Neural-net based coordinated stabilizing control for the exciterand governor loops of low head hydropower plants

This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbines are performed and the comparisons of conventional excitation-governor state-space optimal control and neural-net based control are presented. Results obtained on the nonlinear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers     

Risk assessment of river-type hydropower plants using fuzzy logic approach

In this paper, a fuzzy rating tool was developed for river-type hydropower plant projects, and risk assessment and expert judgments were utilized instead of probabilistic reasoning. The methodology is a multi-criteria decision analysis, which provides a flexible and easily understood way to analyze project risks. The external risks, which are partly under the control of companies, were considered in the model. A total of eleven classes of risk factors were determined based on the expert interviews, field studies and literature review as follows: site geology, land use, environmental issues, grid connection, social acceptance, macroeconomic, natural hazards, change of laws and regulations, terrorism, access to infrastructure and revenue. The relative importance of risk factors was determined from the survey results. The survey was conducted with the experts that have experience in the construction of river-type hydropower schemes. The survey results revealed that the site geology and environmental issues were considered as the most important risks. The new risk assessment method enabled a Risk Index (R) value to be calculated, establishing a 4-grade evaluation system. The proposed risk analysis will give investors a more rational basis to make decisions and it can prevent cost and schedule overruns.     

Polar coordinated fuzzy controller based real-time maximum-power point control of photovoltaic system

It is crucial to improve the photovoltaic (PV) system efficiency and to develop the reliability of PV generation control systems. There are two ways to increase the efficiency of PV power generation system. The first is to develop materials offering high conversion efficiency at low cost. The second is to operate PV systems optimally. However, the PV system can be optimally operated only at a specific output voltage and its output power fluctuates under intermittent weather conditions. Moreover, it is very difficult to test the performance of a maximum-power point tracking (MPPT) controller under the same weather condition during the development process and also the field testing is costly and time consuming. This paper presents a novel real-time simulation technique of PV generation system by using dSPACE real-time interface system. The proposed system includes Artificial Neural Network (ANN) and fuzzy logic controller scheme using polar information. This type of fuzzy logic rules is implemented for the first time to operate the PV module at optimum operating point. ANN is utilized to determine the optimum operating voltage for monocrystalline silicon, thin-film cadmium telluride and triple junction amorphous silicon solar cells. The verification of availability and stability of the proposed system through the real-time simulator shows that the proposed system can respond accurately for different scenarios and different solar cell technologies.     

Iterative exergoeconomic evaluation and improvement of thermal power plants using fuzzy inference systems

This paper deals with the increase of cost effectiveness in the design of thermal power plants and presents a combination of the iterative exergoeconomic optimization technique with fuzzy inference systems (FIS). The development of FIS for the iterative exergoeconomic evaluation and improvement of plant components is described. An application to the iterative cost minimization of a simple cogeneration plant is discussed. Several case studies show that the application of fuzzy logic in exergoeconomics is very useful.     

A fuzzy logic based supervisory controller for an FC/UC hybrid vehicular power system

Depending on growing concerns on energy crises and environmental issues, fuel cell (FC) powered electrical vehicles are favored for possible substitute to conventional internal combustion engine (ICE) based vehicular systems. However, the typical power profile of an automobile motor consisting of transients is not suitable for the use of a sole FC system for vehicle propulsion. This shortcoming could be partly overcome by hybridization. Two potential benefits of combining an FC system with an energy storage unit, ultra-capacitor (UC) has been presented in this study. Firstly, the durability of the FC system could be improved because the additional energy source can fulfill the transient power demand fluctuations. Secondly, the ability of the energy storage source to recover braking energy enhances the fuel economy greatly. An important aspect in designing a hybrid power structure is to find a suitable control strategy that can manage the active power sharing and take advantage of the inherent scalability and robustness benefits of the hybrid system. An integrated procedure for mathematical modeling and power control strategy design for an FC/UC hybrid vehicle is presented in this paper. A fuzzy logic supervisory controller based power management strategy that secures the power balance in hybrid structure, enhances the FC performance and minimizes the power losses is proposed. The main contribution of this paper apart from the previous studies of the authors is the modeling of the complete FC power system with air supply compressor and the integration of the control of the FC system internal dynamics (especially the oxygen excess ratio) into the overall supervisory control structure to maximize the efficiency and durability. To demonstrate the effectiveness of the proposed power management scheme, simulation studies were performed using MATLAB^®, Simulink^® and SimPowerSystems^® environments by integrating the detailed mathematical and electrical models of the hybrid vehicular system.     

