CN104102782B - Method for controlling temperatures of reactors of pressurized water reactor nuclear power stations by aid of RBF (radial basis function) neural networks - Google Patents
Method for controlling temperatures of reactors of pressurized water reactor nuclear power stations by aid of RBF (radial basis function) neural networks Download PDFInfo
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Abstract
The invention relates to a method for controlling temperatures of reactors of pressurized water reactor nuclear power stations by the aid of RBF (radial basis function) neural networks. The method is used for controlling the temperatures of the reactors of 900MW pressurized water reactor nuclear station units by the aid of the RBF neural networks, and includes testing characteristics of reactor temperature control systems on pressurized water reactor nuclear power unit simulation test platforms; fitting analytic and transfer functions of the systems according to data acquired from tests, solving the reactor temperature transfer functions and building high-precision mathematical models; designing traditional PID (proportion, integration and differentiation) controllers and controlling the temperatures of the reactors. The method has the advantages that the RBF neural networks are combined with the traditional PID controllers on the basis of the traditional PID controllers, so that the temperatures of the reactors can be compositely controlled, and obvious control effects can be realized as compared with control effects of the traditional PID control and fuzzy PID control; the method has high practical application value in the aspect of reactor temperature control, research and application of advanced intelligent control on nuclear power units can be promoted, and accordingly the control level on the units can be improved.
Description
Technical field
The present invention relates to one kind, more particularly to a kind of pressurized-water reactor nuclear power plant reactor temperature RBF neural control method.
Background technology
Since 21 century, recovery and increasingly severe energy crisis with World Economics, nuclear energy is used as cleaning energy
The advantage in source is favored, and constitutes three big pillars of the world power energy together with thermoelectricity, water power.The heap-type of nuclear reactor has
It is various, such as presurized water reactor, boiling-water reactor, PHWR, fast neutron reactor.Presurized water reactor relies on its compact conformation, technology maturation, capital cost
The low, construction period is short etc., and unique advantage becomes so far most widely used nuclear reactor in the world.Present invention research
It is exactly the humidity control system of pressurized-water reactor nuclear power plant reactor, the main purpose of research is to use intelligent controller, such as RBF nerve nets
Network, PID controller optimizes the control effect of reactor temperature with this.The temperature control of nuclear reactor is nuclear power
Stand one of most important control parameter, core temperature directly reflects the reactivity and power of reactor core.It is temperature controlled
Quality is directly connected to the safety of nuclear island.Therefore, pressurized-water reactor nuclear power plant reactor humidity control system is studied and is designed
It is very necessary, and with practical significance.
Reactor temperature control system realizes the control of reactor temperature by adjusting the rod position of temperature control rod R rods.
Mean temperature regulating system operation principle is the moving direction and speed that R rods are controlled by the polarity and size of integrated temperature deviation e
（The movement of R rods is intended to reduce integrated temperature deviation）, move to makes integrated temperature deviation enter within dead zone range always, from
And make temperature equal or approximately equal to setting valve, reach reactor capability and match with secondary circuit steam turbine power or approximate match
Purpose.
In recent years, with the development of nuclear power station, the reactor temperature control system to pressurized-water reactor nuclear power plant is carried out both at home and abroad
Substantial amounts of research.
2000, Chen Yuzhong, Yang Kaijun, Shen Yongping carried out nuclear reactor on nuclear reactor experimental simulation system and have obscured
The research of PID control, result of study shows：Nuclear reactor system is controlled using fuzzy controller, can be compared with when loading and changing
Fast to suppress coolant temperature change, stable state accuracy is high, can reach good control effect.
2003, Hussein Arab-Alibeik, Saeed Setayeshi were with based on LQG/LTR（Linear quadratic
Type Gauss or loop transfer recover）Control is improved presurized water reactor temperature control system.
2009, Ji Huayan, leaf Jian Hua were carried out to the emulation technology of nuclear power generating sets average reactor temperature control system
Inquire into, elaborate that the instrument using mathematics library and the mean temperature control system to nuclear power replicating machine carry out graphical modeling
Method.
In June, 2011, application of Yu's Yun to Model Predictive Control in the control of nuclear power station core temperature is studied.It is logical
The research to Model Predictive Control Theory is crossed, problem is controlled for core temperature, devise the model prediction based on state space
Constrained optimization control strategy, and carried out simulation study in different operating points.
