基于模糊神经网络的直流蒸汽发生器压力协调控制

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目录Part 1 PID type fuzzy controller and parameters adaptive method (1)Part 2 Application of self adaptation fuzzy-PID control for main steam temperature control system in power station (7)Part 3 Neuro-fuzzy generalized predictive control of boiler steam temperature ..................................................................... (13)Part 4 为Part3译文：锅炉蒸汽温度模糊神经网络的广义预测控制21Part 1 PID type fuzzy controller and Parametersadaptive methodWu zhi QIAO, Masaharu MizumotoAbstract: The authors of this paper try to analyze the dynamic behavior of the product-sum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a PD controller that may yield steady-state error for the control system. By relating to the conventional PID control theory, we propose a new fuzzy controller structure, namely PID type fuzzy controller which retains the characteristics similar to the conventional PID controller. In order to improve further the performance of the fuzzy controller, we work out a method to tune the parameters of the PID type fuzzy controller on line, producing a parameter adaptive fuzzy controller. Simulation experiments are made to demonstrate the fine performance of these novel fuzzy controller structures.Keywords: Fuzzy controller; PID control; Adaptive control1. IntroductionAmong various inference methods used in the fuzzy controller found in literatures , the most widely used ones in practice are the Mamdani method proposed by Mamdani and his associates who adopted the Min-max compositional rule of inference based on an interpretation of a control rule as a conjunction of the antecedent and consequent, and the product-sum method proposed by Mizumoto who suggested to introduce the product and arithmetic mean aggregation operators to replace the logical AND (minimum) and OR (maximum) calculations in the Min-max compositional rule of inference.In the algorithm of a fuzzy controller, the fuzzy function calculation is also a complicated and time consuming task. Tagagi and Sugeno proposed a crisp type model in which the consequent parts of the fuzzy control rules are crisp functional representation or crisp real numbers in the simplified case instead of fuzzy sets . With this model of crisp real number output, the fuzzy set of the inference consequence willbe a discrete fuzzy set with a finite number of points, this can greatly simplify the fuzzy function algorithm.Both the Min-max method and the product-sum method are often applied with the crisp output model in a mixed manner. Especially the mixed product-sum crisp model has a fine performance and the simplest algorithm that is very easy to be implemented in hardware system and converted into a fuzzy neural network model. In this paper, we will take account of the product-sum crisp type fuzzy controller.2. PID type fuzzy controller structureAs illustrated in previous sections, the PD function approximately behaves like a parameter time-varying PD controller. Since the mathematical models of most industrial process systems are of type, obviously there would exist an steady-state error if they are controlled by this kind of fuzzy controller. This characteristic has been stated in the brief review of the PID controller in the previous section.If we want to eliminate the steady-state error of the control system, we can imagine to substitute the input (the change rate of error or the derivative of error) of the fuzzy controller with the integration of error. This will result the fuzzy controller behaving like a parameter time-varying PI controller, thus the steady-state error is expelled by the integration action. However, a PI type fuzzy controller will have a slow rise time if the P parameters are chosen small, and have a large overshoot if the P or I parameters are chosen large. So there may be the time when one wants to introduce not only the integration control but the derivative control to the fuzzy control system, because the derivative control can reduce the overshoot of the system's response so as to improve the control performance. Of course this can be realized by designing a fuzzy controller with three inputs, error, the change rate of error and the integration of error. However, these methods will be hard to implement in practice because of the difficulty in constructing fuzzy control rules. Usually fuzzy control rules are constructed by summarizing the manual control experience of an operator who has been controlling the industrial process skillfully and successfully. The operator intuitively regulates the executor to control the process by watching theerror and the change rate of the error between the system's output and the set-point value. It is not the practice for the operator to observe the integration of error. Moreover, adding one input variable will greatly increase the number of control rules, the constructing of fuzzy control rules are even more difficult task and it needs more computation efforts. Hence we may want to design a fuzzy controller that possesses the fine characteristics of the PID controller by using only the error and the change rate of error as its inputs.One way is to have an integrator serially connected to the output of the fuzzy controller as shown in Fig. 1. In Fig. 1,1K and 2K are scaling factors for e and ~ respectively, and fl is the integral constant. In the proceeding text, for convenience, we did not consider the scaling factors. Here in Fig. 2, when we look at the neighborhood of NODE point in the e - ~ plane, it follows from (1) that the control input to the plant can be approximated by(1)Hence the fuzzy controller becomes a parameter time-varying PI controller, itsequivalent proportional control and integral control components are BK2D and ilK1 P respectively. We call this fuzzy controller as the PI type fuzzy controller (PI fc). We can hope that in a PI type fuzzy control system, the steady-state error becomes zero.To verify the property of the PI type fuzzy controller, we carry out some simulation experiments. Before presenting the simulation, we give a description of the simulation model. In the fuzzy control system shown in Fig. 3, the plant model is a second-order and type system with the following transfer function:)1)(1()(21++=s T s T K s G (2) Where K = 16, 1T = 1, and 2T = 0.5. In our simulation experiments, we use thediscrete simulation method, the results would be slightly different from that of a continuous system, the sampling time of the system is set to be 0.1 s. For the fuzzy controller, the fuzzy subsets of e and d are defined as shown in Fig. 4. Their coresThe fuzzy control rules are represented as Table 1. Fig. 5 demonstrates the simulation result