蒸汽动力主锅炉燃烧控制系统设计及应用

船舶主锅炉运行时负荷变化频繁,且变化幅度大。因此,稳定地,控制主蒸汽压力,防止锅炉安全阀启跳尤为重要,这就对燃烧控制系统的快速性提出了很高的要求。本文介绍了适用于大负荷扰动的锅炉燃烧控制系统。     

基于模糊神经网络的生物质气化炉的智能控制

利用神经网络理论与模糊理论融合而成的模糊神经网络,对具有非线性、非最小相位特征、大时滞以及负荷干扰特点的生物质气化炉气化过程进行了研究,设计了生物质气化炉炉温及一次进风量的智能控制系统.控制对象分别为气化炉气温及烟气含氧量,调节对象分别为生物质给料量与一次进风量,所建立的模糊神经网络具有五层拓扑结构,输入为给定值与实测值的误差及误差变化率,输出为PID参数变化量.仿真实验表明:该控制系统与传统的模糊控制系统相比具有更好的控制效果.     

CMAC逆模型用于电站负荷协调控制的研究

在常规PID负荷协调控制回路中加入CMAC神经网络模型。利用神经网络的非线性映射能力，能很好地解决负荷协调控制对象的动态特性具有非线性、时变性、参数可变等问题。仿真对比试验表明：负荷协调控制系统引入CMAC逆模型后，系统的跟踪速度加快了大约1个周期，调节精度提高。CMAC逆模控制器有较好的适应性、鲁棒性。图6表1参9     

单元机组协调系统的神经网络解耦控制仿真研究

神经网络控制是一种新颖的智能控制方法。应用神经网络技术,对难以精确建模的复杂非线性对象进行神经网络模型辨识作为控制器,提高了响应的快速性和准确性,可满足工业过程所提出的安全性、可靠性与易实现性的要求。大型火力发电单元机组协调控制系统是一个相对复杂的多变量控制系统,控制对象具有大时滞、时变、非线性、强耦合的特点,传统的PID控制算法很难实现对过程参数的良好跟踪和理想的控制效果。针对单元机组协调控制系统的特点将神经网络解耦控制应用于单元机组协调控制系统中,仿真实验表明,神经网络解耦控制具有较强的适应性和较高的控制精度,提高了负荷的响应速率,控制效果优于传统的PID控制算法。     

电站甩负荷情况下动力系统的协调控制策略研究

针对电站甩负荷时,主蒸汽压力易产生超压,且传统PID控制器无法快速有效地控制主蒸汽压力的问题,设计了燃烧系统和主机回汽协调控制策略以及小脑神经网络(Cerebellar Model Articulation Controller,CMAC)与PID并行的控制器,并在Simulink平台上进行了电站甩30%和100%负荷时系统动态特性仿真。结果表明:所设计的控制策略和控制器能够使主蒸汽压力控制系统较快地对电站的大负荷扰动作出响应,取得较好的控制效果,保障动力系统的稳定。     

直冷式空冷机组燃料BTU实时校正方法研究

煤质的频繁变化严重影响着直冷式空冷机组的特性和效率。火电厂来煤不稳定且实际煤种发热量很难实时准确的得到,给机组协调控制系统的设计带来很大困难。对此,针对直冷式空冷机组研究了一种基于模型的燃料BTU校正方法,用机组负荷实际值与机组模型负荷预测值的比值来反映实际煤种发热量与设计煤种发热量的关系,实时校正进入锅炉的燃料量,消除入炉煤质变化对机组协调控制系统的影响。经仿真实验和实际数据验证:在煤质频繁波动情况下协调控制系统控制品质良好,煤质稳定情况下不会对协调控制产生影响;得到的BTU校正系数不受模型输入信号的影响,能实时准确反映实际煤发热量的变化。该方法可行且有效,可适应现场煤质频繁变化的情况。     

超临界直流锅炉变工况实时运行中间点温度控制研究

以超临界直流锅炉为研究对象,分析了给水量和燃料量与锅炉中间点温度的关系,建立了中间点温度非线性离散模型,并进行控制系统设计,最后将该控制方法应用于某600 MW超临界直流机组,通过实际数据仿真结果和分析,证明了该控制系统在适应变工况运行的同时,能够实现中间点温度稳定的控制目的,能实时响应负荷变化,保证电力系统安全经济运行。     