In October, 2011, in generation and, Dai Xiang, Cao Xinrong presurized water reactor temperature coefficient is analyzed under high burnup, lead to
Change initial enrichment, batch of material number, length of the cycle are crossed, a high burnup reactor core is designed.Set up with CASMO-4/SIMULATE-3
Core model, and calculate the temperature coefficient in the case where average discharge burn-up is more than 60GWD/T burn-up levels.Result of study shows,
In the case of high burnup, the neutron energy spectrum for whether having burnable poison, hardening increases moderator temperature coefficient absolute value, and with
The increase of circulation burnup and increase, especially the end of term in longevity absolute value exceeds general presurized water reactor limit value, and fuel temperature coefficient becomes
Change not notable.
In May, 2013, Zhou Haixiang, Lin Ye, Wang Yunwei produce negative reaction by research to pressurized water type reactor temperature effect
Property feedback mechanism be set forth, and based on Point reactor nutron kinetics model, under the simulated environment of Matlab/Simulink
Establish the transient condition reactor core response simulation model with temperature feedback link.According to the simulation model set up, it is considered to anti-
Heap is answered to lift the transient condition of power, the negative-feedback influence on different reactor temperatures coefficient has carried out simulation analysis.Emulation
The result of analysis shows, after reactor needs to change its power output to reactor core introducing step microresponse disturbance, due to
The negative temperature coefficient effect of nuclear fuel and moderator, makes reactor possess intrinsic stability in itself, and the change of reactor core neutron density is
Tend towards stability.According to simulation result, the temperature effect produced by reactor negative temperature coefficient is to anti-under nuclear power plant's transient condition
The control of answering property has favorable influence.
In June, 2013, Gong Dichen with PWR nuclear power plant as object, ground by the important control system to presurized water reactor
Study carefully.On the basis of control simulation model is set up, from the angle of real-time control by Traditional control is theoretical and Intelligent Control Theory phase
With reference to optimization is controlled, from the probabilistic angle of things development and change, using random network and the reason of random process
Quantitative analysis and algorithm optimization are carried out by open the stack operation process, burst accident contingency procedure to nuclear power plant, and using existing
Computer software and hardware condition, detailed design plan and ins and outs are proposed to nuclear power plant's control operation information management system platform
Analysis.
In production process of nuclear power plant, reactor temperature is generally using conventional PI controls.Nuclear reactor is special due to its
Safety issue and the notable nonlinear characteristic itself having, are difficult to accomplish both to improve power using the conventional control as PI
The rapidity of change again can and and overshoot, can only typically make between the two compromise.Improve the property of reactor control system
Can, then need to introduce advanced control strategy.With fuzzy control strategy, fuzzy rule sets up experience to be relied on, it is difficult to accomplish
Precise control.Trial RBF neural of the invention, trains pid parameter, and reactor temperature is controlled, and is compared
Good control effect.
The content of the invention
The present invention be directed in present production process of nuclear power plant, reactor temperature control has strict requirements, control now
System cannot adaptive system parameter wide variation problem, it is proposed that a kind of pressurized-water reactor nuclear power plant reactor temperature RBF nerve nets
Network control method, when reactor steady working condition is run, under parameter small range situation of change, the PI or PID control effect of routine
It is relatively good, but cannot adaptive system parameter wide variation, the present invention RBF neural has been combined with traditional PID control
Come, realize complex controll, the effect of control is better than and traditional PID control and fuzzy-adaptation PID control.
The technical scheme is that：A kind of pressurized-water reactor nuclear power plant reactor temperature RBF neural control method, in pressure
On water-water reactor nuclear power generating sets simulation test platform, the characteristic test of reactor temperature control system is carried out；Number according to obtained by experiment
According to, system is analyzed and transmission function fitting, try to achieve the transmission function of reactor temperature, set up high-precision mathematical modulo
Type；The traditional PID controller of design, is controlled to reactor temperature；On the basis of traditional PID controller, by RBF god
Combine with traditional PID control through network, realize complex controll.
The RBF neural is implemented in combination with complex controll with traditional PID control, is the intelligence under traditional pid algorithm
Optimization, on-line tuning goes out the pid parameter of more high-quality, comprises the following steps that：
1）Determine the structure of RBF networks, the setting of initial value is carried out to learning rate, inertia coeffeicent, weight coefficient；
2）Sampling obtain reactor temperature system input rin (k) and output yout (k), ask for error e rror (k)=
rink(k)-yout(k)；
3）Input, the output of each layer neuron of RBF neural are calculated, third layer output layer is output as PID controller
Three parameters kp, ki, kd；
4）Tri- parameters of kp, ki, kd bring PID controller into, output u (k) of PID controller are calculated, for real-time control
And calculating, reactor temperature is controlled, the output y (k+1) of next step reactor temperature system is produced, calculate mistake now
Difference error (k+1)=rink (k+1)-yout (k+1)；
5）Adjustment RBF weight coefficients, hidden node center vector and sound stage width parameter；
6）Return to step 2, until error meets required precision.