of step response of the fuzzy control system with a Pl fc. We can see that the steady-state error of the control system becomes zero, but when the integration factor fl is small, the system's response is slow, and when it is too large, there is a high overshoot and serious oscillation. Therefore, we may want to introduce the derivative control law into the fuzzy controller to overcome the overshoot and instability. We propose a controller structure that simply connects the PD type and the PI type fuzzy controller together in parallel. We have the equivalent structure of that by connecting a PI device with the basic fuzzy controller serially as shown in Fig.6. Where ~ is the weight on PD type fuzzy controller and fi is that on PI type fuzzy controller, the larger a/fi means more emphasis on the derivative control and less emphasis on the integration control, and vice versa. It follows from (7) that the output of the fuzzy controller is(3)3. The parameter adaptive methodThus the fuzzy controller behaves like a time-varying PID controller, its equivalent proportional control, integral control and derivative control components are respectively. We call this new controller structure a PID type fuzzy controller (PID fc). Figs. 7 and 8 are the simulation results of the system's step response of such control system. The influence of ~ and fl to the system performance is illustrated. When ~ > 0 and/3 = 0, meaning that the fuzzy controller behaves like PD fc, there exist a steady-state error. When ~ = 0 and fl > 0, meaning that the fuzzy controller behaves like a PI fc, the steady-state error of the system is eliminated but there is a large overshoot and serious oscillation.When ~ > 0 and 13 > 0 the fuzzy controller becomes a PID fc, the overshoot is substantially reduced. It is possible to get a comparatively good performance by carefully choosing the value of αandβ.4. ConclusionsWe have studied the input-output behavior of the product-sum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a parameter time-varying PD controller. Therefore, the analysis and designing of a fuzzy control system can take advantage of the conventional PID control theory. According to the coventional PID control theory, we have been able to propose some improvement methods for the crisp type fuzzy controller.It has been illustrated that the PD type fuzzy controller yields a steady-state error for the type system, the PI type fuzzy controller can eliminate the steady-state error. We proposed a controller structure, that combines the features of both PD type and PI type fuzzy controller, obtaining a PID type fuzzy controller which allows the control system to have a fast rise and a small overshoot as well as a short settling time.To improve further the performance of the proposed PID type fuzzy controller, the authors designed a parameter adaptive fuzzy controller. The PID type fuzzy controller can be decomposed into the equivalent proportional control, integral control and the derivative control components. The proposed parameter adaptive fuzzy controller decreases the equivalent integral control component of the fuzzy controller gradually with the system response process time, so as to increase the damping of the system when the system is about to settle down, meanwhile keeps the proportional control component unchanged so as to guarantee quick reaction against the system's error. With the parameter adaptive fuzzy controller, the oscillation of the system is strongly restrained and the settling time is shortened considerably.We have presented the simulation results to demonstrate the fine performance of the proposed PID type fuzzy controller and the parameter adaptive fuzzy controller structure.Part 2 Application of self adaptation fuzzy-PID control for main steam temperature control system inpower stationZHI-BIN LIAbstract: In light of the large delay, strong inertia, and uncertainty characteristics of main steam temperature process, a self adaptation fuzzy-PID serial control system is presented, which not only contains the anti-disturbance performance of serial control, but also combines the good dynamic performance of fuzzy control. The simulation results show that this control system has more quickly response, better precision and stronger anti-disturbance ability．Keywords：Main steam temperature；Self adaptation；Fuzzy control；Serial control1. IntroductionThe boiler superheaters of modem thermal power station run under the condition of high temperature and high pressure, and the superheater’s temperature is highest in the steam channels．so it has important effect to the running of the whole thermal power station．If the temperature is too high, it will be probably burnt out. If the temperature is too low ,the efficiency will be reduced So the main steam temperature mast be strictly controlled near the given value．Fig l shows the boiler main steam temperature system structure.Fig.1 boiler main steam temperature systemIt can be concluded from Fig l that a good main steam temperature controlsystem not only has adequately quickly response to flue disturbance and load fluctuation, but also has strong control ability to desuperheating water disturbance. The general control scheme is serial PID control or double loop control system with derivative. But when the work condition and external disturbance change large, the performance will become instable. This paper presents a self adaptation fuzzy-PID serial control system. which not only contains the anti-disturbance performance of serial control, but also combines the good dynamic character and quickly response of fuzzy control ．1. Design of Control SystemThe general regulation adopts serial PID control system with load feed forward ．which assures that the main steam temperature is near the given value 540℃in most condition ．If parameter of PID control changeless and the work condition and external disturbance change large, the performance will become in stable ．The fuzzy control is fit for controlling non-linear and uncertain process. The general fuzzy controller takes error E and error change ratio EC as input variables ．actually it is a non-linear PD controller, so it has the good dynamic performance ．But the steady error is still in existence. In linear system theory, integral can eliminate the steady error. So if fuzzy control is combined with PI control, not only contains the anti-disturbance performance of serial control, but also has the good dynamic performance and quickly response.In order to improve fuzzy control self adaptation ability, Prof ．Long Sheng-Zhao and Wang Pei-zhuang take the located in bringing forward a new idea which can modify the control regulation online ．