Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller （NFC） in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.

Comparative assessment of maximum power point tracking procedures for photovoltaic systems

The fast growing demands and increasing awareness for the environment, PV systems are being rapidly installed for numerous applications.However, one of the important challenges in utilizing a PV source is the maximum power harnessing using various maximum power point tracking techniques available. With the large number of MPPT techniques, each having some merits and demerits, confusion is always there for their proper selection. Discussion on various proposed procedures for maximum power point tracking of photovoltaic array has been done. Based on different parameters analysis of MPPT techniques is carried out. This assessment will serve as a suitable reference for selection, understanding different ways and means of MPPT.

Overview of Maximum Power Point Tracking Control Methods for PV Systems

Maximum power point tracking (MPPT) controllers play an important role in photovoltaic systems. They maximize the output power of a PV array for a given set of conditions. This paper presents an overview of the different MPPT techniques. Each technique is evaluated on its ability to detect multiple maxima, convergence speed, ease of implementation, efficiency over a wide output power range, and cost of implementation. The perturbation and observation (P & O), and incremental conductance (IC) algorithms are widely used techniques, with many variants and optimization techniques reported. For this reason, this paper evaluates the performance of these two common approaches from a dynamic and steady state perspective.

Variable Parameter Nonlinear Control for Maximum Power Point Tracking Considering Mitigation of Drive-train Load

Since mechanical loads exert a significant influence on the life span of wind turbines, the reduction of transient load on drive-train shaft has received more attention when implementing a maximum power point tracking(MPPT) controller.Moreover, a trade-off between the efficiency of wind energy extraction and the load level of drive-train shaft becomes a key issue. However, for the existing control strategies based on nonlinear model of wind turbines, the MPPT efficiencies are improved at the cost of the intensive fluctuation of generator torque and significant increase of transient load on drive train shaft. Hence, in this paper, a nonlinear controller with variable parameter is proposed for improving MPPT efficiency and mitigating transient load on drive-train simultaneously. Then,simulations on FAST(Fatigue, Aerodynamics, Structures, and Turbulence) code and experiments on the wind turbine simulator(WTS) based test bench are presented to verify the efficiency improvement of the proposed control strategy with less cost of drive-train load.

Comprehensive review and performance evaluation of maximum power point tracking algorithms for photovoltaic system

A photovoltaic array is environmentally friendly and a source of unlimited energy generation.However,it is presently a costlier energy generation system than other non-renewable energy sources.The main reasons are seasonal variations and continuously changing weather conditions,which affect the amount of solar energy received by the solar panels.In addition,the non-linear characteristics of the voltage and current outputs along with the operating environment temperature and variation in the solar radiation decrease the energy conversion capability of the photovoltaic arrays.To address this problem,the global maxima of the PV arrays can be tracked using a maximum power point tracking algorithm(MPPT)and the operating point of the photovoltaic system can be forced to its optimum value.This technique increases the efficiency of the photovoltaic array and minimizes the cost of the system by reducing the number of solar modules required to obtain the desired power.However,the tracking algorithms are not equally effective in all areas of application.Therefore,selecting the correct MPPT is very critical.This paper presents a detailed review and comparison of the MPPT techniques for photovoltaic systems,with consideration of the following key parameters:photovoltaic array dependence,type of system(analog or digital),need for periodic tuning,convergence speed,complexity of the system,global maxima,implemented capacity,and sensed parameter(s).In addition,based on real meteorological data(irradiance and temperature at a site located in Addis Ababa,Ethiopia),a simulation is performed to evaluate the performance of tracking algorithms suitable for the application being studied.Finally,the study clearly validates the considerable energy saving achieved by using these algorithms.

Maximum Power Point Tracking Control Using Neural Networks for Stand-Alone Photovoltaic Systems

The employment of maximum power point tracking techniques in the photovoltaic power systems is well known and even of immense importance. There are various techniques to track the maximum power point reported in several literatures. In such context, there is an increasing interest in developing a more appropriate and effective maximum power point tracking control methodology to ensure that the photovoltaic arrays guarantee as much of their available output power as possible to the load for any temperature and solar radiation levels. In this paper, theoretical details of the work, carried out to develop and implement a maximum power point tracking controller using neural networks for a stand-alone photovoltaic system, are presented. Attention has been also paid to the command of the power converter to achieve maximum power point tracking. Simulations results, using Matlab/Simulink software, presented for this approach under rapid variation of insolation and temperature conditions, confirm the effectiveness of the proposed method both in terms of efficiency and fast response time. Negligible oscillations around the maximum power point and easy implementation are the main advantages of the proposed maximum power point tracking (MPPT) control method.

Maximum Power Point Tracking Using the Incremental Conductance Algorithm for PV Systems Operating in Rapidly Changing Environmental Conditions

Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.

