光伏系统最大功率跟踪方法的研究及其仿真

在分析光伏阵列的数学模型和输出特性的基础上,提出了自适应扰动控制算法。对该算法进行了理论分析,建立了光伏系统的仿真模型,并在Matlab/Simulink环境下进行仿真。仿真结果表明,系统能够快速地跟踪到最大功率点,在光照强度突变的情况下也能快速追踪到最大功率点,具有较好的控制性能。     

小波神经网络PID在风电系统最大功率点跟踪中的应用研究

针对风电系统处于欠功率阶段时,风能利用系数必须保持在最大值处的问题,提出了一种基于小波神经网络PID控制器的最大功率点跟踪控制策略。该策略采用小波神经网络对风能利用系数进行在线辨识,1个辨识周期结束后返回此时的灵敏度信息,PID控制器根据该灵敏度信息调整PID参数。仿真结果表明,与常规PID控制器相比,小波神经网络PID控制器提高了风电系统的叶尖速比、风能利用系数和输出功率,缩短了响应时间,实现了风力发电机组的优化运行。     

A novel maximum power point tracking algorithms for stand-alone photovoltaic system

A novel control algorithm, namely subsection adaptive hill climbing method (SSAHC), for seeking the maximum power point (MPP) of a photovoltaic (PV) panel for any temperature and solar radiation level is proposed. The algorithm is thus a combination of the subsection and adaptive hill climbing methods. In this algorithm, the characteristic curve of power-voltage of PV panel was divided into three subsections, namely large step approximation section, adaptive hill climbing section and maximum power section. Using this method, the MPP tracker (MPPT) can tune adaptively the step to track the MPP of PV system. The main advantage of the MPPT controlled by this new algorithm, when is compared with others, is that it can draw more power at a certain weather condition, especially, in case solar radiation changes rapidly at higher radiation.     

一种用于光伏最大功率点跟踪的Boost电路

光伏发电系统中利用Boost电路进行最大功率跟踪的过程存在电路升压能力不足、输入纹波较大等问题,利用开关电感结构替代并联交错Boost电路中电感,构成一种高升压比且低纹波的改进型Boost电路。该电路在同一开关周期中拥有四种开关模式,存在三种不同工作状态,利用平均周期建模法讨论其不同占空比情况下输出电压增益及输入电流纹波情况。MATLAB仿真结果表明,改进型Boost相比于传统Boost电路具有更高的升压能力;且在动态输入条件下,具有较快的跟踪速度,输入电流纹波小,输出功率控制效果稳定,适用于光伏发电最大功率点跟踪。     

光伏电池最大功率点跟踪方法比较

根据太阳能光伏电池的工程数学模型,在Matlab环境下建立了光伏电池仿真模型,分析了光照强度和温度变化对光伏电池输出特性的影响。针对扰动观察法采用固定的扰动步长而难以获得较高跟踪精度和响应速度的问题,提出了一种基于变步长的改进的扰动观察法,并通过对光伏电池控制系统进行仿真,比较了这2种最大功率点跟踪方法的仿真曲线。结果表明,采用改进的扰动观察法的光伏电池控制系统能更快速跟踪最大功率点,且在最大功率点处稳定性较好。     

改进扰动观察法的光伏阵列最大功率点跟踪算法

为了更好地跟踪光伏阵列的最大功率点,分析单个光伏电池的物理特性,建立光伏阵列的Matlab仿真模型,分析光伏阵列随光照温度不同而变化的P-U、U-I特性.针对系统在工作工况发生变化时跟踪情况的不同,对传统的扰动观察法做了变步长的寻优算法,并结合调整策略搭建光伏系统最大功率点跟踪的仿真实验模型.结果表明该算法可以快速准确地跟踪最大功率点,稳态效果好,能够更好地提高光伏发电最大功率点跟踪系统的跟踪性能.     

A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current-mode boost converter with bifurcation control

This paper presents a novel fuzzy logic control technique tuned by particle swarm optimization (PSO-FLC) for maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed PV system composes of a current-mode boost converter (CMBC) with bifurcation control. An optimal slope compensation technique is used in the CMBC to keep the system adequately remote from the first bifurcation point in spite of nonlinear characteristics and instabilities of this converter. The proposed PSO technique allows easy and more accurate tuning of FLC compared with the trial-and-error based tuning. Consequently, the proposed PSO-FLC method provides faster tracking of maximum power point (MPP) under varying light intensities and temperature conditions. The proposed MPPT technique is simple and particularly suitable for PV system equipped with CMBC. Experimental results are shown to confirm superiority of the proposed technique comparing with the conventional PVVC technique and the trial-and-error based tuning FLC.     

无线电能传输系统最高效率点控制策略的研究

无线电能传输在实际应用中的关键性问题是系统的传输效率和负载电压的稳定。综合考虑线圈谐振频率、耦合系数、负载阻抗、电源内阻,应用互感耦合理论分析了系统传输效率和电压增益与耦合系数和负载电阻的关系。对比了四种常见的无线传能系统闭环控制方法,并提出最高效率点控制策略,保证系统效率最大化同时调节负载输出电压。     

Intelligent control system design for UAV using a recurrent wavelet neural network

This paper aims to propose an efficient control algorithm for the unmanned aerial vehicle (UAV) motion control. An intelligent control system is proposed by using a recurrent wavelet neural network (RWNN). The developed RWNN is used to mimic an ideal controller. Moreover, based on sliding-mode approach, the adaptive tuning laws of RWNN can be derived. Then, the developed RWNN control system is applied to an UAV motion control for achieving desired trajectory tracking. From the simulation results, the control scheme has been shown to achieve favorable control performance for the UAV motion control even it is subjected to control effort deterioration and crosswind disturbance.     