This regulation is:]1,0[,)1(∈-+=αααEC E UThis control regulation depends on only one parameter α.Once αis fixed ．the weight of E and EC will be fixed and the self adaptation ability will be very small ．It was improved by Prof. Li Dong-hui and the new regulation is as follow;]1,0[,,,3,)1(2,)1(1,)1(0,)1({321033221100∈±=-+±=-+±=-+=-+=ααααααααααααE EC E E EC E E EC E E EC E UBecause it is very difficult to find a self of optimum parameter, a new method is presented by Prof ．Zhou Xian-Lan, the regulation is as follow:)0(),ex p(12>--=k ke αBut this algorithm still can not eliminate the steady error ．This paper combines this algorithm with PI control ，the performance is improved ．2. Simulation of Control System3.1 Dynamic character of controlled objectPapers should be limited to 6 pages Papers longer than 6 pages will be subject to extra fees based on their length ．Fig .2 main steam temperature control system structureFig 2 shows the main steam temperature control system structure ，)(),(21s W s W δδare main controller and auxiliary controller,)(),(21s W s W o o are characters of the leading and inertia sections,)(),(21s W s W H H are measure unit.3.2 Simulation of the general serial PID control systemThe simulation of the general serial PID control system is operated by MATLAB, the simulation modal is as Fig.3.Setp1 and Setp2 are the given value disturbance and superheating water disturb & rice .PID Controller1 and PID Controller2 are main controller and auxiliary controller ．The parameter value which comes from references is as follow ：667.37,074.0,33.31)(25)(111111122===++===D I p D I p p k k k s k sk k s W k s W δδFig.3. the general PID control system simulation modal3.3 Simulation of self adaptation fuzzy-PID control system SpacingThe simulation modal is as Fig 4.Auxiliary controller is:25)(22==p k s W δ.Main controller is Fuzzy-PI structure, and the PI controller is:074.0,33.31)(11111==+=I p I p k k s k k s W δFuzzy controller is realized by S-function, and the code is as fig.5.Fig.4. the fuzzy PID control system simulation modalFig 5 the S-function code of fuzzy control3.4 Comparison of the simulationGiven the same given value disturbance and the superheating water disturbance，we compare the response of fuzzy-PID control system with PID serial control system. The simulation results are as fig.6-7.From Fig6-7,we can conclude that the self adaptation fuzzy-PID control system has the more quickly response, smaller excess and stronger anti-disturbance．4. Conclusion(1)Because it combines the advantage of PID controller and fuzzy controller, theself adaptation fuzzy-PID control system has better performance than the general PID serial control system.(2)The parameter can self adjust according to the error E value. so this kind of controller can harmonize quickly response with system stability．Part 3 Neuro-fuzzy generalized predictive controlof boiler steam temperatureXiangjie LIU, Jizhen LIU, Ping GUANAbstract: Power plants are nonlinear and uncertain complex systems. Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPC) is proposed in this paper. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional controller is obtained.Keywords: Neuro-fuzzy networks; Generalized predictive control; Superheated steam temperature1. IntroductionContinuous process in power plant and power station are complex systems characterized by nonlinearity, uncertainty and load disturbance. The superheater is an important part of the steam generation process in the boiler-turbine system, where steam is superheated before entering the turbine that drives the generator. Controlling superheated steam temperature is not only technically challenging, but also economically important.From Fig.1,the steam generated from the boiler drum passes through the low-temperature superheater before it enters the radiant-type platen superheater. Water is sprayed onto the steam to control the superheated steam temperature in both the low and high temperature superheaters. Proper control of the superheated steam temperature is extremely important to ensure the overall efficiency and safety of the power plant. It is undesirable that the steam temperature is too high, as it can damage the superheater and the high pressure turbine, or too low, as it will lower the efficiency of the power plant. It is also important to reduce the temperaturefluctuations inside the superheater, as it helps to minimize mechanical stress that causes micro-cracks in the unit, in order to prolong the life of the unit and to reduce maintenance costs. As the GPC is derived by minimizing these fluctuations, it is amongst the controllers that are most suitable for achieving this goal.The multivariable multi-step adaptive regulator has been applied to control the superheated steam temperature in a 150 t/h boiler, and generalized predictive control was proposed to control the steam temperature. A nonlinear long-range predictive controller based on neural networks is developed into control the main steam temperature and pressure, and the reheated steam temperature at several operating levels. The control of the main steam pressure and temperature based on a nonlinear model that consists of nonlinear static constants and linear dynamics is presented in that.Fig.1 The boiler and superheater steam generation process Fuzzy logic is capable of incorporating human experiences via the fuzzy rules. Nevertheless, the design of fuzzy logic controllers is somehow time consuming, as the fuzzy rules are often obtained by trials and errors. In contrast, neural networks not only have the ability to approximate non-linear functions with arbitrary accuracy, they can also be trained from experimental data. The neuro-fuzzy networks developed recently have the advantages of model transparency of fuzzy logic and learning capability of neural networks. The NFN is have been used to develop self-tuning control, and is therefore a useful tool for developing nonlinear predictive control. Since NFN is can be considered as a network that consists of several local re-gions, each of which contains a local linear model, nonlinear predictive control based onNFN can be devised with the network incorporating all the local generalized predictive controllers (GPC) designed using the respective local linear models. Following this approach, the nonlinear generalized predictive controllers based on the NFN, or simply, the neuro-fuzzy generalized predictive controllers (NFG-PCs)are derived here. The proposed controller is then applied to control the superheated steam temperature of the 200MW power unit. Experimental data obtained from the plant are used to train the NFN model, and from which local GPC that form part of the NFGPC is then designed. The proposed controller