槽式光热发电典型热工过程控制系统设计

针对槽式光热发电系统,结合火电厂控制系统设计经验,提出了包括机组负荷-供能控制系统、蒸汽发生器水位控制系统和过热蒸汽温度控制系统的槽式光热发电典型热工过程控制系统的设计方案。为了解决机组负荷和供能之间的耦合关系,提出了光热供能跟随汽轮机、汽轮机跟随光热供能、以光热供能跟随为基础的协调控制以及以汽轮机跟随为基础的协调控制等4种负荷-供能控制方案;为了降低蒸汽发生器水位系统中"虚假水位"现象对控制性能的影响,提出了蒸汽发生器三冲量串级汽包水位控制方案;为了补偿过热汽温大惯性、大时延的特性,提出了过热蒸汽温度内外环控制方案。该研究可以为槽式光热发电控制系统设计提供参考。     

采用神经网络改善式循环柴油机的供氧控制

在氧气反馈调节的基础上,不依赖系统模型,借助神经网络的构成前馈控制器,以反馈输出引导网络权值及输出的调整,使网络逐步学成前馈补偿功能,并最在控制中占据主导地位,实现对负荷扰动的补偿。仿真结果表明,采用这一复合控制系统能有效地改善氧气控制的动态特性。     

神经网络自适应PID控制在柴油机齿条位置控制中的应用

提出了基于BP神经网络的PID控制算法在柴油机齿条位置控制系统中的应用，把传统的PID控制和BP(误差反向传播)神经网络控制相结合，采用“先离线学习，后在线控制”的思想实现了齿条位置闭环的自适应控制。初步实验结果表明，该控制算法能满足齿条位移控制的性能指标，且其稳态特性较好，克服静摩擦的效果令人满意。     

An adaptive neuro-control system of synchronous generator for powersystem stabilization

This paper proposes a nonlinear adaptive generator control system using neural networks, called an adaptive neuro-control system (ANCS). This system generates supplementary control signals to conventional controllers and works adaptively in response to changes in operating conditions and network configuration. Through digital time simulations for a one-machine infinite bus test power system, the control performance of the ANCS and advanced controllers such as a linear optimal regulator and a self-tuning regulator is evaluated from the viewpoint of stability enhancement. As a result, the proposed ANCS using neural networks with nonlinear characteristics improves system damping more effectively and more adaptively than the other two controllers designed for the linearized model of the power system     

基于无源化方法的非线性系统自适应控制

研究了一种非线性系统的动态神经网络控制方案，利用无源化方法证明了其自适应控制系统的稳定性，并得到了动态神经网络系统的学习算法，仿真实验验证了本方法的有效性。     

新型恒压供水系统

针对地区城市水厂供水管网普遍存在铺设复杂、传输距离远、控制对象具有大滞后、强耦合、非线性、参数时变等特点,综合考虑目前供水系统注重管网压力而轻视水库水位等问题。文中提出基于智能神经网络的可调整修正因子模糊PID控制算法进行双闭环控制,使得供水系统不仅能按照模糊控制规则对不同供水工程进行调节,并且能够实时调节PID参数,使系统输出稳定。     

Enhancement of transient stability of an industrial cogeneration system with superconducting magnetic energy storage unit

This paper has developed the coordination of load shedding scheme and superconducting magnetic energy storage (SMES) unit to enhance the transient stability of a large industry cogeneration facility. The load-shedding scheme and the tie line tripping strategy by using the frequency relays have been designed to prevent the power system from collapse when an external fault of utility power system occurs. An actual external fault case and a simulated internal fault case have been selected to verify the accuracy of the load shedding scheme by executing the transient stability analysis. To improve the frequency and voltage responses, an SMES unit with various control modes has been installed in the cogeneration system. The sensitivity analysis of the SMES unit with different parameters is applied to achieve better system responses. Besides, an SMES unit with active power deviation as feedback signal is also considered to improve the electric power fluctuation of the study plant with rolling mills. It is found that the SMES system will enhance the electric power quality and minimize the economic losses of the cogeneration facility due to unnecessary load shedding.     

