An adaptive control scheme for speed control of diesel drivenpower-plants

The performance of an incremental Clarke-Gawthrop adaptive control scheme, suitable for a diesel-engine prime mover, is described. The controller uses a predictor that is derived from explicit estimates of the plant deadtime and time constants. Its performance under speed reference changes and load disturbances has been compared to that of a fixed, tuned proportional-integral (PI) controller. The algorithm is found to operate satisfactorily under different values of droop without any additional complexity of computation being incurred. However, the improvement in plant response due to the adaptive algorithm is somewhat reduced at high droops. The effective improvement due to adaptation is also seen to be reduced under `cold oil' conditions. However, even under such conditions, it is possible to obtain improved response as compared to the PI controller     

Dynamic stall control via adaptive blowing

An aerodynamic load control concept termed “adaptive blowing” was successfully tested on a NACA 0018 airfoil model at Reynolds numbers ranging from 1.5·10⁵ to 5·10⁵. The global objective was to eliminate lift oscillations typically encountered on wind turbine blade sections. Depending on the jet momentum flux, steady blowing from a control slot in the leading-edge region can be utilized to either enhance or reduce lift by suppressing or inducing boundary layer separation respectively. Furthermore, high momentum blowing effectively eliminated the dynamic stall vortex during deep dynamic stall conditions. Based on these previous findings, the present work explores the feasibility of controlling unsteady aerodynamic loads by dynamically varying the jet momentum flux to compensate for transient changes of the inflow. Various scenarios including high amplitude pitching, rapid freestream oscillations and combinations of both were investigated in a custom-built unsteady wind tunnel facility. An iterative control algorithm was implemented which successfully identified the momentum coefficient time profiles required to minimize the lift excursions. The combination of fully suppressing dynamic stall and dynamically adjusting the lift coefficient provided an unprecedented control authority, producing virtually constant phase averaged lift in all cases.     

Adaptive control of speed and equivalence ratio dynamics of adiesel driven power-plant

A method of modeling, involving approximation of dead-times, is presented for diesel driven power prime-movers. The resulting predictive adaptive control scheme is compared to a conventional PI scheme on the basis of common disturbances encountered by a practical prime-mover. It is found that the adaptive scheme achieves significant improvement despite the nonlinearities introduced by turbocharger operation. It is observed in general that adaptive schemes, such as the one described, affect the prime-mover dynamics in essentially two ways. First, the engine fuel-flow response is improved by better control of the actuator. Secondly, by indirectly controlling the exhaust energy of the plant, the turbocharger air-dynamics is made faster, leading to better dynamic fuel/air ratio. The overall effect is good response of the prime-mover speed notwithstanding the fact that the diesel prime-mover is a low inertia structure and that the system parameters suffer from various nonlinear variations     

A new control strategy for a stand-alone self-excited induction generator driven by a variable speed wind turbine

This paper presents a new control strategy of a stand-alone self-excited induction generator (SEIG) driven by a variable speed wind turbine. The proposed system consists of a three phase squirrel-cage induction machine connected to a wind turbine through a step-up gear box. A current controlled voltage source inverter (CC–VSI) with an electronic load controller (ELC) is connected in parallel with the main consumer load to the AC terminals of the induction machine. The proposed control strategy is based on fuzzy logic control principles which enhance the dynamic performance of the proposed system. Three fuzzy logic PI controllers and one hysteresis current controller (HCC) are used to extract the maximum available energy from the wind turbine as well as to regulate the generator terminal voltage simultaneously against wind speed and main load variations. However, in order to extract the maximum available energy from the turbine over a wide range of wind speeds, the captured energy is limited due to electrical constraints. Therefore the control strategy proposed three modes of control operation. The steady state characteristics of the proposed system are obtained and examined in order to design the required control parameters. The proposed system is modeled and simulated using Matlab/Simulink software program to examine the dynamic characteristics of the system with proposed control strategy. Dynamic simulation results demonstrate the effectiveness of the proposed control strategy.     

