Predictive control of SOFC based on a GA-RBF neural network model

Transients in a load have a significant impact on the performance and durability of a solid oxide fuel cell (SOFC) system. One of the main reasons is that the fuel utilization changes drastically due to the load change. Therefore, in order to guarantee the fuel utilization to operate within a safe range, a nonlinear model predictive control (MPC) method is proposed to control the stack terminal voltage as a proper constant in this paper. The nonlinear predictive controller is based on an improved radial basis function (RBF) neural network identification model. During the process of modeling, the genetic algorithm (GA) is used to optimize the parameters of RBF neural networks. And then a nonlinear predictive control algorithm is applied to track the voltage of the SOFC. Compared with the constant fuel utilization control method, the simulation results show that the nonlinear predictive control algorithm based on the GA-RBF model performs much better.     

基于差分进化算法的燃气轮机转速模糊PID复合控制

为了改善电站燃气轮机转速控制系统的调节品质,采用模糊PID(比例积分微分调节器)复合控制,通过差分进化算法对模糊控制器参数进行寻优,基于电力系统高级仿真支撑平台(JSSC)对该方法进行了验证。试验结果表明:模糊PID复合控制无论在升速过程还是甩负荷等突变工况时,不管在超调量、过渡过程时间等动态性能上还是静态偏差等方面,转速控制结果均明显优于单纯的PID控制;当转速特性发生变化时,该控制方法表现出良好的鲁棒性。     

Neuro-fuzzy controller of low head hydropower plants usingadaptive-network based fuzzy inference system

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order nonlinear hydrogenerator model     

Dynamic behavior of a stand-alone hybrid power generation system of wind turbine,microturbine, solar array and battery storage

This paper presents dynamic behavior and simulation results in a stand-alone hybrid power generation system of wind turbine, microturbine, solar array and battery storage. The hybrid system consists of a 195 kW wind turbine, an 85 kW solar array; a 230 kW microturbine and a 2.14 kAh lead acid battery pack optimized based on economic analysis using genetic algorithm (GA). At first, a developed Lyapunov model reference adaptive feedback linearization method accompanied by an indirect space vector control is applied for extraction of maximum energy from a variable speed wind power generation system. Then, a fuzzy logic controller is designed for the mentioned purpose and its performance is compared with the proposed adaptive controller. For meeting more load demands, the solar array is integrated with the wind turbine. In addition, the microturbine and the battery storage are combined with the wind and solar power generation system as a backup to satisfy the load demand under all conditions.A supervisory controller is designed in order to manage energy between the maximum energy captured from the wind turbine/solar arrays, and consumed energies of the load, dump load, battery state of charge (SOC), and generated energy by the microturbine. Dynamic modeling and simulation are accomplished using MATLAB Simulink? 7.2.     

提高燃气轮机联合循环电站性能的优化方法

天然气联合循环机组因启停快、运行灵活性好、热效率高、排放清洁、建造周期短而倍受中国市场青睐.围绕如何通过燃气轮机进气系统、主机参数匹配、汽轮机冷端等参数优化来提高联合循环热效率是国内外学者研究的热点.以配有目前市场上最高性能等级燃气轮机的联合循环为研究对象,建立了以提高联合循环热效率为目标的热力计算和分析模型,提出了各段蒸汽压力及温度参数优化匹配方法,并进一步分析、讨论了燃料预热对联合循环热效率的影响.在综合考虑余热锅炉换热温差、汽轮机结构设计等制约因素下得到了一组蒸汽循环的优化参数配置.计算结果表明,相比直接沿用上一代蒸汽循环参数,使用该优化参数配置可大幅度提高联合循环效率,并且使用燃料预热可使循环性能得到进一步改善.     

Hierarchical fuzzy control of low-energy building systems

A hierarchical fuzzy supervisory controller is described that is capable of optimizing the operation of a low-energy building, which uses solar energy to heat and cool its interior spaces. The highest level fuzzy rules choose the most appropriate set of lower level rules according to the weather and occupancy information; the second level fuzzy rules determine an optimal energy profile and the overall modes of operation of the heating, ventilating and air-conditioning system (HVAC); the third level fuzzy rules select the mode of operation of specific equipment, and assign schedules to the local controllers so that the optimal energy profile can be achieved in the most efficient way. Computer simulation is used to compare the hierarchical fuzzy control scheme with a supervisory control scheme based on expert rules. The performance is evaluated by comparing the energy consumption and thermal comfort.     