有源电力滤波器神经终端滑模控制

以电力电子装备为接口的高渗透率可再生能源并网已成为未来配电网的显著特性。可再生能源具有随机性和间歇性,作为其并网接口的电力电子装备也会导致电能质量恶化等问题。为提高电能质量,该文提出一种有源电力滤波器神经终端滑模控制方法。首先,结合分数阶思想和滑模控制理论设计一种分数阶终端滑模控制器,以保证误差有限时间收敛,并引入边界层技术降低抖振。然后,利用自组织模糊神经网络构造一种无模型控制方案以更好地应对各种不确定因素。所设计的自组织模糊神经网络控制器用于学习分数阶终端滑模控制器,不仅从根源上解决抖振问题,而且可继承原控制器的有限时间收敛性能,并满足李雅普诺夫理论框架下的稳定控制性能。仿真与实验结果表明：所提出的控制方法能有效解决可再生能源发电系统中的谐波问题。     

Stabilizing control of a high-order generator model by adaptive feedback linearization

We present an adaptive feedback linearizing control scheme for excitation control and power system stabilization. The power system is a synchronous generator which is first modeled as an input-output nonlinear discrete-time system approximated by two neural networks. Then, the controller is synthesized to adaptively compute an appropriate feedback linearizing control law at each sampling instant using estimates provided by the neural system model. This formulation simplifies the problem to that of designing a linear pole-placement controller which is itself not a neural network but is adaptive in the sense that the neural estimator adapts itself online. Additionally, the requirement for exact knowledge of the system dynamics, full state measurement, as well as other difficulties associated with feedback linearizing control for power systems are avoided in this approach. Simulations demonstrate its application to a high-order single-machine system under various conditions.     

Energy management in photovoltaic battery hybrid systems: A novel type-2 fuzzy control

In this paper, a new robust fuzzy control approach is presented to power management in the photovoltaic (PV)-battery hybrid system. The stability and robustness of the developed control scheme is analyzed through the Lyapunov technique. The dynamics of the converters, battery and PV system are assumed to be fully unknown. Specifically, the uncertainties are online estimated by the adaptive interval type-2 fuzzy neural networks. It is shown that the implemented control technique results are in good performance in the presence of time-varying radiation, changes of temperature and output load variation in comparison with the other well-known control techniques.     

220kV电网解环方式对稳控系统的影响及分析

为设计出适用于合解环运行方式灵活、网架结构薄弱的220kV区域电网的安全稳定控制系统,以贵州省奢香区域电网为例,采用BPA程序建立了交直流系统机电暂态仿真模型,针对2013年丰大、枯大合环、解环多种运行方式进行潮流稳定计算分析。结果表明,合解环运行方式下系统潮流分布、稳定问题和稳控措施差别较大,且不同解环方式的判别方法和切机切负荷对象也不同。为准确判断系统运行方式、实施正确的稳控措施,稳控系统宜采用集中式配置方案。     

An adaptive power system stabilizer based on recurrent neuralnetworks

Application of recurrent, neural networks in the design of an adaptive power system stabilizer (PSS) is presented in this paper. The architecture of the proposed adaptive PSS has two recurrent neural networks. One functions as a tracker to learn the dynamic characteristics of the power plant and the second one functions as a controller to damp the oscillations caused by the disturbances. In the proposed approach, the weights of the neural networks are updated on-line. Therefore, any new information available during actual control of the plant is considered. Simulation studies show that the artificial neural network (ANN) based PSS can provide very good damping over a wide range of operating conditions     

Improved artificial neural network method for predicting photovoltaic output performance

To ensure the safety and stability of power grids with photovoltaic (PV) generation integration, it is necessary to predict the output performance of PV modules under varying operating conditions. In this paper, an improved artificial neural network (ANN) method is proposed to predict the electrical characteristics of a PV module by combining several neural networks under different environmental conditions. To study the dependence of the output performance on the solar irradiance and temperature, the proposed neural network model is composed of four neural networks, it called multi- neural network (MANN). Each neural network consists of three layers, in which the input is solar radiation, and the module temperature and output are five physical parameters of the single diode model. The experimental data were divided into four groups and used for training the neural networks. The electrical properties of PV modules, including I–V curves, P– V curves, and normalized root mean square error, were obtained and discussed. The effectiveness and accuracy of this method is verified by the experimental data for different types of PV modules. Compared with the traditional single-ANN (SANN) method, the proposed method shows better accuracy under different operating conditions.