Concept risk assessment of a hydrogen driven high speed passenger ferry

A concept risk assessment of a hydrogen and fuel cell driven high speed passenger ferry has been performed. The study focused on fatality risk related to the hydrogen systems on the vessel, both during operation and while moored in harbour overnight. The main objective with the study was to evaluate whether the risk related to the hydrogen systems is equivalent to that of conventionally fuelled vessels and can be considered acceptable according to the requirements of the IGF-code (International Code of Safety for Ships Using Gases or Other Low-flashpoint Fuels). Since hydrogen behaves differently than other flammable gases, some adjustments to existing models and vulnerability criteria have been proposed. The conclusion of the study is that the estimated risk related to hydrogen systems is relatively low, and much lower than the expected acceptable risk tolerance level of 0.5–1.0 fatalities per 10⁹ passenger km. Furthermore, for the overnight mooring in harbour the estimated risks are well within acceptable limits. The work presented is part of a maritime case study performed within MoZEES, a Norwegian research centre for environmentally friendly technology and zero emission transport.     

Dynamic modeling and adaptive control of voltage in proton exchange membrane fuel cell using water management

Maintaining a constant voltage in polymer electrolyte membrane fuel cells (PEMFCs) has always attracted the attention of many researchers, and many articles have been published on this issue. Furthermore, water management in PEMFC has become an important challenge because it can improve cell efficiency and lifetime. This paper will develop a one‐dimensional dynamic model for a single PEMFC, which correlates changes in the cell voltage to changes in the cell current density and humidification rate. Subsequently, a recurrent neural network controller based on the approximation of nonlinear autoregressive moving average model is proposed. The controller manipulates the anode and the cathode water mole fractions in order to fix cell voltage and preserve cell water content within a satisfactory interval regardless of the varying cell current. The model and the controller are simulated in matlab /Simulink (Mathworks Inc., Natick, MA) software, and the results are compared with a PID controller from different viewpoints such as current disturbance and plant parameter variation. Copyright © 2011 John Wiley & Sons, Ltd.     

CTD-350燃气轮机转速的动态仿真

通过实验获得的数据初步得到了GTD-350燃气轮机各部件的特性曲线,据此对该型燃机进行仿真.本文把该型燃机简化为两个动态环节,应用小偏差线性化方法进行数学建模,应用MATLAB/simulink仿真软件进行数学仿真.     

Bayesian adaptive combination of short-term wind speed forecasts from neural network models

Short-term wind speed forecasting is of great importance for wind farm operations and the integration of wind energy into the power grid system. Adaptive and reliable methods and techniques of wind speed forecasts are urgently needed in view of the stochastic nature of wind resource varying from time to time and from site to site. This paper presents a robust two-step methodology for accurate wind speed forecasting based on Bayesian combination algorithm, and three neural network models, namely, adaptive linear element network (ADALINE), backpropagation (BP) network, and radial basis function (RBF) network. The hourly average wind speed data from two North Dakota sites are used to demonstrate the effectiveness of the proposed approach. The results indicate that, while the performances of the neural networks are not consistent in forecasting 1-h-ahead wind speed for the two sites or under different evaluation metrics, the Bayesian combination method can always provide adaptive, reliable and comparatively accurate forecast results. The proposed methodology provides a unified approach to tackle the challenging model selection issue in wind speed forecasting.     