燃气轮机调节方式对IGCC系统变工况性能的影响

为选择合理的燃气轮机调节方式,采用Thermoflex软件建立了200 MW级IGCC系统模型,从系统的角度出发比较研究了燃气轮机的调节方式对燃气顶循环系统、蒸汽底循环系统和整个IGCC系统变工况性能的影响.研究表明:与压气机可转导叶(IGV)不调相比,IGV可调时更有利于提高系统的变工况性能.等燃气透平初温(T3)调...     

基于模糊小脑模型的水轮机调速控制

水轮机调速器是控制水轮机输出机械功率的关键设备．是水轮发电机组控制的重要内容。针对非线性复杂时变水轮机调速器．研究了一种模糊小脑模型关节控制器,该智能控制器是一种模糊逻辑与小脑模型关节控制器的结合,集中了二者的优点和长处。分析了该控制器的结构、具体实现以及学习优化。仿真实验表明该智能控制器的实际效果良好。     

基于模糊的自校正PID控制在水电机组上的应用

针对水电机组具有非线性、时变、非最小相位且结构参数变化范围较大的特点，基于模糊集理论设计了一种自校正PID控制器．该控制器基于对控制误差、误差的变化和误差变化的变化的识别，通过模糊推理来实现其参数的实时优化．从而达到对水电机组不同工况下的自适应控制，算法实现简单．实时性强。在水电机组的调节系统仿真实验中．与采用常规PID控制相比，能更有效改善系统的动态性能．增强系统的鲁棒性。图7参7     

Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications

Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and bypass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications.     

Nonlinear design-point performance adaptation approaches and their comparisons for gas turbine applications

Accurate performance simulation and understanding of gas turbine engines is very useful for gas turbine manufacturers and users alike and such a simulation normally starts from its design point. When some of the engine component parameters for an existing engine are not available, they must be estimated in order that the performance analysis can be started. Therefore, the simulated design point performance of an engine may be slightly different from its actual performance. In this paper, two nonlinear gas turbine design-point performance adaptation approaches have been presented to best estimate the unknown component parameters and match available design point engine performance, one using a nonlinear matrix inverse adaptation method and the other using a Genetic Algorithm-based adaptation approach. The advantages and disadvantages of the two adaptation methods have been compared with each other. In the approaches, the component parameters may be compressor pressure ratios and efficiencies, turbine entry temperature, turbine efficiencies, engine mass flow rate, cooling flows, and by-pass ratio, etc. The engine performance parameters may be thrust and SFC for aero engines, shaft power, and thermal efficiency for industrial engines, gas path pressures, temperatures, etc. To select the most appropriate to-be-adapted component parameters, a sensitivity bar chart is used to analyze the sensitivity of all potential component parameters against the engine performance parameters. The two adaptation approaches have been applied to a model gas turbine engine. The application shows that the sensitivity bar chart is very useful in the selection of the to-be-adapted component parameters, and both adaptation approaches are able to produce good quality engine models at design point. The comparison of the two adaptation methods shows that the nonlinear matrix inverse method is faster and more accurate, while the genetic algorithm-based adaptation method is more robust but slower. Theoretically, both adaptation methods can be extended to other gas turbine engine performance modelling applications.     

一种基于燃气轮机实时模型的硬件在回路平台集成与验证

为了研究基于燃气轮机模型的先进控制算法和故障诊断技术,需要建立准确性和实时性更好的燃气轮机实时模型。采用基于模型的设计方法,在Simulink中建立燃气轮机分段线性化实时模型,通过代码生成技术和封装技术,将模型集成在南京航空航天大学航空发动机/燃气轮机全数字仿真平台和硬件在回路平台。在硬件在回路仿真试验中,燃气轮机实时模型与非线性模型的最大相对误差不超过3%;单位步长内,实时模型在数字电子控制器中计算平均耗时为0.126 ms,最大耗时为0.131 ms,精确性和实时性均满足控制系统的要求。     

T-S模糊控制器设计新方法及应用仿真

以具有非线性和非最小相位特性的水轮发电机调速系统为例．提出了T—S模糊控制器设计的新方法——基于PID控制器知识样本的T—S模糊控制器设计方法。将模糊控制器运用于水轮机控制。并对其进行仿真,把得到的模糊控制响应曲线与PID控制器进行比较,从调节时间、超调量、鲁棒性等方面可以看出,模糊控制器控制效果要优于PID控制。     