is tested first on the simulation of the process, before applying it to control the power plant.2. Neuro-fuzzy network modellingConsider the following general single-input single-output nonlinear dynamic system:),1(),...,(),(),...,1([)(''+-----=uy n d t u d t u n t y t y f t y ∆+--/)()](),...,1('t e n t e t e e (1)where f[.]is a smooth nonlinear function such that a Taylor series expansion exists, e(t)is a zero mean white noise and Δis the differencing operator,''',,e u y n n n and d are respectively the known orders and time delay of the system. Let the local linear model of the nonlinear system (1) at the operating point )(t o be given by the following Controlled Auto-Regressive Integrated Moving Average (CARIMA) model:)()()()()()(111t e z C t u z B z t y z A d ----+∆= (2) Where )()(),()(1111----∆=z andC z B z A z A are polynomials in 1-z , the backward shift operator. Note that the coefficients of these polynomials are a function of the operating point )(t o .The nonlinear system (1) is partitioned into several operating regions, such that each region can be approximated by a local linear model. Since NFN is a class of associative memory networks with knowledge stored locally, they can be applied to model this class of nonlinear systems. A schematic diagram of the NFN is shown in Fig.2.B-spline functions are used as the membership functions in theNFN for the following reasons. First, B-spline functions can be readily specified by the order of the basis function and the number of inner knots. Second, they are defined on a bounded support, and the output of the basis function is always positive, i.e.,],[,0)(j k j j k x x λλμ-∉=and ],[,0)(j k j j k x x λλμ-∈>.Third, the basis functions form a partition of unity, i.e.,.][,1)(min,∑∈≡j mam j k x x x x μ(3)And fourth, the output of the basis functions can be obtained by a recurrence equation.Fig. 2 neuro-fuzzy network The membership functions of the fuzzy variables derived from the fuzzy rules can be obtained by the tensor product of the univariate basis functions. As an example, consider the NFN shown in Fig.2, which consists of the following fuzzy rules: IF operating condition i (1x is positive small, ... , and n x is negative large),THEN the output is given by the local CARIMA model i:...)()(ˆ...)1(ˆ)(ˆ01+-∆+-++-=d t u b n t y a t y a t yi i a i in i i i a )(...)()(c i in i b i in n t e c t e n d t u b c b -+++--∆+ (4)or )()()()()(ˆ)(111t e z C t u z B z t yz A i i i i d i i ----+∆= (5) Where )()(),(111---z andC z B z A i i i are polynomials in the backward shift operator 1-z , and d is the dead time of the plant,)(t u i is the control, and )(t e i is a zero mean independent random variable with a variance of 2δ. The multivariate basis function )(k i x a is obtained by the tensor products of the univariate basis functions,p i x A a nk k i k i ,...,2,1,)(1==∏=μ (6)where n is the dimension of the input vector x , and p , the total number of weights in the NFN, is given by,∏=+=nk i i k R p 1)( (7)Where i k and i R are the order of the basis function and the number of inner knots respectively. The properties of the univariate B-spline basis functions described previously also apply to the multivariate basis function, which is defined on the hyper-rectangles. The output of the NFN is,∑∑∑=====p i i i p i ip i i i a y aa yy 111ˆˆˆ (8) 3. Neuro-fuzzy modelling and predictive control of superheatedsteam temperatureLet θbe the superheated steam temperature, and θμ, the flow of spray water to the high temperature superheater. The response of θcan be approximated by a second order model:The linear models, however, only a local model for the selected operating point. Since load is the unique antecedent variable, it is used to select the division between the local regions in the NFN. Based on this approach, the load is divided into five regions as shown in Fig.3,using also the experience of the operators, who regard a load of 200MW as high,180MW as medium high,160MW as medium,140MW as medium low and 120MW as low. For a sampling interval of 30s , the estimated linear local models )(1-z A used in the NFN are shown in Table 1.Fig. 3 Membership function for local modelsTable 1 Local CARIMA models in neuro-fuzzy modelCascade control scheme is widely used to control the superheated steam temperature. Feed forward control, with the steam flow and the gas temperature as inputs, can be applied to provide a faster response to large variations in these two variables. In practice, the feed forward paths are activated only when there are significant changes in these variables. The control scheme also prevents the faster dynamics of the plant, i.e., the spray water valve and the water/steam mixing, from affecting the slower dynamics of the plant, i.e., the high temperature superheater. With the global nonlinear NFN model in Table 1, the proposed NFGPC scheme is shown in Fig.4.Fig. 4 NFGPC control of superheated steam temperature with feed-for-ward control.As a further illustration, the power plant is simulated using the NFN model given in Table 1,and is controlled respectively by the NFGPC, the conventional linear GPC controller, and the cascaded PI controller while the load changes from 160MW to 200MW.The conventional linear GPC controller is the local controller designed for the“medium”operating region. The results are shown in Fig.5,showing that, as expected, the best performance is obtained from the NFGPC as it is designed based on a more accurate process model. This is followed by the conventional linear GPC controller. The performance of the conventional cascade PI controller is the worst, indicating that it is unable to control satisfactory the superheated steam temperature under large load changes. This may be the reason for controlling the power plant manually when there are large load changes.Fig.5 comparison of the NFGPC, conventional linear GPC, and cascade PI controller.4. ConclusionsThe modeling and control of a 200 MW power plant using the neuro-fuzzy approach is presented in this paper. The NFN consists of five local CARIMA models.The out-put of the network is the interpolation of the local models using memberships given by the B-spline basis functions. The proposed NFGPC is similarly constructed, which is designed from the CARIMA models in the NFN. The NFGPC is most suitable for processes with smooth nonlinearity, such that its full operating range can be partitioned into several local linear operating regions. The proposed NFGPC therefore provides a useful alternative for controlling this class of nonlinear power plants, which are formerly difficult to be controlled using traditional methods.Part 4 为Part3译文：锅炉蒸汽温度模糊神经网络的广义预测控制Xiangjie LIU, Jizhen LIU, Ping GUAN摘要：发电厂是非线性和不确定性的复杂系统。