粒子群算法在光伏系统最大功率点跟踪中的应用

介绍了光伏电池的特性,并在Matlab/Simulink中进行建模仿真研究.针对局部遮阴条件下光伏阵列的P-U特性呈现多个极值点,导致常规的最大功率点跟踪算法失效的问题,提出了一种基于粒子群算法(PSO)的最大功率点跟踪(MPPT)控制方法.仿真结果表明,该方法能够快速、准确地跟踪光伏阵列的最大功率点,具有较好的控制精度,有效地提高了光伏阵列的输出效率.     

Intelligent transportation control system design using wavelet neural network and PID-type learning algorithms

In this paper, an intelligent transportation control system (ITCS) using wavelet neural network (WNN) and proportional-integral-derivative-type (PID-type) learning algorithms is developed to increase the safety and efficiency in transportation process. The proposed control system is composed of a neural controller and an auxiliary compensation controller. The neural controller acts as the main tracking controller, which is designed via a WNN to mimic the merits of an ideal total sliding-mode control (TSMC) law. The PID-type learning algorithms are derived from the Lyapunov stability theorem, which are utilized to adjust the parameters of WNN on-line for further assuring system stability and obtaining a fast convergence. Moreover, based on H_∞ control technique, the auxiliary compensation controller is developed to attenuate the effect of the approximation error between WNN and ideal TSMC law, so that the desired attenuation level can be achieved. Finally, to investigate the effectiveness of the proposed control strategy, it is applied to control a marine transportation system and a land transportation system. The simulation results demonstrate that the proposed WNN-based ITCS with PID-type learning algorithms can achieve favorable control performance than other control methods.     

Intelligent identification and control for autonomous guided vehicles using adaptive fuzzy-based algorithms

Fuzzy logic was first suggested as the mechanism by which humans drive cars. This paper addresses the use of fuzzy logic and algorithms towards the intelligent autonomous motion control of land vehicles. To cope with vehicle complexities, internal parametric changes, and with unpredictable environmental effects, the controllers that are presented, whilst heuristic in nature, are self-organizing or self-learning in that they generate automatically by observation an experiential rule base that models the vehicle, and via an appropriate performance index an optimal control rule base that is robust to large parametric changes. The methodology presented is applicable to any complex process which is too difficult to model or control using conventional methods, or which has relied on the experience of a human operator. An overview of fuzzy logic and static fuzzy logic control (akin to expert systems) is provided, together with illustrative examples.     

Intelligent tracking control for robot manipulator including actuator dynamics via TSK-type fuzzy neural network

In this paper, a Takagi-Sugeno-Kang-type fuzzy-neural-network control (T-FNNC) scheme is constructed for an n-link robot manipulator to achieve high-precision position tracking. According to the concepts of mechanical geometry and motion dynamics, the dynamic model of an n-link robot manipulator including actuator dynamics is introduced initially. However, it is difficult to design a suitable model-based control scheme due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, a T-FNNC system without the requirement of prior system information and auxiliary control design is investigated to the joint position control of an n-link robot manipulator for periodic motion. In this model-free control scheme, a five-layer fuzzy-neural-network is utilized for the major control role, and the adaptive tuning laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. In addition, experimental results of a two-link robot manipulator actuated by dc servomotors are provided to verify the effectiveness and robustness of the proposed T-FNNC methodology.     

改进型双馈风力发电机最大功率点跟踪策略

针对传统双馈风力发电机最大功率点跟踪极值搜索策略将正弦信号作为搜索信号而很难将正弦信号从总的输出信号中区分出来的问题,提出了一种利用风湍流作为极值搜索信号的改进型最大功率点跟踪策略。该改进策略对叠加风湍流的叶尖速比和功率系数进行傅里叶变换,获得相位差信息,从而确定叶尖速比变化方向,使双馈风力发电机达到最佳运行工作点。仿真结果表明,该改进策略可控制风力发电机转速较好地跟踪风速变化,实现了额定风速以下运行区域的最大风能捕获。     

Climatic sensorless maximum power point tracking in PV generation systems

The problem of maximum power point tracking (MPPT) is addressed for photovoltaic (PV) arrays considered in a given panel position. The PV system includes a PV panel, a PWM boost power converter and a storing battery. Although the maximum power point (MPP) of PV generators varies with solar radiation and temperature, the MPPT is presently sought without resorting to solar radiation and temperature sensors in order to reduce the PV system cost. The proposed sensorless control solution is an adaptive nonlinear controller involving online estimation of uncertain parameters, i.e. those depending on radiation and temperature. The adaptive control problem at hand is not a standard one because parameter uncertainty affects, in addition to system dynamics, the output-reference trajectory (expressing the MPPT purpose). Therefore, the convergence of parameter estimates to their true values is necessary for MPPT achievement. It is formally shown, under mild assumptions, that the developed adaptive controller actually meets the MPPT objective.     

基于神经网络的多层控制系统智能故障诊断

阐述了智能故障诊断技术的特点，针对复杂多层次控制系统，提出了一种分层模块化设计方法及模块间故障变量传播的搜索模式；探讨了基于神经网络与其它理论方法相结合的几种智能化诊断模式，对应用的方法，特点作了进一步分析。     

光伏发电双轴逐日跟踪系统设计与实现

为提高太阳能板的光电转化率,采用 自动跟踪和人工调节两种模式相结合的方式,设计了 一种以STC12C5A60S2单片机为控制核心的光伏发电双轴逐日跟踪系统.人工模式分为人工按键控制和无线远程控制模式;自动模式又分为光电跟踪和视日运行轨迹跟踪模式,两者通过阴晴检测电路判断阴天或者晴天.阴天时,系统利用DS1302时钟芯片...