永磁交流速度伺服系统抗饱和设计研究

传统的AW算法属于"二步法"设计,但有2个本质的缺陷：①控制器补偿系数具有较大的随意性;②系统综合困难,即补偿后闭环系统没有明确的动态品质。针对传统的AW设计的问题,该文采用基于状态跟踪的AW设计方法。该方法通过理想线性模型及其控制器参数便可直接获得补偿器的参数,补偿器不受外部参考输入和状态限制的影响。这就意味着一旦系统线性控制器设计完成,非线性补偿器也就随之确定,并且使系统具有最优的补偿效果。通过与PI控制器传统AW设计方法的对比研究验证了该方法的有效性。仿真与实验结果表明该方法在永磁交流速度伺服系统获得了良好的控制效果。     

Global optimization method for controlling a plant

A control system that contains a generic plant has been built. Its controller parameters have been designed by using a global search algorithm for a given plant, and its performance has been evaluated for the heat control of a thermal system, and the speed control of a DC motor. This paper explains this control system with a DC motor as its plant, the speed of which is controlled by the controller.     

Robust decentralized load frequency control of interconnected power system with Generation Rate Constraint using Type-2 fuzzy approach

The Load Frequency Control (LFC) problem has been a major subject in electrical power system design/operation. LFC is becoming more significant recently with increasing size, changing structure and complexity in interconnected power systems. In practice LFC systems use simple Proportional Integral (PI) controllers. As the PI control parameters are usually tuned, based on classical approaches. Moreover, they have fixed gains; hence are incapable of obtaining good dynamic performance for a wide range of operating conditions and various load changes, in multi-area power system. Literature shows that fuzzy logic controller, one of the most useful approaches, for utilizing expert knowledge, is adaptive in nature and is applied successfully for power system stabilization control. This paper proposes a Type-2 (T2) fuzzy approach for load frequency control of two-area interconnected reheat thermal power system with the consideration of Generation Rate Constraint (GRC). The performance of the Type-2 (T2) controller is compared with conventional controller and Type-1 (T1) fuzzy controller with regard to Generation Rate Constraint (GRC). The system parametric uncertainties are verified by changing parameters by 40% simultaneously from their typical values.     

Bacteria foraging optimization algorithm based load frequency controller for interconnected power system

Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes BFOA based Load Frequency Control (LFC) for the suppression of oscillations in power system. A two area non-reheat thermal system is considered to be equipped with proportional plus integral (PI) controllers. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by genetic algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on BFOA in compare to optimized PI controller based on GA and conventional one over wide range of operating conditions, and system parameters variations.     

基于遗传算法的发电机模糊式综合励磁控制器的研究

本文在分析了模糊控制器工作原理及其传统设计方法的基础上,提出了一种模糊控制器的自寻优设计方案,成功地解决了在模糊控制器的传统设计方法中存在的过分依赖于设计者预先选用的启发式知识及操作者的经验等问题,并采用遗传算法（ＧＡ）实现了这一方案。用新方法设计的发电机模糊式综合励磁控制器兼顾了对发电机端电压和功角稳定的要求。仿真结果表明,新方法设计的控制器具有良好的控制效果,而且设计过程更加合理。因为这种设计     

锅炉过热汽温自适应PI型模糊控制的仿真研究

针对锅炉过热蒸汽温度对象的非线性和不确定性等,设计了PI型模糊串级控制系统,并将该系统应用于某电厂600 MW机组锅炉过热蒸汽温度的控制。在分析模糊控制器相关参数与控制系统品质之间关系的基础上,提出相关参数的在线自校正方法,构造了一种自适应PI型模糊控制器。该控制器能够在线调整其量化因子和比例因子,以适应被控对象动态特性的变化,从而提高模糊控制系统的性能。将自适应PI型模糊控制方法与常规PID控制及PI型模糊控制进行仿真比较,表明采用自适应PI型模糊控制方法建立的过热蒸汽温度控制系统具有较好的控制品质和较强的自适应能力。     

MPPT control design and performance improvements of a PV generator powered DC motor-pump system based on artificial neural networks

This paper presents the optimum photovoltaic (PV) water pumping system using maximum power point tracking technique (MPPT). The optimum is suspended to reference optimal power. This optimal technique is developed to assure the optimum chopping ratio of buck–boost converter. The presented MPPT technique is used in photovoltaic water pumping system in order to optimize its efficiency. An adaptive controller with emphasis on Nonlinear Autoregressive Moving Average (NARMA) based on artificial neural networks approach is applied in order to optimize the duty ratio for PV maximum power at any irradiation level. In this application, an indirect data-based technique is taken, where a model of the plant is identified on the basis of input–output data and then used in the model-based design of a neural network controller. The proposed controller has the advantages of fast response and good performance. The PV generator DC motor pump system with the proposed controller has been tested through a step change in irradiation level. Simulation results show that accurate MPPT tracking performance of the proposed system has been achieved. Further, the performance of the proposed artificial neural network (ANN) controller is compared with a PID controller through simulation studies. Obtained results demonstrate the effectiveness and superiority of the proposed approach.     