Maximum Power Point Tracking With Fractional Order High Pass Filter for Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cell(PEMFC) is widely recognized as a potentially renewable and green energy source based on hydrogen. Maximum power point tracking(MPPT) is one of the most important working conditions to be considered. In order to improve the performance such as convergence and robustness under disturbance and uncertainty,a fractional order high pass filter(FOHPF) is applied for the MPPT controller design based on the traditional extremum seeking control(ESC). The controller is designed with integerorder integrator(IO-I) and low pass filter(IO-LPF) together with fractional order high pass filter(FOHPF), by substituting the normal HPF in the original ESC system. With this FOHPF ESC,better convergence and smoother performance are achieved while maintaining the robust specifications. First, tracking stability is discussed under the commensurate-order condition. Then,simulation results are included to validate the proposed new FOHPF ESC scheme under disturbance. Finally, comparison results between FOHPF ESC and the traditional ESC method are also provided.

Parasitic Effects on the Performance of DC-DC SEPIC in Photovoltaic Maximum Power Point Tracking Applications

This paper presents an analysis of the effect of parasitic resistances on the performance of DC-DC Single Ended Pri- mary Inductor Converter (SEPIC) in photovoltaic maximum power point tracking (MPPT) applications. The energy storage elements incorporated in the SEPIC converter possess parasitic resistances. Although ideal components significantly simplifies model development, but neglecting the parasitic effects in models may sometimes lead to failure in predicting first scale stability and actual performance. Therefore, the effects of parasitics have been taken into consideration for improving the model accuracy, stability, robustness and dynamic performance analysis of the converter. Detail mathematical model of SEPIC converter including inductive parasitic has been developed. The performance of the converter in tracking MPP at different irradiance levels has been analyzed for variation in parasitic resistance. The converter efficiency has been found above 83% for insolation level of 600 W/m2 when the parasitic resistance in the energy storage element has been ignored. However, as the parasitic resistance of both of the inductor has increased to 1 ohm, a fraction of the power managed by the converter has dissipated;as a result the efficiency of the converter has reduced to 78% for the same insolation profile. Although the increasing value of the parasitic has assisted the converter to converge quickly to reach the maximum power point. Furthermore it has also been observed that the peak to peak load current ripple is reduced. The obtained simulation results have validated the competent of the MPPT converter model.

基于多策略混合改进MVO算法的光伏多峰MPPT研究

光伏阵列的电功率输出在局部遮荫工况下具有多峰特性,且随外界环境的变化而变化.为实现高效电能输出,利用多元宇宙优化(MVO)算法在解决低维度、小规模优化问题中的突出优势进行最大功率点跟踪(MPPT),并融合多种策略对其存在的缺陷进行改进.采用拉丁超立方抽样策略初始化宇宙种群,并对按照轮盘赌策略随机交换的宇宙实施柯西变异,提高宇宙种群的多样性.同时引入莱维飞行式量子粒子群优化(QPSO)算法,且对虫洞存在概率及旅行距离率进行自适应调整,以增强算法的全局勘探及局部开发能力.Matlab仿真结果表明,相比其他算法,采用该算法的MPPT时间减少了45%以上,精度亦有所提高,从而具有更优的MPPT性能,可有效提高光伏发电效率.

Improved MPPT Control Based on the Reduction of Tracking Range

考虑到基于转矩调整的改进方法存在难以获取最优调整状态的问题，该文针对传统功率曲线法提出收缩风机转速的跟踪区间、减小跟踪路程的改进思路，以优化风机最大功率点跟踪的性能。在此基础上，设计出根据平均风速，周期性的调整起始发电转速的改进功率曲线法。其设计机理在于风速平均值恰好反映了风速及风能量集中分布的区间。因此，最优转速跟踪区间与平均风速存在数量上的直接关系而变得容易预估，且无需迭代搜索。与目前基于调整转矩的改进方法相比，该文所提方法不仅提高了风能捕获效率，且简单易行。最后，通过对模拟风速序列的仿真计算与比较分析，验证了该方法的有效性和先进性。

基于改进变步长爬山法的潮流能发电MPPT算法

针对传统变步长爬山法在潮流能发电系统中对潮流流速突变和随机性波动的适应能力较差,易出现扰动方向判断错误和速度追踪过快的问题,在变步长爬山法的基础上提出一种改进变步长最大功率追踪(MPPT)算法。该方法能够准确判断多种最大功率跟踪的情况并采用两次采样点下的功率和转速的变化程度确定下一时刻步长的大小,自动根据获能系数大小进行分阶段调节,实现对潮流流速突变情况的应对,从而实现最大功率的跟踪。在MATLAB/Simulink环境下搭建基于PID控制与自抗扰控制的仿真系统进行仿真试验,结果表明,改进的变步长爬山法不仅能够实现最大功率的跟踪,而且对潮流流速的随机性波动和突变情况的适应能力更强,具有良好的稳态特性。