蒸汽发生器传热管的凹痕尺寸测量

蒸汽发生器传热管的凹痕尺寸测量
罗炜;邓道勇;张立殷;代勤龙;程怒涛
【期刊名称】《无损检测》
【年(卷),期】2024(46)1
【摘要】压水堆核岛主设备蒸汽发生器传热管在制造安装过程中易产生凹痕,要对凹痕进行残余应力分析、壁厚损失计算和运行寿命评估等工作,须获得凹痕的深度、长度和宽度等关键尺寸信息。

针对已安装的传热管系密集和蒸汽发生器壳体限制导致凹痕尺寸无法直接测量的问题,提出了一种利用视频内窥镜测量凹痕深度、同轴
线圈内穿涡流测量凹痕长度及阵列内穿涡流测量凹痕宽度的方法。

该方法为传热管凹痕的定量分析提供了依据,为后续核岛主设备热交换器传热管凹痕质量问题的处
理累积了宝贵经验。

【总页数】6页(P51-56)
【作者】罗炜;邓道勇;张立殷;代勤龙;程怒涛
【作者单位】东方电气(广州)重型机器有限公司
【正文语种】中文
【中图分类】TG115.28
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2.蒸汽发生器传热管导热系数的测量研究
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管凹痕预防技术4.蒸汽发生器抗振条与换热管间隙的测量5.蒸汽发生器传热管凹痕与缺陷的涡流检测信号分析
因版权原因，仅展示原文概要，查看原文内容请购买。