Artificial intelligence-based speed control of DTC induction motor drives—A comparative study

The design of the speed controller greatly affects the performance of an electric drive. A common strategy to control an induction machine is to use direct torque control combined with a PI speed controller. These schemes require proper and continuous tuning and therefore adaptive controllers are proposed to replace conventional PI controllers to improve the drive's performance. This paper presents a comparison between four different speed controller design strategies based on artificial intelligence techniques; two are based on tuning of conventional PI controllers, the third makes use of a fuzzy logic controller and the last is based on hybrid fuzzy sliding mode control theory. To provide a numerical comparison between different controllers, a performance index based on speed error is assigned. All methods are applied to the direct torque control scheme and each control strategy has been tested for its robustness and disturbance rejection ability.     

A synchronous generator fuzzy excitation controller optimallydesigned with a genetic algorithm

Design of a fuzzy logic power system controller with satisfactory performance is not an easy task. The difficulties come from two aspects. First, design of a fuzzy logic controller mainly uses the experience of the human experts. To acquire enough heuristic knowledge from the domain experts and to represent this kind of knowledge appropriately with a set of fuzzy rules present difficulties. Second, it is difficult to appropriately tune the parameters used in the fuzzy logic controller. These parameters are commonly determined by a “trial and error” method which is rather time consuming. In this paper, genetic algorithm is introduced to design an optimal fuzzy logic controller. The proposed method has been used to design an optimal fuzzy logic excitation controller for a generating unit. Test results with the fuzzy logic controller show very satisfactory results     

不稳定大时滞过程的串级PID控制

针对一类不稳定大时滞过程,提出了一种基于内模控制（IMC）框架下改进的串级PID控制结构,给出了参数设计的一般准则。系统双闭环均按照IMC原理设计为经典反馈控制结构,内环控制器主要用于镇定不稳定对象和抑制主要干扰,外环用于改善系统性能。内外环控制器均只用一个调节参数,调节参数和对象参数与闭环系统性能解析关系明确。仿真表明,该方法能有效控制滞后时间与时间常数之比大于1的大时滞过程,鲁棒性强,结构设计和调节简单,适合工程应用。     

A self-tuning fuzzy PI controller for TCSC to improve power system stability

In this paper, a self-tuning fuzzy PI controller (STFPIC) is proposed for thyristor-controlled series capacitor (TCSC) to improve power system dynamic performance. In a STFPIC controller, the output-scaling factor is adjusted on-line by an updating factor (α). The value of α is determined from a fuzzy rule-base defined on error (e) and change of error (Δe) of the controlled variable. The proposed self-tuning controller is designed using a very simple control rule-base and the most natural and unbiased membership functions (MFs) (symmetric triangles with equal base and 50% overlap with neighboring MFs). The comparative performances of the proposed STFPIC and the standard fuzzy PI controller (FPIC) have been investigated on two multi-machine power systems (namely, 4 machine, 2 area system and 10 machine 39 bus system) through detailed non-linear simulation studies using MATLAB/SIMULINK. From the simulation studies it has been found out that for damping oscillations, the performance of the proposed STFPIC is better than that obtained by the standard FPIC. Moreover, the proposed STFPIC as well as the FPIC have been found to be quite effective in damping oscillations over a wide range of operating conditions and are quite effective in enhancing the power carrying capability of the power system significantly.     

Feedback-linearization and feedback–feedforward decentralized control for multimachine power system

In this paper a decentralized nonlinear controller for large-scale power systems is investigated. The proposed controller design is based on the input–output feedback linearization methodology. In order to overcome computational difficulties in adopting such methodology, the overall interconnected nonlinear system, given as n-order, is analyzed as a cascade connection of an n₁-order nonlinear subsystem and an n₂-order linear subsystem. The controller design is obtained by applying input–output feedback linearization to the nonlinear subsystem and adopting a tracking control scheme, based on feedback–feedforward technique, for the linear subsystem. In the assumed system model, which is characterised by an interconnected structure between generating units, a decentralised adaptive controller is implemented by decentralizing these constraints. The use of a totally decentralised controller implies a system performance decay with respect to performance when the system is equipped with a centralised controller. Fortunately, the robustness of the proposed controller, based on input–output feedback procedure, guarantees good performance in terms of disturbance even when disturbances are caused by decentralization of interconnection constraints.     

Decentralized nonlinear H∞ controller for large scale power systems

The aim of this paper is to design nonlinear decentralized controllers for multi-machine power systems. The design procedure is based on H_∞ control theory and consists of two parts. First, the feedback linearization technique is used. Then, a robust controller is designed using the linear matrix inequalities (LMI) approach. The controller has two blocks. One, is a nonlinear function of some local measurable signals such as the generator active and reactive powers, the rotor speed and the armature current. The other block is a PID controller. The linear H_∞ theory is used to tune the PID parameters. The method results in a controller which is easy for implementing in practice. The performance of the controller is tested on a sample multi-machine power system model. Simulation results show the effectiveness, robustness and good performance of the proposed controller.     