基于Mamdani模糊推理的风电机组控制方法研究

针对风电机组复杂多变的工作条件和自身控制要求,文章提出了一种基于Mamdani模糊推理的风电机组控制方法。该方法首先对控制对象进行模糊化处理,然后制定推理规则并确定控制推理方式,最后对推理结果进行精确化模块处理,反馈给控制对象。文章以风电机组偏航系统控制为例,研究了在风速大小、风速变化和风速变化率3种因素下调桨量的模糊控制,试验结果证明了该方法可以满足变工况下风电机组变桨控制要求。     

A fuzzy logic pitch angle controller for power system stabilization

In this article the design of a fuzzy logic pitch angle controller for a fixed speed, active‐stall wind turbine, which is used for power system stabilization, is presented. The system to be controlled, which is the wind turbine and the power system to which the turbine is connected, is described. The advantages of fuzzy logic control when applied to large‐signal control of active‐stall wind turbines are outlined. The general steps of the design process for a fuzzy logic controller, including definition of the controller inputs, set‐up of the fuzzy rules and the method of defuzzification, are described. The performance of the controller is assessed by simulation, where the wind turbine's task is to dampen power system oscillations. In the scenario simulated for this work, the wind turbine has to ride through a transient short‐circuit fault and subsequently contribute to the damping of the grid frequency oscillations that are caused by the transient fault. It is concluded that the fuzzy logic controller enables the wind turbine to dampen power system oscillations. It is also concluded that, owing to the inherent non‐linearities in a wind turbine and the unpredictability of the whole system, the fuzzy logic controller is very suitable for this application. Copyright © 2006 John Wiley &Sons, Ltd.     

Output voltage control of double chambers microbial fuel cell using intelligence-based optimized adaptive neuro fuzzy inference controller

Microbial fuel cell (MFC) has become a very important biotechnological tool to produce clean energy in recent years. It is very important to adjust the output voltage and power density in order to obtain the desired energy quickly and smoothly at the output of the MFC. In this study, an optimization-based neuro-fuzzy inference controller is proposed for improving voltage tracking performance of the MFC. A double-chambers MFC model including biochemical reactions, Butler-Volmer expressions and mass/charge balances was studied and Particle Swarm Optimization (PSO) and Improved Grey Wolf Optimization (IGWO) algorithms are used to adjust the parameters of the neuro-fuzzy controller. The results show that PSO and IGWO based controllers have efficient performances to follow the reference voltage pattern quickly and robustly against external load changes, distributions and parameter uncertainties. Moreover, it was observed that IGWO was a more stable and robust controller than PSO according to rise time, overshoot and peak time.     

Doubly-fed induction generator drive based WECS using fuzzy logic controller

The purpose of this paper is to improve the control performance of the variable speed, constant frequency doubly-fed induction generator in the wind turbine generation system by using fuzzy logic controllers. The control of the rotor-side converter is realized by stator flux oriented control, whereas the control of the grid-side converter is performed by a control strategy based on grid voltage orientation to maintain the DC-link voltage stability. An intelligent fuzzy inference system is proposed as an alternative of the conventional proportional and integral (PI) controller to overcome any disturbance, such as fast wind speed variation, short grid voltage fault, parameter variations and so on. Five fuzzy logic controllers are used in the rotor side converter (RSC) for maximum power point tracking (MPPT) algorithm, active and reactive power control loops, and another two fuzzy logic controllers for direct and quadratic rotor currents components control loops. The performances have been tested on 1.5 MW doubly-fed induction generator (DFIG) in a Matlab/Simulink software environment.     

模糊理论结合PID控制方法在集中供热系统中的应用

集中供热系统是一个具有大惯性、纯滞后特点的复杂非线性系统,为了提高系统稳定性,实现合理用热,将模糊自适应控制和常规PID控制两种控制方法结合并使用模糊工具箱进行了系统仿真实验。实际结果表明,同常规的PID控制方法相比,模糊自适应PID控制方法有效提高了集中供热系统的鲁棒性、稳定性和自适应的能力。在集中供热的复杂系统中,能更好的满足用户的热需求     

Control of a fuel cell based on the SIRMs fuzzy inference model

A typical working mode for the fuel cell hybrid system is that the fuel cell produces constant power output while the auxiliary storage energy device such as ultracapacitor or battery provides the deviation between the desired power demand and the value of the actual operation. This paper concentrates on the control of the fuel cell flow system. The system which is like an under-actuated mechanical system needs to control two objects (the cathode pressure and the air flow) with one manipulated variable (the set voltage of the air mass flow controller). A SIRMs-based fuzzy inference model is successfully implemented in the system. Online random search optimization algorithm based on Simulink C-S functions is developed to adjust the parameters in the model. By alternative control of the two objects, experimental results demonstrate the realization of the control strategy with one adjustment means.     

Frequency control in micro-grid power system combined with electrolyzer system and fuzzy PI controller

The widespread use of various kinds of distributed power sources would impact the quality of the power supply within a micro-grid power system, causing many control problems. This paper focuses on the stability of micro-grid operation and discusses the control techniques of combining a micro-turbine with the fuel cell and electrolyzer hybrid system to expand the micro-grid system's ability to solve power quality issues resulting from frequency fluctuations. The paper examines the feasibility of fuel cell and electrolyzer hybrid system control, especially dynamic control of an electrolyzer system, to secure a real power balance and enhance the operational capability of load frequency control. The proposed control and monitoring system can be considered to be a means of power quality control, both to improve the frequency fluctuations caused by random power fluctuations on the generation and load sides and to relax tie-line power flow fluctuations caused by frequency fluctuations in the interconnected micro-grid power system.     

