Short-current pulse-based maximum-power-point tracking method formultiple photovoltaic-and-converter module system

This paper proposes a novel maximum-power-point tracking (MPPT) method with a simple algorithm for photovoltaic (PV) power generation systems. The method is based on use of a short-current pulse of the PV to determine an optimum operating current where the maximum output power can be obtained and completely differs from conventional hill-climbing-based methods. In the proposed system, the optimum operating current is instantaneously determined simply by taking a product of the short-current pulse amplitude and a parameter k because the optimum operating current is exactly proportional to the short current under various conditions of illuminance and temperature. Also, the system offers an identification capability of k by means of fast power-versus-current curve scanning, which makes the short-current pulse-based MPPT method adaptive to disturbances such as shades partially covering the PV panels and surface contamination. The above adaptive MPPT algorithm has been introduced into a current-controlled boost chopper and a multiple power converter system composed of PV-and-chopper modules. Various operating characteristics have experimentally been examined on this multiple PV-and-chopper module system from a practical viewpoint and excellent MPPT performance has been confirmed through the tests     

T-S模糊系统非脆弱跟踪控制器设计

针对T-S模糊系统,研究了非脆弱H∞跟踪控制器的设计问题.目的是通过设计跟踪控制器使得被控闭环系统满足给定的H∞跟踪控制性能指标.本文采用线性矩阵不等式(LMI)方法,求解过程不必通过二次迭代的两步算法,所设计的控制器在一定的加性参数变化情况下,仍能保证系统的渐进稳定性.最后,通过单关节刚性机械臂系统的仿真实验表明了该方法的有效性.     

自适应模糊PID控制的太阳光跟踪伺服系统

针对太阳光跟踪伺服系统中应用的传统PID控制过程中的一些问题,本文通过对自适应模糊PID控制系统的分析,设计了双轴跟踪伺服系统自适应模糊PID控制器,并在Simulink环境中建立方位角跟踪传动机构仿真模型且完成仿真。仿真结果表明,太阳光跟踪伺服系统自适应模糊PID控制器较传统PID控制器具有较强的稳定性、适应性与鲁棒性,这在太阳光跟踪伺服系统控制领域具有重要的实用价值与应用空间。     

Using fuzzy controller for improvement of power decoupling performance in PV systems

ABSTRACT

In photovoltaic systems in order to ensure the stable operation of the module, ripple value of the input voltage should be mitigated to a small value; for this purpose, the power decoupling circuit can be used. In this work, considering the four-switch-based power decoupling circuit developed, the conventional Proportional-Integral (PI) controller has been replaced with well-known fuzzy controller, which enjoys a wide range of control gain variation, both linear and nonlinear rules' capability, robustness and more flexibility. The operating modes of the circuit are addressed and accordingly the space state model is derived. Then, the proposed controller has been replaced and the performance of the system will be analysed. The power decoupling circuit along with the proposed controller is implemented by MATLAB/Simulink software. The simulation results show the ability of the proposed fuzzy controller to improve the performance of the system in comparison to the conventional PI controller. A deeper comparison of two controllers confirms that the system would experience more accuracy, faster response and lower total harmonic distortion (THD) in presence of the proposed fuzzy controller.     

基于模糊免疫自适应PID的汽车烘房温度控制系统

烘房温度控制是汽车涂装系统的重要环节,对汽车生产的质量、成品率等有重要影响。针对烘房温度控制系统的难点和性能要求,提出了利用模糊免疫自适应PID控制器调节烘房温度。该控制方法兼具模糊控制、免疫控制和传统PID的优点,通过实时调整PID控制器的参数来保持系统的稳定性。仿真结果表明基于自适应模糊免疫PID控制器在响应速度、抗干扰能力上都优于模糊PID控制器和传统PID控制器。该控制方法为汽车烘房温度控制提供了一种新的控制策略。     

Neural-network-based self-tuning PI controller for precise motioncontrol of PMAC motors

In general, proportional plus integral (PI) controllers used in computer numerically controlled machines possess fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed-gain PI controllers, we propose a new neural-network-based self-tuning PI control system. In this new approach, a well-trained neural network supplies the PI controller with suitable gain according to each operating condition pair (torque, angular velocity, and position error) detected. To demonstrate the advantages of our proposed neural-network-based self-tuning PI control technique, both computer simulations and experiments were executed in this research. During the computer simulation, the direct experiment method was adopted to better model the problem of hysteresis in the AC servo motor. In real experiments, a PC-based controller was used to carry out the control tasks. Results of both computer simulations and experiments show that the newly developed dynamic PI approach outperforms the fixed PI scheme in rise time, precise positioning, and robustness     