The beneficial effects of the present invention are：Pressurized-water reactor nuclear power plant reactor temperature RBF neural control method of the present invention,
For the research of 900 MW presurized water reactors units, there is actual application value very high to reactor temperature control, can promote advanced
Research of the Based Intelligent Control on nuclear power generating sets and application, so as to improve the controlled level of unit.
Brief description of the drawings
Fig. 1 is the comparison diagram between experimental data curve of the present invention and fitting transfer curve；
Fig. 2 is the step response curve comparison diagram of three groups of pid parameters control of the invention；
Fig. 3 is the structure chart of RBF neural of the present invention；
Fig. 4 is the flow chart of RBF neural Composite PID control of the present invention；
Fig. 5 is the response curve comparison diagram of fuzzy controller and PID controller control；
Fig. 6 is the control effect comparison diagram of RBF neural Composite PID controller of the present invention and PID controller.
Specific embodiment
For 900 MW pressurized-water reactor nuclear power plant units, it is proposed that reactor temperature RBF neural control method, exist first
On compacted clay liners simulation test platform, the characteristic test of reactor temperature control system is carried out；According to obtained by experiment
Data, system is analyzed and transmission function fitting, try to achieve the transmission function of reactor temperature, set up high-precision mathematics
Model；The traditional PID controller of design, is controlled to reactor temperature；On the basis of traditional PID controller, RBF god
Combine with traditional PID control through network, realize complex controll, the effect of control is compared with traditional PID control effect
Compared with, and be compared with the control effect of fuzzy.Comprise the following steps that：
1st, reacted on typical second generation Three links theory 900MW compacted clay liners simulation test platforms first
The characteristic test of heap temperature control system, obtains under the various disturbances of control rod, the response curve of reactor temperature.In reaction
Heap temperature control rod R rods are added under -5% step signal, the response curve of reactor temperature system.Our emulation experiment
Within the data of platform are with nuclear power station actual operating data error 1%, it is believed that the data obtained by experiment are exactly the reality of nuclear power station
Border service data.
2nd, the data according to obtained by experiment, system is analyzed and transmission function fitting.With the skill of System Discrimination
Art carries out the fitting of transmission function to reactor temperature, sets up high-precision Mathematical Modeling.The result requirement worst error of fitting
Within 0.1%.Fitting result is as shown in Figure 1.
One program of design realizes the ratio of error size between experimental record data data corresponding with fitting transmission function
Compared with the error analysis of the curve and actual curve being fitted as shown in table 1 with analysis, error log, maximum Error Absolute Value is
0.00091 i.e. 0.09%, the precision of fitting is very high.
Table 1
3rd, traditional PID controller is designed, and parameter is adjusted repeatedly, realize reactor temperature control.In research process,
Devise 3 groups of pid parameters.Fig. 2 is three groups of step response curve comparison diagrams of pid parameter control.First group of K=0.3, Ti=
In 0.03, Td=100 control parameter combination, reactor temperature has overshoot, and system response time is quickly；Second group of K=0.6, Ti=
In 0.02, Td=70 control parameter combination, reactor temperature both without overshoot, response speed also quickly, the 3rd K=1, Ti=
In 0.008, Td=100 control parameter combination, system non-overshoot, but response speed is slower.Choose second group of control parameter group
Cooperate the pid control parameter for reactor.At this moment PID controller parameter is：K=0.3；Ti=0.03；Td=100, the controller control
Adjustment time t under systems=270s, non-overshoot.Such controller meets the requirement of the technological process of production.
4th, RBF neural Composite PID control algolithm is the intelligent optimization under traditional pid algorithm, and on-line tuning goes out more
The pid parameter of high-quality, is more effectively controlled controlled device.Fig. 3 is the structure chart of RBF neural.