面向光伏能量收集应用的实时MPPT预估算法研究

对于环境中存在的各种类型能量源,其往往具有不同的阻抗特性以及输出功率范围。为了提高能量收集系统的能量萃取能力,合理的接口电路设计是关键。基于此,通过对环境中光伏(Photovoltaic,PV)能量源微弱直流特性以及高效率收集和转化的研究,在传统开路电压法(Open-Circuit Voltage,OCV)的基础上,结合输入电压纹波控制,提出了一种可实时最大功率点追踪(Maximum Power Point Tracking,MPPT)的预估算法。该预估算法根据能量源的输出特性,采用了分数开路电压法(Fractional Open-Circuit Voltage,FOCV),并根据纹波大小动态调节变换器的工作模式,实现阻抗匹配。为了尽可能减小因采样带来的能量损失,采用可片上全集成的较小的采样电容,并逐周期的进行开路电压采样和计算,实现了对源功率变化的高精度追踪。仿真结果表明,所提出的追踪算法能够实时监测能量源的状态,具有高的追踪速度和追踪精度,且采样时间仅需100 ns。能量源功率在1μW~10 mW范围内变化时,最短的追踪时间仅需4.37μs,追踪精度可达99.7%。

基于APID-RBF神经网络的光伏MPPT方法

针对光照强度急速变化和局部阴影时光伏发电系统最大功率点追踪响应速度慢、多峰值等问题,提出一种基于RBF神经网络与自适应PID控制相结合的控制方法。首先,采用RBF神经网络对环境的实时变化直接跟踪光伏最大功率点。然后,利用自适应PID的辅助修正,抑制光伏电池输出功率的波动。神经网络能提升在复杂环境下的跟踪速度,自适应PID能增强对神经网络误差的消除能力,提升跟踪精度。仿真结果表明,APIDRBF双控策略具有稳态性能高和控制精度高等优点,能有效提高光伏发电效率和稳定性。

基于Labview的模拟光伏MPPT研究

根据光伏电池的物理数学模型,以及基于Boost变换器稳态工作原理推导出来的光伏占空比扰动控制机理和扰动观察法控制流程,采用电压源串联电阻替代光伏电池板,并采用STC15W4K32S4单片机作为主控单元通过编程实现模拟光伏MPPT爬山法控制策略,采用Labview对模拟光伏的电压、电流的采样,通过Labview图形编程实现对模拟光伏系统工作状态和MPPT控制策略实施效果的实时直观呈现。实验结果表明,利用Labview强大的数据采集、处理、图形功能能够方便地把复杂系统状态和控制策略的实施效果实时直观地呈现出来,为光伏等系统的研究开发提供方便,对光伏控制器的研究设计具有实际意义。

基于分段控制的光伏全工况MPPT研究

光伏(PV)阵列输出特性随运行环境及自身工况的变化而变化.为满足不同工况下最大功率点跟踪(MPPT)控制需求,在对光伏阵列各工况下输出特性进行分析的基础上,提出了一种改进量子粒子群算法(QPSO)与扰动观察法相结合的MPPT分段控制方法.在跟踪控制初期,采用非一致性自适应变异DCWQPSO算法进行最大功率点全局搜索,使功率点快速收敛至最大功率点附近,提高跟踪速度;在跟踪控制后期,采用闭环模糊控制扰动观察法进行最大功率点局部搜索,提高跟踪精度.Matlab仿真结果表明,该分段控制方法在光伏阵列各工况下仅需0.32 s即可完成MPPT,并保持稳定,比其他控制方法具有更快的跟踪速度及更高的跟踪精度,可有效提高光伏发电效率.

基于拟合反演滑模方法的光伏系统MPPT控制

该文提出一种基于拟合优化的反演滑模控制的混合MPPT方案。该方案首先根据光伏发电系统的P-V特征曲线,使用3次插值函数来拟合最大功率点处的参考电压,然后基于参考电压以及反演滑模控制原理,设计相应的控制器并通过PWM调制控制占空比,使之在不同的环境条件下能迅速有效跟踪最大功率点。最后,通过Matlab/Simulink仿真验证所提出优化方案的有效性,并与其他经典方法进行对比分析。结果显示,该方法跟踪速度快、稳态功率波动小,跟踪性能优异。

基于Fuzzy-PI的直驱永磁风力发电系统MPPT控制

通过对模糊PI控制算法、MPPT算法分析,并对变流器的控制策略进行推导,提出基于Fuzzy-PI控制策略,确定机侧以转速的偏差及其微分为输入,交轴电流为输出,风机达到最大功率跟踪效果;网侧变流器采用电压外环、电流内环双闭环控制策略。通过Simulink系统仿真,结果表明模糊PI控制系统能实现发电机的最大功率跟踪和单位功率因数运行,控制效果优于传统的PI控制策略。

基于Boost变换器的D-PMSG风力发电系统MPPT控制

为了提高直驱永磁风力发电系统的最大功率点跟踪控制性能,通过对Boost占空比变化与风力机输出特性的关系分析,提出一种基于模糊梯度步长爬山搜索法,将风力机输出转速变化量和风力机输出功率变化量作为模糊控制器的输入量,Boost变换器的占空比作为模糊控制器的输出量,实现风力机最大功率点跟踪控制。建立了系统仿真模型并进行仿真验证,结果表明,模糊梯度步长爬山搜索法能实现直驱永磁风力发电系统的最大功率点跟踪控制,控制效果优于传统变步长爬山搜索算法。