Dynamical sliding mode power control of wind driven inductiongenerators

This paper concerns power regulation of variable-speed wind energy conversion systems. These systems have two regions of operation, depending on the tip speed ratio of the wind turbines. They are distinguished by a minimum phase behavior in one of these regions and a nonminimum phase one in the other. A sliding mode control strategy is proposed that assures stability in both regions of operation and imposes the ideally designed feedback control solution in spite of model uncertainties. Moreover, power regulation by the proposed sliding control in the minimum phase region is completely robust to wind disturbances and parameter uncertainties     

Dynamic simulation and experimental verification on absorption refrigeration driven by vehicle waste heat

Waste heat exhausted from vehicle engine has significant influences on the performances of absorption refrigeration driven by vehicle exhaust heat (VARS), especially for the transient response of VARS to the exhaust temperature and flow rate. Moreover, ambient temperature and flow rate of solution pump also affect its performances. Based on these, a dynamic mathematical model of VARS was proposed and established to observe its dynamic performances in this work. Further, the dynamic model was solved, and the simulation results were compared to the experimental data of a prototype in the same operating conditions. Finally, the transient response of VARS to exhaust temperature and flow rate, ambient temperature, and flow rate of solution pump were analyzed. The simulation results showed that the response time is about 2000 seconds when a disturbance was imposed. The research findings can supply some guidance for a reliable design, optimization, and control strategy for the actual application of the VARS.     

基于PID控制的变速恒频风力发电机组液压变桨距控制系统研究

文章分析了变桨距控制原理,对变速恒频风力发电机组各部分数学模型进行了分析与研究,在此基础上,建立了变速恒频风力发电机组PID液压变桨控制系统仿真模型,在matlab/simulink环境下对未加PID控制器与加PID控制器两种情况下的系统进行了仿真研究。仿真结果表明,PID控制器能改善风力机桨距控制效果,更好地捕获风能,稳定风力机功率输出。     

Environmental performance and economic analysis of all-variable speed chiller systems with load-based speed control

There are increasing views on implementing all-variable speed chiller plants in place of conventional constant speed plants. Supporters of these views claim that all-variable speed chiller systems can operate much more efficiently at part load in response to changes in building cooling load. This paper introduces load-based speed control for all-variable speed plants to optimize their environmental performance. Thermodynamic-behaviour chiller system models were developed to perform environmental assessment (in terms of annual electricity and water consumption) for typical constant speed and all-variable speed chiller systems operating for the cooling load profile of a local office building. Operating cost differences between the two systems were calculated and compared in an economic analysis. Applying load-based speed control to the variable speed chiller plant can decrease the annual total electricity use by 19.7% and annual water use by 15.9% relative to the corresponding constant speed plant. The significance of this study is to provide more insights into how to make chiller systems more sustainable.     

Dynamic models of wind farms with fixed speed wind turbines

The increasing wind power penetration on power systems requires the development of adequate wind farms models for representing the dynamic behaviour of wind farms on power systems. The behaviour of a wind farm can be represented by a detailed model including the modelling of all wind turbines and the wind farm electrical network. But this detailed model presents a high order model if a wind farm with high number of wind turbines is modelled and therefore the simulation time is long. The development of equivalent wind farm models enables the model order and the computation time to be reduced when the impact of wind farms on power systems is studied. In this paper, equivalent models of wind farms with fixed speed wind turbines are proposed by aggregating wind turbines into an equivalent wind turbine that operates on an equivalent wind farm electrical network. Two equivalent wind turbines have been developed: one for aggregated wind turbines with similar winds, and another for aggregated wind turbines under any incoming wind, even with different incoming winds.The proposed equivalent models provide high accuracy for representing the dynamic response of wind farm on power system simulations with an important reduction of model order and simulation time compare to that of the complete wind farm modelled by the detailed model.     

A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction

In this paper, an adaptive control scheme for maximum power point tracking of stand-alone PMSG wind turbine systems (WTS) is presented. A novel procedure to estimate the wind speed is derived. To achieve this, a neural network identifier (NNI) is designed in order to approximate the mechanical torque of the WTS. With this information, the wind speed is calculated based on the optimal mechanical torque point. The NNI approximates in real-time the mechanical torque signal and it does not need off-line training to get its optimal parameter values. In this way, it can really approximates any mechanical torque value with good accuracy. In order to regulate the rotor speed to the optimal speed value, a block-backstepping controller is derived. Uniform asymptotic stability of the tracking error origin is proved using Lyapunov arguments. Numerical simulations and comparisons with a standard passivity based controller are made in order to show the good performance of the proposed adaptive scheme.     