Design and implementation of Type-2 fuzzy neural system controller for PWM rectifiers

It is necessary to convert AC to DC for the systems that do not work with AC sources. For this reason, diode and thyristor rectifiers were developed and designed. However, these rectifiers are not well suited for industrial applications requiring high performance. With the advances in power electronics and semiconductor technology, Pulse width modulation (PWM) rectifiers have been successfully employed in various industrial applications including variable-speed drives and uninterruptible power supplies. PWM rectifiers have the advantages of being low input current harmonic, adjustable input power factor, and controllable DC voltage and bidirectional energy flow. Because of all these features of the PWM rectifiers, the control and design of these rectifiers are very important topic. The aim of this paper is to control DC-link voltage of PWM rectifier with type-2 fuzzy neural system (T2FNS) instead of PI controller. For this aim, three-phase PWM rectifier with proposed controller is designed and simulated for four scenarios in this paper. A simulation model of the PWM rectifier is designed in MATLAB/Simulink and the performance of PWM rectifier with proposed controller is analyzed.     

风力发电机多域物理建模方法及独立变桨模糊控制器优化研究

针对独立变桨风力发电机,分析了风力发电机运行原理,基于以上原理在Matlab下分别建立了1.5MW独立变桨风力发电机的监测控制系统、独立变桨距控制系统、偏航控制系统、液压执行器系统和电网系统的物理模型,设计了独立变桨距控制模糊PI控制器,与传统PI控制器进行了仿真对比。仿真结果表明了该物理模型的正确性,对比结果表明,模糊PI控制器不仅能提高节距角跟踪精度而且能够稳定输出电压和输出功率,同时也减小了拍打震动和桨叶载荷。     

基于嵌入式系统的节能软启动控制器

针对电动机的启动电流过大，设计了一种以嵌入式系统为核心，辅以其他芯片的节能软启动控制器。它是具备节能运行、软启动和多种保护功能的电动机软启动装置。     

模糊控制器在水泵恒压频比调速系统中的实现

提出一种带自调整因子的模糊控制器的交流闭环变频调速系统的设计方法,把变频器和感应电机组成的变频调速系统看成一个整体—恒压频比变频调速系统。仿真结果表明:带自调整因子的模糊控制器比传统PID控制器的动态响应曲线好、响应时间短、超调量小,稳态精度高,动静态性能好。     

Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system

This paper proposes a methodology of designing a Maximum Power Point Tracking (MPPT) controller for photovoltaic systems (PV) using a Fuzzy Gain Scheduling of Proportional-Integral-Derivative (PID) type controller (FGS-PID) with adaptation of scaling factors (SF) for the input signals of FGS. The proposed adaptive FGS-PID method is based on a two-level control system architecture, which combines the advantages of fuzzy logic and conventional PID control. The initial values of the PID's gains are determined by the Ziegler–Nichols tuning method. During transient and steady states, the PID's gains are adapted by the FGS-PID to damp out the transient oscillations, to reduce settling time and to guarantee system stability and accuracy. Also, the conditioned input signals of the FGS-PID are tuned dynamically by gain factors which are based on fuzzy logic system (FLS). The FLS is characterized by a set of fuzzy rules which are fuzzy conditional statements expressing the relationship between inputs (error and change of error) and outputs. This approach creates an adaptive MPPT controller and achieves better overall system performance. The simulation results demonstrate the effectiveness of the proposed adaptive FGS-PID and show that this approach can achieve a good maximum power operation under any conditions such as different levels of solar radiation and PV cell temperature for varying PV sources. Compared to conventional methods (PID, perturb and observe method P&O), this method shows a considerable high tracking performance.     

Design and implementation of a fuzzy controller based automaticvoltage regulator for a synchronous generator

Fuzzy controllers are increasingly being accepted by engineers and scientists alike as a viable alternative for classical controllers. The processes involved in fuzzy controllers closely imitate human control processes. Human responses to stimuli are not governed by transfer functions and neither are those from fuzzy controllers. This study involves the design and application of fuzzy control to the problem of automatic voltage regulation of a synchronous generator. The method explored deals with the use of binary input-output fuzzy associative memories for control. Error and rate of change of voltage are used to maintain a constant output voltage. Software routines were written in the `C' language and were fast enough for real time computer control. The fuzzy controller was implemented in an IBM compatible personal computer to control an industrial size 5 kVA synchronous machine     

基于GSM的水电厂闸门PLC远程监控系统

利用GSM网络的SMS(ShortMessageService)通讯模块,构建了水电厂闸门远程监控系统的现地控制层与中控层之间的无线数据通讯系统,并采用Wavecom公司生产的SMS通讯模块WMOi3实现了SMS装置与PLC以及工作站之间的数据通讯,为闸门PLC远程监控系统提供了一种新的通讯方式