Bay, the error of the data is less than 1%, which indicates that the data generated when step disturbance signals are input into the simulator is reliable and can represent the actual operation data, and the data can be used for carrying out mathematical modeling on a deaerator water level object subsequently;
2) establishing a water level control system simulation system of a deaerator of a nuclear power plant:
the simulation system comprises a step response signal unit, a fitness function module of a PSO algorithm, a system transfer function module, a traditional PID (proportion integration differentiation) controller and an oscilloscope display unit for signal comparison, wherein the fitness function module of the PSO algorithm consists of a time module t, a product operation module x and an integral module 1/s;
taking the step response signal v1 output by the step response signal unit as a system input signal, sequentially passing the input signal through a traditional PID controller and a system transfer function module to generate an output signal y, taking the output signal as a negative feedback signal, and making a difference between the negative feedback signal and the system input signal v1 to form an error signal e and then sending the error signal e to the traditional PID controller to form a closed-loop traditional PID control system;
the output signal y of the other same closed-loop traditional PID control system is differed from the step response signal v1 output by the step response signal unit and is simultaneously sent to the fitness function module, the PID parameters are optimized through the improved PSO algorithm, and the optimized parameters are sent to the traditional PID controller, so that the control system for optimizing the PID parameters through the improved PSO algorithm is formed;
the output y of the closed-loop traditional PID control system and the output y1 of the control system for optimizing PID parameters by improving the PSO algorithm are connected with a comparative oscilloscope display unit to form the whole simulation control system;
3) performing mathematical modeling on a deaerator water level control object, sending the operation data obtained by the simulator in the step 1) into an MATLAB, performing data fitting by adopting an identification tool box, and solving a second-order control system model to obtain a control system transfer function mathematical model;
4) the mathematical model of the transfer function of the control system obtained in the step 3) is sent into a system transfer function module of the control system for optimizing PID parameters by using the improved PSO algorithm, the parameters of a PID controller in the control system are optimized by using the improved PSO algorithm, the improved PSO algorithm takes an ITAE index (a performance index of time multiplied by an absolute value integral of error) formed by the difference between a PID system output signal and a step output signal as an evaluation function of a particle swarm optimization algorithm, namely a fitness function, the value range of three parameters of the PID is limited, then the three parameters of the PID are adjusted according to the minimum fitness of the function, and the optimal value is searched and sent into a PID controller of the control system for optimizing the PID parameters by using the improved PSO algorithm;
5) comparing output signals of a closed-loop traditional PID control system and a control system for optimizing PID parameters by improving a PSO algorithm, repeating the step 4) to change the particle setting parameters of the improved PSO algorithm, calculating for multiple times to obtain the control effect with the minimum ITAE error index, manually setting the traditional PID for multiple times to obtain the optimal control effect, analyzing the steady-state error and the dynamic deviation of the two control schemes, verifying the control system for optimizing the PID parameters by improving the PSO algorithm, and using the control system for optimizing the PID parameters by improving the PSO algorithm for the water level control of the deaerator of the nuclear power station.
Fig. 1 is an overall simulation structure diagram of a water level control method of a pressurized water reactor deaerator based on a PSO, and fig. 1 includes a step response signal unit, a fitness function module of a PSO algorithm composed of a time module t, a product operation module x and an integration module 1/s, a data acquisition and storage module, a system transfer function module, a traditional PID controller and an oscilloscope display unit for signal comparison.
The step response signal unit is used as an input signal and sequentially acts on a traditional PID controller and a system transfer function module to generate an output signal, so that an open-loop traditional PID control system is formed; then unit negative feedback signals are introduced from the output end, and error signals formed by the unit negative feedback signals and the input signals act on the traditional PID controller to form a closed-loop traditional PID control system; and then adding a fitness function module at the output signal end of the system, optimizing the PID parameters by using the improved PSO algorithm, and sending the optimized PID parameters to a PID controller to form a control system for optimizing the PID parameters by using the improved PSO algorithm. The outputs of the traditional PID closed-loop control system and the control system for optimizing the PID parameters by the improved PSO algorithm are connected with the oscilloscope display unit for comparison, so that the whole simulation control system is formed.
In order to facilitate the design of a controller, the rotating speed control system of the steam turbine of the nuclear power station is simplified into a second-order control system model, and then a discrete linear model of the system can be expressed as follows:
writing a least square method in MATLAB to solve m file functions of second-order control system transfer function parameters, writing input and output data in another m file and calling the previous m file function to obtain parameters of a system to be identified, and simplifying to obtain a mathematical model of the nuclear power station steam turbine speed control system transfer function to be researched:
a traditional PID controller is designed, and then the PID parameters are optimized by using an improved PSO intelligent algorithm, so that the controller with the control performance obviously superior to that of the controller based on the traditional PSO algorithm for optimizing the PID parameters is obtained.
