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。     

Output feedback control of sensorless photovoltaic systems,with maximum power point tracking

This paper presents an output feedback control of sensorless photovoltaic systems with maximum power point tracking (MPPT). The system consists of a Photovoltaic Generator (PVG) which supplies a DC centrifugal pump, via a DC/DC boost converter. This later being connected to the PVG by a long PV cable. Generally, PV systems are established near the control unit of the converter. The MPPT methods and control laws are based on the PVG voltage and current measurements. However, PV arrays must be located in a site that guarantees good solar radiation. In most cases, such a site is at great distance from the control unit. Thus, on the one hand, the PVG voltage and current measurements become difficult and, on the other hand, the PV cable parameters could significantly effect the MPPT control accuracy if only voltage and current measurements in the cable converter side are used. To overcome these issues, a state estimation for PV systems is considered in this paper. A high gain observer is designed on the basis of a PV system model that accounts for PV cable parameters. It provides estimates of PVG output voltage and current using only current and voltage measurements in the converter side of the cable. A backstepping controller is then synthesized with the view of ensuring the MPPT objective. The output feedback control convergence is formally analyzed and its performances are illustrated by simulation.     

光伏发电最大功率跟踪系统的研究

针对光伏发电系统最大功率跟踪问题,提出了一种新的自适应模糊控制器方案.理论证明,利用在线调整算法改变模糊规则使光伏发电系统达到了全局稳定,基于这种结构动态性能好,鲁棒性强,最后采用MATLAB7.0的Simulink工具对光伏发电最大功率跟踪系统进行仿真,结果证明了其有效性.     

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。     

粒子群优化鱼群算法及其在光伏系统最大功率点跟踪中的应用

将标准粒子群优化算法中的速度惯性、粒子个体的记忆因素和粒子间学习交流因素等几个特征引入人工鱼群算法,提出了粒子群优化鱼群算法。在新算法中,鱼群的游动具有了速度惯性的特征,并且其行为模式被扩充为追尾、聚群、记忆、交流以及觅食。通过仿真分析,验证了粒子群优化鱼群算法比两种基本算法具有更快的收敛速度和更高的寻优精度,且性能稳定。最后将所提出的粒子群优化鱼群算法应用于局部遮阴情况下的光伏发电系统最大功率点跟踪,实验表明,该算法可以在很短时间内以很高精度寻得不均匀光照系统的最大功率点。     

Intelligent maximum power tracking control of a PMSG wind energy conversion system

A comparative control study for a maximum power tracking strategy of variable speed wind turbine is provided. The system consists of a direct drive permanent magnet synchronous generator (PMSG) and an uncontrolled rectifier followed by a DC/DC switch‐mode step down converter connected to a DC load. The buck converter is used to catch the maximum power from the wind by generating an efficient duty cycle. Distinct Maximum Power Point Tracking (MPPT) algorithms are analyzed and compared: a classical Proportional‐Integral controller (PI) and two based Fuzzy Logic Controllers (FLC), including a conventional Fuzzy‐PI and an Adaptive FLC‐PI. The main aim of the presented study is to develop an advanced control scheme for wind generators to ensure a high level operating of the system and a maximum power extraction from the wind. This is achieved by analyzing the behavior of the system under fluctuating wind conditions employing Matlab Simpower Systems tool. Simulation results confirm that the Adaptive FLC‐PI controller algorithm has better performances in terms of dynamic response and efficiency especially in comparison with the ones of a PI controller under variable wind speed.     

Application of sliding mode control for maximum power point tracking of solar photovoltaic systems: A comprehensive review

A robust maximum power point tracking (MPPT) control is of paramount importance in the performance enhancement and the optimization of photovoltaic systems (PVSs). Solar panel exhibits nonlinear behavior under real climatic conditions and output power fluctuates with the variation in solar irradiance and temperature. Therefore, a control strategy is requisite to extract maximum power from solar panels under all operating conditions. Sliding mode control (SMC) is extensively used in non-linear control systems and has been implemented in PVSs to track maximum power point (MPP). The objective of this work is to classify, scrutinize and review the SMC techniques used to extract maximum power from PVSs in both off-grid and grid connected applications. The first order, perturb and observe, incremental conductance, linear expression based sliding mode control algorithms and their adaptive forms are discussed in detail. The advanced form of SMC, terminal sliding mode control (TSMC), super twisting theorem (STT) and artificial intelligent (AI) algorithm based are also presented with the focused application of MPPT of PVSs. A tabular comparison is provided at the end of each category to help the users to choose the most appropriate method for their PV application. It is anticipated that this work will serve as a reference and provides important insight into MPPT control of the PV systems.     

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.     

Fuzzy observer-based control for maximum power-point tracking of a photovoltaic system

This paper presents a novel fuzzy control design method for maximum power-point tracking (MPPT) via a Takagi and Sugeno (TS) fuzzy model-based approach. A knowledge-dynamic model of the PV system is first developed leading to a TS representation by a simple convex polytopic transformation. Then, based on this exact fuzzy representation, a H_∞ observer-based fuzzy controller is proposed to achieve MPPT even when we consider varying climatic conditions. A specified TS reference model is designed to generate the optimum trajectory which must be tracked to ensure maximum power operation. The controller and observer gains are obtained in a one-step procedure by solving a set of linear matrix inequalities (LMIs). The proposed method has been compared with some classical MPPT techniques taking into account convergence speed and tracking accuracy. Finally, various simulation and experimental tests have been carried out to illustrate the effectiveness of the proposed TS fuzzy MPPT strategy.     

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.     

基于RBF神经网络与Smith预估补偿的智能PID控制

针对工业控制中普遍存在的大滞后现象,提出了一种将RBF神经网络算法和Smith预估补偿算法与传统的PID控制器相结合的智能RBF-Smith-PID控制策略。该方法利用RBF神经网络的在线学习、控制参数自整定能力,和Smith预估补偿对纯滞后系统的良好控制,有效地克服了常规PID控制的缺陷,提高了系统的鲁棒性和自适应性,对纯滞后系统起到了良好的控制。     

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.     

高斯基神经网络的非线性PID控制方法

针对二阶非线性系统,提出了一种用高斯基函数作为神经元激励函数的PID（Proportion-Integral-Derivative）控制方法。该方法用高斯基函数模拟PID参数随误差变化的曲线,用神经网络算法在线调整各模拟曲线的系数,从而构造出具有非线性特征的PID控制策略,实现了基于高斯基神经网络的非线性PID智能控制方法。计算机仿真结果表明,该方法具有良好的非线性控制效果,因此在工业领域具有广泛的应用前景。     

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.