基于DSP的无人机配电控制器检测系统的设计

针对某型无人机配电控制器现有检测方法的不足,根据配电控制器的性能测试要求,设计了一种基于DSP技术的配电控制器检测系统。该系统以TMS320LF2407控制器为核心,合理利用了DSP控制器自身集成的ADC模块、CAN总线控制器模块和捕捉单元等,实现对配电控制器各项性能参数的检测。文章详细介绍了该检测系统的组成、工作原理以及硬件电路和软件设计。实际应用表明,该系统能够准确的完成各项性能指标参数的检测,达到了设计目的,具有较高的实用价值。     

Decentralized robust adaptive-output feedback controller for power system load frequency control

In this paper, a new model reference-decentralized robust adaptive-output feedback controller is proposed for the load frequency control (LFC) of large-scale power systems with unknown parameters. This control strategy requires only local input–output data and can follow random changes in the operating conditions. The controller is designed such that the trajectory errors and the control gains of each area remain uniformly bounded. In the proposed method, firstly an adaptive observer is designed to estimate the state variables and system parameters using local data only. Then a locally linear combination of the estimated states and the model reference states are used to design a robust adaptive-output feedback controller for each area. Simulation results for a three-area power system show that the proposed controller achieves good performance even in the presence of plant parameter changes and system non-linearities. Received: 18 October 2001/Accepted: 24 October 2001     

Sequential design of controller for parallel dc/dc converters via Gauss-Jordan factorization: Nyquist approach

Conclusion  In this paper the design of a multivariable controller for parallel operating dc/dc converters is presented. The applied design method is the Gauss-Jordan Nyquist sequential procedure. Three differently structured plant models from the literature are subjected to this procedure, each with arbitrary number of paralleled converters. In each case adequate controller is obtained, its structure discussed and variable parameters tuned. Proximity of resulting controllers with those obtained through decoupling approach is also explained. The simulation analysis revealed excellent closed loop performance, proving that the performance objectives are satisfied. In all three cases, resulting controllers were designed for plant models based on laboratory ‘buck’ converter prototype described in literature. Nevertheless, arguments used and explanation given make generalization of obtained controllers to any converter type straightforward.     

Development and field tests of a damping controller to mitigate electromechanical oscillations on large diesel generating units

This paper presents the development and field tests of a digital damping controller designed to mitigate intra-plant electromechanical oscillations via the speed governor system of fast acting units. The controller performance is assessed on an 18-MVA diesel generating unit, at Santana Power Plant (Amapá State, Amazon Region at Northern Brazil). In order to design the damping control law, a set of parametric ARX models representing the plant dynamics at several load conditions, are previously identified from data collected on field tests. The damping controller gains are calculated by using the identified ARX models parameters as inputs to a discrete-time pole-placement design method (pole-shifting) and then embedded on a DSP based microcontroller digital system, for field tests assessment. The digital damping controller modulates the diesel engine inlet valve position according to the observed oscillation on the measured electric power, using a PWM device, which is specially developed to this application. The experimental results shown the good performance of the developed controller on damping efficiently the electromechanical oscillations observed between generating units at Santana Power Plant.     

Performance Evaluation of Power Oscillation Damping Controller—Firefly Algorithm Based Parameter Tuning

This paper presents the performance evaluation of power oscillation damping controller based on firefly algorithm (FA) parameter tuning. The power system stabilizer (PSS), unified power flow controller (UPFC), and static synchronous series compensator (SSSC) are tuned with FA by minimizing integral time multiplied by absolute error (ITAE) as an objective function. An integrated multi-stage linear quadratic regulator – power oscillation damping UPFC/SSSC has been proposed with precise tuning of control parameters which results in overall states' oscillation damping as compared to other classical methods. It has been observed that the proposed control structure damps the oscillations adequately and is modular in design methodology. The sample power system comprising six areas has been considered to demonstrate the effectiveness of the concept. The software has been developed based on the proposed work by the authors and the MATLAB code has been generated in R2009b version. The states' inter-relation which has been shown with eigenvalues reflects a better regulation and the step response is also validated.     

基于遗传算法的Boost变换器协同控制器设计

由于Boost变换器是一个非线性时变系统,使得控制器的设计遇到了一定的挑战。为改善Boost变换器的控制性能,本文设计了一种基于协同控制理论的控制器,并利用遗传算法对控制器参数进行优化。仿真和试验结果表明,该控制方法具有较好的启动特性,并对系统参数及负载的变化具有很强的鲁棒性。     

Assessment of dynamic matrix control controller parameters via estimating Markov parameters of disturbance

For an industrial control system, controller parameters are important factors that affect the control system performance. This article introduces a controller parameter performance assessment method based on disturbance characteristic variation for a dynamic matrix control system. Assuming that the process model is accurate and the setpoint is constant, disturbance characteristic variation without adjusting controller parameters leads to the degradation of system performance. Hence, the Markov parameters of a disturbance model, which inflect the variation of disturbance characteristics, are used to assess the controller parameter performance. These Markov parameters can be obtained from closed-loop data using the subspace identification method. The differences between the Markov parameters in an actual running state and those in a well-regulated state are used to design the assessing index of controller parameters. The simulation results in the Wood–Berry model and TE process show the validity of the proposed index.