In order to illustrate the correctness and feasibility of the invention, simulation verification is carried out on deaerator water level data collected on a simulation machine of a 900MW unit of the Bay nuclear power station. The experimental parameters are response data of full power operating conditions plus a 10% positive step signal. The specific 29 groups of data are shown in the deaerator water level measurement data shown in table 1.
TABLE 1
Fig. 2 is a schematic structural diagram of a conventional PID control system. In the conventional PID structure, r (t) is a reference input signal, e (t) is a control deviation signal, u (t) is a PID controller output control signal, and y (t) is a controlled system output signal. Wherein the control deviation signal e (t) (r (t) -y (t)), and the control signal u (t) is:
wherein KpIs a proportionality coefficient, TiTo integrate the time constant, TdIs a differential time constant; integral coefficient Ki＝Kp/Ti(ii) a Differential coefficient Kd＝Kp*Td。
The specific parameters in the PSO master function are as follows:
the population scale n is 300; maximum iteration number MAXIter is 100; the particle motion space dimension dim is 3; minimum value ω of inertia weight factor ω of PSOminAnd maximum value ωmaxGenerally, 0.5 and 0.95 are respectively taken; acceleration factor c of PSO1And c2Respectively minimum and maximum ofc1ini、c1finAnd c2ini、c2finGenerally, 0.5 and 2 are respectively taken; the parameter K is calculated according to the called PID parameter and experienceP、Ki、KdAre set to [0,2 ] respectively]、[0,1]、[0,10]。
Inertia weight and acceleration constants in a traditional PSO algorithm speed updating formula are constants, under the condition of more variables, the solving result is not consistent with the reality, and the phenomena of low convergence speed and poor precision exist, so that an improved particle swarm optimization algorithm for parameter estimation by combining random inertia weight and asynchronous learning factors is provided; the design adopts a random inertia weight strategy, changes the variance towards the expected direction of the inertia weight along with the increase of the iteration times, and asynchronously changes the learning factors, so that the variance changes along with the increase of the iteration times, the strategy that the individual learning factors are larger in the early stage and smaller in the later stage of operation is realized, and the group learning factors are opposite and are smaller in the early stage of search and are larger in the later stage; the purpose of improving the algorithm is to encourage the particles to move in the whole search space in the early stage of optimization and improve the convergence speed of the optimal solution in the later stage of optimization.
The flow chart of the PSO main function is shown in FIG. 3, the fitness F and the speed updating formula v in the main functionis(t +1), position update formula xis(t +1) are each
vis(t+1)＝ωvis(t)+c1r1|pis-xis(t)|+c2r2|pgs-xis(t)|；
xis(t+1)＝xis(t)+vis(t+1)；
ω＝ωmin+(ωmax-ωmin)*rand()+σ*randn()；
Wherein r is1And r2Is a random number in the range of (0,1), vis(t) and vis(t +1) the particle velocities at times t and t +1, respectively; x is the number ofis(t) and xis(t +1) is the particle position at times t and t +1, respectively; pisThe optimal position searched for by the particle so far; pgsThe optimal position searched so far for the whole particle swarm; omegaminAnd ωmaxThe minimum value and the maximum value of the inertia weight factor omega of the PSO are respectively; σ is the variance of the weight coefficient; rand () is uniformly distributed [0,1 ]](ii) a randn () is normally distributed [0,1 ]]；c1ini、c1finAnd c2ini、c2finAcceleration factor c of PSO respectively1、c2Minimum and maximum values of; iter is the current iteration number; MAXiter is the maximum number of iterations.
The PID parameters are optimized by the particle swarm optimization algorithm, the difference between the output and the input of the system is used as the evaluation function of the particle swarm optimization algorithm, namely the fitness function input, the numerical value of the fitness function is calculated, then the three parameters of the PID are adjusted according to the fitness of the function, the optimal value is searched in the parameter space of the three variables, and the control performance of the system achieves the best effect. The control system block diagram is shown in fig. 2.
When the m file is operated, the simulink simulation graph also operates, the waveform of the step response can be seen in the oscilloscope, after the condition of maximum iteration of 100 times is met, the program automatically stops operating, the optimal step output waveform is displayed, and the PSO optimization PID parameter change curve and the PSO optimization fitness function change curve are respectively shown in fig. 4 and 5. And importing the data of the obtained optimal oscillogram into a working space, drawing a comparison oscillogram of the optimal oscillogram, and analyzing the data.
And (3) error analysis: FIG. 6 is an error diagram of a PID controller based on the improved PSO algorithm, and the absolute error of the maximum point is 0.0064 when observing FIG. 6; the difference value between the time domain response value of the transfer function calculated by the method and the original data mostly exists between-1% and 1%, and the design performance requirement is met.