A new composite adaptive speed controller for induction motor basedon feedback linearization

A new adaptive control technique is proposed to control the speed of the induction motor in this paper. First, the rotor flux is estimated with the simplified rotor flux observer on the rotor reference frame and the feedback linearization theory is used to decouple the rotor speed and the flux amplitude. Then, a new composite adaptive control algorithm based on an integral cost function is designed to control the speed of the induction motor. The overall speed control system is verified to be stable and robust to the parameter variations and external disturbances. Experimental results are provided to demonstrate the effectiveness of the presented approach. The good speed tracking and load regulating responses can be obtained by the proposed controller     

Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)

Wind energy has become a major competitor of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, wind with reasonable speed is not adequately sustainable everywhere to build an economical wind farm. The potential site has to be thoroughly investigated at least with respect to wind speed profile and air density. Wind speed increases with height, thus an increase of the height of turbine rotor leads to more generated power. Therefore, it is imperative to have a precise knowledge of wind speed profiles in order to assess the potential for a wind farm site. This paper proposes a clustering algorithm based neuro-fuzzy method to find wind speed profile up to height of 100 m based on knowledge of wind speed at heights 10, 20, 30, 40 m. The model estimated wind speed at 40 m based on measured data at 10, 20, and 30 m has 3% mean absolute percent error when compared with measured wind speed at height 40 m. This close agreement between estimated and measured wind speed at 40 m indicates the viability of the proposed method. The comparison with the 1/7th law and experimental wind shear method further proofs the suitability of the proposed method for generating wind speed profile based on knowledge of wind speed at lower heights.     

Dynamic control of wind turbines

The paper presents an intelligent wind turbine control system based on models integrating the following three approaches: data mining, model predictive control, and evolutionary computation. To enhance the control strategy of the intelligent system, a multi-objective model is proposed. The model involves five different objectives with different weights controlling the wind turbine performance. These weights are adjusted in response to the variable wind conditions and operational requirements. Three control factors, wind speed, turbulence intensity, and electricity demand are considered in eight computational scenarios. The performance of each scenario is illustrated with numerical results.     

Cascade fuzzy control for gas engine driven heat pump

In addition to absorption chillers, today’s gas cooling technology includes gas engine driven heat pump systems (GEHP) in a range of capacities and temperature capacities suitable for most commercial air conditioning and refrigeration applications. Much is expected from GEHPs as a product that would help satisfy the air conditioning system demand from medium and small sized buildings, restrict electric power demand peaks in summer and save energy in general. This article describes a kind of control strategy for a GEHP, a cascade fuzzy control. GEHPs have large and varying time constants and their dynamic modeling cannot be easily achieved. A cascade control strategy is effective for systems that have large time constants and disturbances, and a fuzzy control strategy is fit for a system that lacks an accurate model. This cascade fuzzy control structure brings together the best merits of fuzzy control and cascade control structures. The performance of the cascade fuzzy control is compared to that of a cascade PI (proportional and integral) control strategy, and it is shown by example that the cascade fuzzy control strategy gives a better performance, reduced reaction time and smaller overshoot temperature.     

Multivariable adaptive control for a thermal generator

A digital control system using the optimal control has been developed for a thermal power generator. In order to increase the generator control performance for improving the power system stability, this system supplies the excitation system directly with control signals and provides the governor system with control signals and supplemental control signals for a conventional governor. The system utilizes adaptive control for changes in generator nonlinear characteristics, as well as in power system characteristics. The principles and application of this system are described and results of simulation tests using a 1000 MVA thermal power plant model on a high-reliability AC/DC electric power system simulator are presented. The results verify the performance of this system, as well as the applicability of the system to actual plants