光电跟踪系统控制器硬件设计

为了提高光电跟踪系统控制器的性能,文中对光电跟踪系统控制器的硬件部分进行了设计。首先给出光电跟踪系统控制器硬件电路框图。接着介绍最小系统的设计方案,其中包括存储器扩展,外部时钟,复位电路,电源电路,CPU板的抗干扰分析与设计等部分。最后对模拟量、数字量模块和通信模块设计进行了详细阐述。采用该控制器不仅增强了光电跟踪系统的灵活性和适应性,保证了系统性能要求,而且还具有良好的实时性。     

Tunable LNA filter design using coupled-inductor Q-enhancement

A tunable LNA filter using Q-enhanced inductors is designed in 0.25 μm BiCMOS Qubic4x technology. The design employs the inductor degenerated LNA, acting as a transconductance which converts the input voltage to output current which drives the second-order Q-enhanced filter. The filter also uses a special technique based on coupled-inductor negative resistance generator to make the quality factor and the center frequency tunable. The overall gain of the LNA filter is about 19.5 dB and the minimum noise figure is 6.4 dB. The center frequency is 942.5 MHz with a 42 MHz (3 dB) bandwidth.     

基于ARM太阳能光伏板自动跟踪系统的设计

以ARM11处理器为主控芯片的嵌入式光伏自动控制方案,由葵花太阳能光伏板,光敏电阻,光电二极管,电机,立体支架和ARM11组成。该系统通过比较光敏电阻的大小,并结合三只眼昆虫识别方向的特点,由一个ARM11系统控制多个电机驱动器,驱动电机自动旋转,使多个葵花太阳能光伏板与太阳光保持最佳角度。从而实现一机多能和对太阳的自动跟踪,实验结果证明该系统不但能有效地提高太阳能的利用率同时还能节约大量的成本。     

DSP based overcurrent relay using fuzzy bang-bang controller

Overcurrent relays are very important protection component that require high reliability to maintain high security in power systems. With the new numerical relay technology using digital signal processor (DSP), it is possible to improve the performance of the relay significantly. However, application of DSP in numerical overcurrent relays is limited especially in coordination among the group of relays. The relay must work proficiently to coordinate with the networks in order to avoid mal-operation. Therefore, in this paper, an implementation of overcurrent relay with improved coordination on a DSP, TMS320F2812 is described. The fuzzy bang-bang controller is used as the control strategy for the relay to provide efficient control for overcurrent protection. The performance evaluation of the proposed system is based on steady state analysis, transient state analysis, coordination and lastly the execution time of the DSP. The results obtained using this new proposed controller is very promising.     

Neural-network-based adaptive control for induction servomotordrive system

A neural-network-based adaptive control (NNAC) design method is proposed to control an induction servomotor. In this NNAC design, a neural network (NN) controller is investigated to mimic a feedback linearization control law; and a compensation controller is designed to compensate for the approximation error between the feedback linearization control law and the NN controller. The interconnection weights of the NN can be online tuned in the sense of the Lyapunov stability theorem; thus, the stability of the control system can be guaranteed. Additionally, in this NNAC system design, an error estimation mechanism is investigated to estimate the bound of approximation error so that the chattering phenomenon of the control effort can be reduced. Simulation and experimental results show that the proposed NNAC servomotor control systems can achieve favorable tracking and robust performance with regard to parameter variations and external load disturbances     

一种自适应模糊PID控制器应用仿真

研究了一种误差驱动型自适应模糊PID控制器在二阶时滞系统中的应用。选择系统的稳态误差和误差变化率为PID参数自适应模糊调整的前件变量,将前件变量模糊化为7个论域期间,建立了PID参数调整的模糊规则表,查表得PID参数的后件参数,采用极大值反模糊化规则得到具体PID控制参数。仿真结果表明采用该控制器后系统的调节时间大大缩短,控制效果令人满意。     

Design and performance analysis of generalised integrator-based controller for grid connected PV system