直流蒸汽发生器控制系统研究

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基于模糊逻辑控制的气体流量调节系统研究

基于模糊逻辑控制的气体流量调节系统研究气体流量调节系统是机械、化工、电子等行业中广泛使用的一种设备。

为了实现准确、可靠、高效的气体流量调节，研究并应用基于模糊逻辑控制的气体流量调节系统越来越受到关注。

模糊逻辑控制是一种基于模糊集合理论的智能控制方法。

相比于传统的PID控制方法，模糊逻辑控制能够更好地应对系统复杂多变的控制需求。

该方法的基本思想是将输入和输出量视为模糊集合，通过设计一套模糊逻辑推理规则来实现对输出量的控制。

在气体流量调节系统中，采用模糊逻辑控制的方法可以帮助调节系统在不同工况下实现流量控制的准确性和稳定性。

首先，利用温度、压力、流量等传感器获取实时的系统反馈信号，通过建立一系列的隶属函数来模糊化反馈信号，进而构建一个模糊控制器。

其次，通过模糊推理规则和模糊控制规则实现对气体流量设定值进行计算，进而控制执行机构的输出来达到控制流量的目的。

在模糊逻辑控制的过程中，需要对模糊数学的知识进行深入理解和运用。

例如，如何确定隶属度函数的参数、如何构建规则库、如何进行推理运算等。

此外，还需要对专业技术和实验室设备进行深入了解与使用，例如气体流量传感器、气体流量控制阀门等。

基于模糊逻辑控制的气体流量调节系统具有以下优势：1.适应性强：模糊逻辑控制方法能够在不同的工况下对系统进行自适应控制。

这使得气体流量调节系统在实际使用中具有更强的适应性和稳定性。

2.精度高：相比传统的PID控制方法，模糊逻辑控制方法能够更准确地控制气体流量，从而达到更高的控制精度。

3.应用广泛：气体流量调节系统是工业生产中不可或缺的设备，在化工、机械、电子等行业中都得到了广泛应用。

基于模糊逻辑控制的气体流量调节系统具有更好的控制效果和应用前景。

总之，基于模糊逻辑控制的气体流量调节系统是一种具有很高实用价值的技术方法。

无论在控制精度、适应性还是应用范围上，都能够带来很大的提升和优化。

我们相信，在未来的应用中，将有越来越多的气体流量调节系统采用这种方法来进行精确、可靠的控制。

锅炉蒸汽温度模糊神经网络的广义预测控制(毕业设计)

毕业设计（论文）外文翻译Neuro-fuzzy generalized predictive control ofboiler steam temperature锅炉蒸汽温度模糊神经网络的广义预测控制本科电气与信息学院自动化讲师学生姓名学历层次所在院系所学专业指导教师教师职称锅炉蒸汽温度模糊神经网络的广义预测控制摘要：发电厂是非线性和不确定的复杂系统。