混流式水轮机调节系统的鲁棒控制策略研究

水轮机及其调节系统是水力发电系统中的重要组成部分,承担着能量转换的重要作用。水轮机运行于复杂工况下,往往表现出各种复杂的非线性行为,危害水力发电系统安全。基于刚性水击下混流式水轮机调节系统的数学模型,采用T-S模糊控制方法建立了水轮机调节系统的非线性T-S模糊模型,基于Lyapunov稳定性定理,提出了一种鲁棒控制策略,设计了相应的鲁棒控制器。仿真计算表明,所设计的鲁棒控制器能够极大地抑制水轮机调节系统的振荡,并在较短时间内将水轮机调节系统的运动控制到稳定运行状态,研究结果可为混流式水轮机调节系统的安全稳定运行控制提供参考依据。     

Performance analysis of open cycle gas turbines

In this study, the performance of ideal open cycle gas turbine system was examined based on its thermodynamic analysis. The effects of some parameters, such as compressor inlet temperature (CIT), pressure ratio (PR) and the turbine inlet temperature (TIT), on the performance parameters of open cycle gas turbine were discussed. The turbine net power output, the thermal efficiency and the fuel consumption of the turbine were taken as the performance parameters. The values of these parameters were calculated using some basic cycle equations and variables values of thermodynamic properties. Other variables such as lower heating value, combustion efficiency and isentropic efficiencies of compressor and turbine were assumed to be constant. The result showed that the net power output and the thermal efficiency increased by a decrease in the CIT and increase in the TIT and PR values. If it is aimed to have a high net power output and the thermal efficiency for the turbine, the CIT should be chosen as low as possible and the TIT should be chosen as high as possible. Copyright © 2008 John Wiley & Sons, Ltd.     

Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design

This paper presents a probabilistic multiobjective framework for optimal distributed energy resources (DERs) planning in the distribution electricity networks. The proposed model is from the distribution company (DISCO) viewpoint. The projected formulation is based on nonlinear programming (NLP) computation. The proposed design attempts to achieve a trade-off between minimizing the monetary cost and minimizing the emission of pollutants in presence of the electrical load as well as electricity market prices uncertainties. The monetary cost objective function consists of distributed generation (DG) investment and operation cost, payment toward loss compensation as well as payment for purchased power from the network. A hybrid fuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used for scenario based modeling of the electricity prices and a combined roulette-wheel/Monte-Carlo simulation (RW/MCS) model is used for generation of the load scenarios. The proposed planning model considers six different types of DERs including wind turbine, photovoltaic, fuel cell, micro turbine, gas turbine and diesel engine. In order to demonstrate the performance of the proposed methodology, it is applied to a primary distribution network and using a fuzzified decision making approach, the best compromised solution among the Pareto optimal solutions is found.     

Revealing the cost-efficiency trends of the design concepts of energy-intensive systems

Design models for the components of an energy system concept are used to extract the information relevant to the prediction of the cost and performance of the system. The information is directed towards seeking an optimal design point for the system and then redirected towards predicting its performance at off-design points. The design model of a heat exchanger is used as an example for information extraction. Few gas turbine power systems are considered for optimal design. The part-load operation of the optimized simple combined cycle is then computed. The methodology in more detail is referred to elsewhere.     

大型风电机组模型预测独立变桨控制器设计

针对恒转速运行时,大型风电机组承受的不平衡载荷问题,提出一种多输入多输出的风电机组模型预测(MPC)独立变桨控制策略.首先,建立风电机组旋转坐标系下的状态空间模型,经过坐标变换得到固定坐标系下的平均周期模型,分析表明模型在非对角存在无法忽视的耦合;然后,计算所需观测器和控制器的参数,进一步设计基于Kalman状态观测器...     

燃气动力装置性能参数的热经济性分析与决策

热经济性是表征在达到一定技术目的条件下,设备的能量利用经济性,从提高经济性的角度出发来对燃气动力装置循环性能参数的分析,发现了热效率不适宜作为选择性能参数的热经济性决策指标,应以装置的运行成本为决策指标才更为合理,因为它兼顾了设计和运行两方面的因素,以等压加热循环的燃 气动力装置为例,建立了热经济优化模型并得出了热经济性能参数,从而说明,寻求和采用热经济性能参数可考虑作为燃气动力装置设计决策的一种新方法。     

水平轴失速型风力机主动非线性控制

讨论了大型主动失速型风力机在额定工况以上时的主动非线性控制问题。直接利用所推导的仿射性非线性模型．采用微分几何精确线性化理论,实现恒速风力机全局精确线性化控制,给出了反馈控制算法,并对闭环系统进行了数字仿真。