This article discusses the design and control of a single-phase grid-connected photovoltaic (PV) system. A 5-kW PV system is designed and integrated at the DC link of an H-bridge voltage source converter (VSC). The control of the VSC and switching logic is modelled using a generalised integrator (GI). The use of GI or its variants such as second-order GI have recently evolved for synchronisation and are being used as phase locked loop (PLL) circuits for grid integration. Design of PLL circuits and the use of transformations such as Park’s and Clarke’s are much easier in three-phase systems. But obtaining in-phase and quadrature components becomes an important and challenging issue in single-phase systems. This article addresses this issue and discusses an altogether different application of GI for the design of compensator based on the extraction of in-phase and quadrature components. GI is frequently used as a PLL; however, in this article, it is not used for synchronisation purposes. A new controller has been designed for a single-phase grid-connected PV system working as a single-phase active compensator. Extensive simulation results are shown for the working of integrated PV system under different atmospheric and operating conditions during daytime as well as night conditions. Experimental results showing the proposed control approach are presented and discussed for the hardware set-up developed in the laboratory.     

Fault-tolerant switched reluctance motor drive using adaptive fuzzy logic controller

An adaptive fuzzy controller has been designed to develop a high-performance fault-tolerant switched reluctance motor (SRM) drive. The fuzzy controller continuously adapts its properties to regulate the machine torque as desired by the drive system even under fault conditions. The adaptation of the fuzzy membership functions results in extended conduction period and increased peak current of the healthy phases to deliver the commanded torque, as much as possible. The adaptive fuzzy controller provides smooth torque output with minimum ripple, even under fault conditions, yielding a high-performance SRM drive with fault-tolerant capability.     

多采样率控制器设计在光电跟踪系统中的应用

采用保持器、采样器和提升技术对输入、反馈和输出多采样率多环路系统进行分析,并将多采样率的周期离散时变系统化为时不变系统,且在输入和反馈端依据信号特性引入高频低通滤波器进行信号频谱延拓周期的扩展.在三环结构光电跟踪控制系统中的应用表明:离散控制器有和连续控制器一致的阶跃响应,在引入高频低通滤波器扩展频域延拓周期后,由低频位置输入引入的系统速度震荡基本上完全被抑制.     

大功率半导体激光器的精密模糊PID温控系统

介绍了一种大功率半导体激光器的精密模糊PID温控系统.利用半导体致冷器作为控温执行器件,构成了全固态致冷系统,并且针对TEC的驱动特性,设计了一种高精度数字PWM功率驱动器.本系统采用AT89C52单片机,利用软件实现了在线参数自整定模糊PID控制算法以及热敏电阻的非线性补偿.试验证明,温度控制精度可达±0.06℃.     

Comparative analysis of maximum power point tracking controller for wind energy system

This paper proposes an adaptive maximum power point tracking (MPPT) control method to achieve the maximum power from the wind turbine (WT) power generation system. The MPPT control method has a vital role to find the maximum power point, and also compute the simulation results in both analysis such as offline using MATLAB/SIMULINK? and online using OPAL-RT simulator. The proposed adaptive perturb and observe (P&;O) algorithm provides better results using the OPAL-RT simulator compared to the P&;O method using MATLAB/SIMULINK?. The power generation through doubly fed induction generator (DFIG) WT system is becoming important day by day throughout the world. The simulation result by OPAL-RT simulator showed that the efficiency of WT-DFIG has been enhanced.     

Neural-network-based fuzzy model and its application to transientstability prediction in power systems

We present a general approach to deriving a new type of neural network-based fuzzy model for a complex system from numerical and/or linguistic information. To efficiently identify the structure and the parameters of the new fuzzy model, we first partition the output space instead of the input space. As a result, the input space itself induces corresponding partitions within each of which inputs would have similar outputs. Then we use a set of hyperrectangles to fit the partitions of the input space. Consequently, the premise of an implication in the new type of fuzzy rule is represented by a hyperrectangle and the consequence is represented by a fuzzy singleton. A novel two-layer fuzzy hyperrectangular composite neural network (FHRCNN) can be shown to be computationally equivalent to such a special fuzzy model. The process of presenting input data to each hidden node in a FHRCNN is equivalent to firing a fuzzy rule. An efficient learning algorithm was developed to adjust the weights of an FHRCNN. Finally, we apply FHRCNNs to provide real-time transient stability prediction for use with high-speed control in power systems. From simulation tests on the IEEE 39-bus system, it reveals that the proposed novel FHRCNN can yield a much better performance than that of conventional multilayer perceptrons (MLP's) in terms of computational burden and classification rate