现代电厂在运作上的，为确保高效率和高负荷的能力，可靠的控制过热蒸汽温度是必要的。

本文提出了一类在非线性广义预测控制器的基础上的模糊神经网络（ nfgpc ）。

所提出的非线性控制器适用于控制一台200 MW电厂的过热蒸汽温度。

从实验的移植和仿真移植上获得比传统的控制器好得多的性能。

关键词：模糊神经网络;广义预测控制;过热蒸汽温度。

1 引言这种持续不断的电厂和电力站复杂系统的特点是非线性、不确定性和负载扰动。

蒸汽发电的过程中锅炉-汽轮机温度过热是一个重要的过程，蒸汽加热后，进入涡轮驱动发电机。

控制过热蒸汽温度不仅是在技术上具有挑战性，但在经济上也是十分重要的。

图 1锅炉过热器和蒸汽生成过程。

从图1看出，产生的蒸汽从锅炉汽包通过低温过热器后进入辐射型屏。

水变成喷涂的蒸汽，以控制过热蒸汽的温度。

适当的控制电厂过热蒸汽温度是极其重要的，可以确保整体效率和安全性。

蒸汽温度太高是不可取的地方，因为过热它可损害和高压力汽轮机，太低也不行，因为它会降低电厂效率的。

减少温度波动内过热也是重要的，因为它有助于减少在单位内机械应力造成的微裂纹，延长单位秩序寿命，并减少维修成本。

作为GPC的推导应该尽量减少这些波动，它是众多的控制器是最适合实现这一目标的。

多变量多步自适应调节已适用于控制过热蒸汽温度在150吨/ h锅炉，提出了广义预测控制以控制蒸汽温度。

非线性长程预测控制器基于神经网络发展是以控制主蒸汽温度和压力。

控制主蒸汽压力和温度的基础上，非线性模型的构成是非线性静力常数和非线性动力学。

神经网络自学习模型控制器在直流蒸汽发生器控制中的应用

后趋于稳定。
本文采用二维模糊控制器，以误差和误差的变
化为输入变量，以控制量的变化为输出变量。描述
输入变量Ｅ的语言值的模糊词集为：Ｅ＝｛大，中，小，零，零，负负负负正正小，中，正
０概
述
发生很大变化，干点是变化的，过热蒸汽压力变蒸在
化很小时，热蒸汽温度变化很大，过这对于螺旋盘管
热疲劳分析是相当重要的。
在国内，由于新型船用核动力装置拟采用直流
蒸汽发生器，因此对直流蒸汽发生器的研究和探讨
描述输出变量Ｕ和输入变化Ｅ的模糊词集Ｃ
为：
化为蒸汽的比例也降低，因此二回路蒸汽压力又慢
慢降低。但一回路载热剂温度扰动相对于负荷与给水扰动来说，二回路蒸汽压力的变化的影响要小。对
直流蒸发器的各段长度均发生变化，受干扰所
也在逐渐展开。根据新型船用核动力装置的运行方案的要求，流蒸汽发生器过热蒸汽的压力必须控直制在规定范围之内，且船用核动力装置变负荷特而性，致产生蒸汽量的变化，导如何保证要求的干度和蒸汽压力的稳定，必须进行蒸汽压力控制策略研也究。由于直流蒸汽发生器水容积小，蓄热能力小，因

微电网混合储能系统控制策略研究及其应用

微电网混合储能系统控制策略研究及其应用摘要：微电网是一种新型的电力系统，能够有效地利用分布式可再生能源，提高电能质量和供电可靠性。

混合储能系统是微电网中的重要组成部分，能够平衡微电网中的功率波动，提高系统的稳定性和经济性。

本文综述了目前微电网混合储能系统的控制策略，分析了其优缺点，指出了面临的挑战和可能的解决方案。

同时，本文介绍了几个典型的微电网混合储能系统的实际应用案例，总结了应用中遇到的问题和解决方案。

最后，本文对微电网混合储能系统的发展前景进行了展望。

关键词：微电网；混合储能；控制策略；应用一、微电网混合储能系统的控制策略（一）现有的控制策略概述微电网混合储能系统通常由不同类型的储能装置组成，如蓄电池、超级电容器、飞轮等。

不同类型的储能装置具有不同的特性，如能量密度、功率密度、寿命、效率等。

因此，如何有效地协调和控制各种储能装置，使其发挥各自的优势，是微电网混合储能系统的关键问题。

目前，微电网混合储能系统的控制策略主要有以下几种：1.基于滤波器的控制策略利用滤波器将微电网中的功率分解为不同频率成分，然后根据不同类型的储能装置的特性，将不同频率成分分配给相应的储能装置。

例如，将低频成分分配给蓄电池，将高频成分分配给超级电容器。

这种控制策略简单易实现，但是需要考虑滤波器的参数设计和调节。

2.基于优化算法的控制策略利用优化算法来求解微电网混合储能系统的最优功率分配问题，以达到某种目标函数的最优值。

例如，最小化运行成本、最大化寿命、最小化损耗等。

这种控制策略可以考虑多种约束条件和目标函数，但是需要较高的计算复杂度和实时性。

3.基于模糊逻辑或神经网络的控制策略利用模糊逻辑或神经网络来建立微电网混合储能系统的非线性模型，并根据模型输出来调节各种储能装置的功率。

这种控制策略可以适应复杂和不确定的环境，但是需要较多的训练数据和学习过程。

（二）控制策略的优点和缺点表1列出了上述三种控制策略的优点和缺点。

（三）面临的挑战和可能的解决方案微电网混合储能系统的控制策略还面临着以下几个方面的挑战：1.微电网混合储能系统的建模问题微电网混合储能系统涉及多种储能装置和多种运行模式，其系统模型具有高度的非线性、时变性和不确定性，难以用传统的数学方法进行精确建模。

基于BP神经网络整定的蒸汽发生器水位PID控制及仿真研究

ｆｒｔｅｗａｅｅｅｎｔｅｎｃｅｒｓｅｍｅｅａｏｏｈｔｒｌｖｌｈｕｌａｔａｇｎｒｔｒｉ
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维普资讯
船海工程
文蕈编号１７ —９３２０）５０７ —４６１７５（０６０ —０８０
２００６年第５期（总第１４）７期
基于ＢＰ神经网络整定的蒸汽发生器水位ＰＤ控制及仿真研究Ｉ
周期。
参考文献
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２０ —１１ ห้องสมุดไป่ตู้０５１－８