From fig. 7, it can be known that the rise time of the optimized PID parameter control based on the improved PSO algorithm is about 11.633s, the overshoot is 2.3%, and the control performance of the optimized PID parameter control based on the improved PSO algorithm is better than that of the optimized PID control based on the traditional PSO algorithm. Compared with the traditional PSO optimization algorithm, the algorithm has high convergence rate. And the simulation result shows that the control based on the improved PSO algorithm optimized PID parameter has the advantages of short regulation time, higher control precision, good noise immunity, good robustness and the like compared with the conventional PID control.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (2)
1. A water level control method of a pressurized water reactor deaerator based on PSO is characterized by comprising the following steps:
1) obtaining water level operation data of a deaerator:
performing a step disturbance experiment on the water level of a deaerator on a 900MW PWR simulator in the great Asia bay to obtain operation data, and taking the simulation data as the actual operation data when the error of the data is less than 1% compared with the actual operation data of the PWR nuclear power station in the great Asia bay;
2) establishing a water level control system simulation system of a deaerator of a nuclear power plant:
the simulation system comprises a step response signal unit, a PSO algorithm fitness function module consisting of a time module t, a product operation module x and an integral module 1/s, a system transfer function module, a traditional PID controller and an oscilloscope display unit for signal comparison;
taking the step response signal v1 output by the step response signal unit as a system input signal, sequentially passing the input signal through a traditional PID controller and a system transfer function module to generate an output signal, taking the output signal y as a negative feedback signal, and making a difference between the negative feedback signal and the system input signal v1 to form an error signal e and then sending the error signal e to the traditional PID controller to form a closed-loop traditional PID control system;
the output signal y of the other same closed-loop traditional PID control system is differed from the step response signal v1 output by the step response signal unit and is simultaneously sent to a PSO algorithm fitness function module, the PID parameters are optimized through the improved PSO algorithm, and the optimized parameters are sent to a traditional PID controller, so that the control system for optimizing the PID parameters through the improved PSO algorithm is formed;
the output y of the closed-loop traditional PID control system and the output y1 of the control system for optimizing PID parameters by improving the PSO algorithm are connected with a comparative oscilloscope display unit to form the whole simulation control system;
3) the system transfer function module uses a second-order control system model, the operation data obtained by the simulator in the step 1) is sent into an MATLAB, an identification tool box is adopted to carry out data fitting, and the second-order control system model is solved to obtain a control system transfer function mathematical model;
4) the mathematical model of the transfer function of the control system obtained in the step 3) is sent into a system transfer function module of the control system for optimizing PID parameters by using the improved PSO algorithm, the parameters of a PID controller in the control system are optimized by using the improved PSO algorithm, the improved PSO algorithm takes an ITAE index formed by the difference between a PID system output signal and a step output signal as an evaluation function, namely a fitness function, of the particle swarm optimization algorithm, the value ranges of three parameters of the PID are limited, then the three parameters of the PID are adjusted according to the minimum fitness of the function, an optimal value is searched and sent into a PID controller of the control system for optimizing the PID parameters by using the improved PSO algorithm;
5) comparing output signals of a closed-loop traditional PID control system and a control system for optimizing PID parameters by improving a PSO algorithm, repeating the step 4) to change the particle setting parameters of the improved PSO algorithm, calculating for multiple times to obtain the control effect with the minimum ITAE error index, manually setting the traditional PID for multiple times to obtain the optimal control effect, analyzing the steady-state error and the dynamic deviation of the two control schemes, verifying the control system for optimizing the PID parameters by improving the PSO algorithm, and using the control system for optimizing the PID parameters by improving the PSO algorithm for the water level control of the deaerator of the nuclear power station.
2. The PSO-based water level control method for the pressurized water reactor deaerator in accordance with claim 1, wherein the fitness F of the improved PSO algorithm and the speed updating formula vis(t +1), position update formula xis(t +1) are respectively:
vis(t+1)＝ωvis(t)+c1r1|pis-xis(t)|+c2r2|pgs-xis(t)|；
xis(t+1)＝xis(t)+vis(t+1)；
ω＝ωmin+(ωmax-ωmin)*rand()+σ*randn()；
wherein r is1And r2Is a random number in the range of (0,1), vis(t) and vis(t +1) the particle velocities at times t and t +1, respectively; x is the number ofis(t) and xis(t +1) is the particle position at times t and t +1, respectively; pisThe optimal position searched for by the particle so far; pgsThe optimal position searched so far for the whole particle swarm; omegaminAnd ωmaxThe minimum value and the maximum value of the inertia weight factor omega of the PSO are respectively; σ is the variance of the weight coefficient; rand () are allUniformly distributed [0,1 ]](ii) a randn () is normally distributed [0,1 ]]；c1ini、c1finAnd c2ini、c2finAcceleration factor c of PSO respectively1、c2Minimum and maximum values of; iter is the current iteration number; MAXiter is the maximum number of iterations.
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