If Fig. 4 is the flow chart that RBF neural Composite PID is controlled, the controller control algolithm can be summarized as：
1）Determine the structure of RBF networks, the setting of initial value is carried out to learning rate, inertia coeffeicent, weight coefficient；
2）Sampling obtain reactor temperature system input rin (k) and output yout (k), ask for error e rror (k)=
rink(k)-yout(k)；
3）Input, the output of each layer neuron of RBF neural are calculated, third layer output layer is output as PID controller
Three parameters kp, ki, kd；
4）Tri- parameters of kp, ki, kd bring PID controller into, output u (k) of PID controller are calculated, for real-time control
And calculating, reactor temperature is controlled, the output y (k+1) of next step reactor temperature system is produced, calculate mistake now
Difference error (k+1)=rink (k+1)-yout (k+1)；
5）Adjustment RBF weight coefficients, hidden node center vector and sound stage width parameter；
6）Return to step 2, until error meets required precision.
Fig. 5 is the response curve comparison diagram of fuzzy controller and PID controller control, now the tune of fuzzy controller
Whole time ts=140s。
Fig. 6 is the control effect comparison diagram of RBF neural Composite PID controller and PID controller, and adjustment time is=75s.It can be seen that, adjustment time is shortened dramatically compared with conventional PID controllers, the adjustment time of fuzzy controller.Cause
This, reactor temperature RBF neural control, scheme is practical.
Claims (2)
1. a kind of pressurized-water reactor nuclear power plant reactor temperature RBF neural control method, it is characterised in that in 900MW presurized water reactors
On nuclear power generating sets simulation test platform, the characteristic test of reactor temperature control system is carried out：Obtain being disturbed in the various of control rod
Under dynamic, the response curve of reactor temperature adds the step signal of setting in reactor temperature control rod R rods, is reacted
The response curve of heap temperature system, such as under the step signal disturbance of setting, the data of Simulation Experimental Platform are with nuclear power station reality
Within service data error 1%, it is believed that the data obtained by experiment are exactly the actual operating data of nuclear power station；
Data according to obtained by experiment, system is analyzed and transmission function fitting, try to achieve the transmission letter of reactor temperature
Number, sets up high-precision Mathematical Modeling；The traditional PID controller of design, is controlled to reactor temperature；In traditional PID
On the basis of controller, RBF neural is combined with traditional PID control, realize complex controll.
2. pressurized-water reactor nuclear power plant reactor temperature RBF neural control method according to claim 1, it is characterised in that
The RBF neural is implemented in combination with complex controll with traditional PID control, is the intelligent optimization under traditional pid algorithm, online
The pid parameter of more high-quality is adjusted out, is comprised the following steps that：
1）Determine the structure of RBF networks, the setting of initial value is carried out to learning rate, inertia coeffeicent, weight coefficient；
2）Sampling obtains input rink (k) and output yout (k) of reactor temperature system, asks for error e rror (k)=rink
(k)-yout(k)；
3）Input, the output of each layer neuron of RBF neural are calculated, third layer output layer is output as the three of PID controller
Individual parameter kp, ki, kd；
4）Tri- parameters of kp, ki, kd bring PID controller into, calculate output u (k) of PID controller, by real-time control and based on
Calculate, reactor temperature is controlled, produce the output yout (k+1) of next step reactor temperature system, calculate mistake now
Difference error (k+1)=rink (k+1)-yout (k+1)；
5）Adjustment RBF weight coefficients, hidden node center vector and sound stage width parameter；
6）Return to step 2), until error meets required precision.
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| CN112307670A (en) * | 2020-09-29 | 2021-02-02 | 中国原子能科学研究院 | Design method of pressurized water reactor core parameter prediction model based on bagging integrated neural network |
| CN112886039B (en) * | 2021-01-11 | 2021-11-23 | 清华大学深圳国际研究生院 | Pressurized water reactor core automatic control method based on reinforcement learning |
| CN113076684B (en) * | 2021-02-23 | 2022-09-20 | 中国核动力研究设计院 | Intelligent calculation method for transient parameters in rod adjusting process of nuclear reactor core |
| CN113465778B (en) * | 2021-06-21 | 2022-04-08 | 中国原子能科学研究院 | Temperature acquisition method |
| CN113963826B (en) * | 2021-10-14 | 2024-01-12 | 西安交通大学 | Abnormal working condition diagnosis and control system for reactor |
| CN114462336B (en) * | 2022-04-11 | 2022-06-24 | 四川大学 | Method for calculating average temperature of coolant of main pipeline of nuclear reactor |
| CN116821588B (en) * | 2023-07-06 | 2024-05-03 | 四川大学 | Reactor working condition judging and predicting method based on DSMF fusion algorithm |
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