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ｏｎｅｔｒｕｈｓｅｍｅｅａｏｆｏｃ — ｈｏｇｔａｇｎｒｔｒ
ＸＩＧｕｏｑｉＡ — ｎｇ。ＩＭｉｇ。ＡｎＺＨＡＮＧｅＷｉ
（ｌｇｆＡｕｏａｉｎ，ＨａｂｎＥｎｎｅｉｇＵｎｖｒｉｙ，Ｃｏｌｅｏｔｍｔｅｏｒｉｇｉｅｒｎｉｅｓｔ
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中图分类号：１３ＴＩ．６文献标志码：Ａ文章编号：２８０ｌ（０００ — ２６００５ — ９８２ｌ）３０６ — ６
Ｐｒｓｕｅｃｏｄｎａｅｏｒｌｂｓｄｏｕｚｅｒｌｎｅｗｏｋｅｓｒｏｒｉｔｄｃｎｔｏａｅｎｆｚｙｎｕａｔｒ
ｓｔｍｆＯＴＳＧ．ｙｓｅｏ
扰动的动态过程中出口蒸汽的压力难于控制的特点，入了协调控制思想，蒸汽发生器出口蒸汽流量引将与给水流量引入到控制系统，模糊神经网络应用到直流蒸汽发生器出口压力的控制中，线调整神经将在网络的权值，在此基础上设计了基于模糊神经网络的直流蒸汽发生器压力协调控制器。仿真结果表明，该控制较ＰＤ控制具有更小的超调量和调整时间，实际控制系统设计具有一定的参考价值。Ｉ对关键词：直流蒸汽发生器；糊控制；经网络模神
ＨａｒｉｆＨｅｌｒＪａｇＰｒｖ５０１．Ｃｈｎ）ｂｎｏｉｌｉｎｏ．】００ｏｇｉａ
ＡｂｔａｔＡｃｏｒｉｇｔｈｅｃｒｃｅｉｔｃｈｅｓａｌｗａｅｏｌｍｅａｄｔｅｗａａａｓｒｃ：ｅｄｎｏｔｈａａｔｒｓｉｓｏｆｔｍｌｔｒｖｕｎｈｋｅｃｐ — ｂｌｔｆｓｖｉｅｔｎｃｒａｎａｄｎｏ —ｉａｆｔｅｍａｈｍａｉｓｍｏｅ，ａｔｉｒｉｉｙｏａｎｇｈａ，ｕｅｔｉｎｎｌｎｅｒｏｈｔｅｔｃｄＩｎｄｉｓｈａｄ
基于模糊神经网络的直流蒸汽发生器压力协调控制
夏国清，明，伟艾张
（尔滨＿程大学自动化学院，哈丁 ‘ 黑龙江哈尔滨１００）０１５
摘要：对直流蒸汽发生器水容积小，热能力小，学模型具有不确定性和非线性，负荷变化或受到针蓄数在
ａｄｓｏｔｒｓｅｄｉｅＴｈｉｆｏｒｏｌｒｉｏｍｅｇｕｄｎｃｏｓｇｉｏｒｌｎｈｒｅｔａｙｔｍ．ｓｅｆｔｃｕｄｐｏｖｄｅｓｉａｅｆｒｄｅｉｎｎｇｃｎｔｏ
ｓｅｍｎｅａｏ（ｔａｇｅｒｔｒＯＴＳＧ）ｗａｗｏｒｅｉｔｅｏｒｓｔｍ，ｆｚｙｅｒＩｔｒｓｋｄｎｈｃｎｔｏ１ｙｓｅｕｚｎｕａｎｅｗｏｋ
（ＮＮ）ｂｓｄｏｔｎａｄｓａｐｉｄｔｒｓｕｅｃｎｒｌｓｓｅｏＦａｅｎｓａｄｒｓｗａｐｌｏｐｅｓｒｏｔｏｙｔｍｆＯＴＳ，ｊｉｔｎ — ｅＧｏｎｕ
ｍｅａｆＦＮＮｓａｊｓｅｎｌｅＰｅｓｒｏｒｉａｅｏｔｏａｅｎＦＮＮｆｒｌｏｗａｄｔｄｏｉ．ｒｓｕｅｃｏｄｎｔｄｃｎｒｌｂｓｄｏｕｎｏ
０ＴＳＧａｄｓｇｎｄＳｉｕｌｔｏｅｕｌｓｓｏｔｔｔｅｃｔｏｌｒｈｓｓａｌｒｏｖｒｈｔｗｓｅｉｅ．ｍａｉｎｒｓｔｈｗｈａｈｏｎｒｌｅａｍｌｅｅｓｏｏ
第３卷第３期Ｏ
２１Ｏ０年９月
核科学与工程
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Ｖｏ．ＯＮＯ３Ｉ３．
Ｓｅ．２０ｐＯ１