改进粒子群算法在光伏阵列多峰值MPPT中的应用

在局部遮荫下,光伏阵列的P-U特性曲线存在多个极值点,常规最大功率跟踪(MPPT)算法在光伏阵列多峰值MPPT应用中将失效。粒子群算法(PSO)有良好的全局搜索能力,被应用于光伏阵列多峰值MPPT,但是PSO存在收敛精度低和收敛不稳定性的缺点。为了提高PSO算法的收敛稳定性和收敛精度,引入非线性策略对PSO算法进行改进,Matlab仿真结果表明,改进的粒子群算法在多峰值MPPT应用中可以稳定、准确的跟踪光伏阵列的最大输出功率。     

基于改进PSO算法的光伏发电系统MPPT控制策略

光伏发电系统运行在局部阴影条件下,其P-V曲线呈现多个峰值,为了保证光伏发电系统能够工作在最大功率点下,提出一种改进粒子群优化(PSO)算法的最大功率点跟踪(MPPT)的控制策略.改进算法采用非线性自适应的学习因子和加速因子同时采用随机惯性权重.仿真实验和真实实验验证所提出的算法在光伏发电系统的MPPT控制中具有较快的...     

局部阴影遮蔽下光伏阵列MPPT算法的研究综述

光伏阵列作为太阳能光伏发电系统的基本发电单元,在局部阴影条件下,它的输出特性发生改变,相应的功率曲线含有多个局域峰值,使常规的最大功率点跟踪算法很难准确地跟踪到真正的最大功率点,在分析常规最大功率点跟踪方法(恒定电压法、扰动观测法、电导增量法)的基础上,对多峰值最大功率点跟踪方法做了比较全面的比较和分析(模糊免疫算法、粒子群优化算法PSO等),为实现光伏阵列在部分遮蔽下实现最大MPPT设计与实现提供参考。     

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.     

基于分组粒子群的光伏最大功率点跟踪方法

针对光伏发电系统在复杂遮阴条件下,光伏输出P-V特性曲线呈现高度非线性,采用基于分组粒子群算法（particle swarm optimization, PSO）和优化的扰动观察法(perturb and observe, P&O)相结合的MPPT（maximum power point tracking）算法进行光伏发电系统输出功率的提升。提出的最大功率点算法分为两个阶段,首先通过将混合蛙跳算法(shuffled frog leaping algorithm, SFLA)的分组思想引入到传统粒子群算法,并采用改进后算法实现近似全局最大功率点的快速搜索,以加快最大功率点跟踪的收敛速度和稳定性。然后,采用优化的扰动观察法实现最大功率点附近的动态精确跟踪,同时减少后续最大功率点跟踪过程中的计算量。通过在不同阶段发挥两种MPPT算法的各自优点来提高光伏最大功率点跟踪控制的效率。最后进行光伏系统遮阴条件变化的仿真实验,与传统粒子群算法相比,提出MPPT方法具有较快的跟踪速度和稳定的功率输出。     

基于粒子群多峰值MPPT算法的光伏系统研究

局部遮荫时,光伏阵列P-V特性曲线呈现多个峰值点,传统算法难以追踪到最大功率点(MPPT),针对此问题提出了粒子群多峰值MPPT算法,该算法通过粒子群搜索最大功率点处电流,并控制光伏阵列输出电流,从而实现最大功率点跟踪;仿真结果表明粒子群多峰值MPPT算法可以用在光伏并网发电系统中,并且该算法比传统算法收敛速度更快,稳态效果更好。     

PSOEM算法在不均匀光照下最大功率点跟踪中的研究

针对目前光伏发电技术的研究现状,分析了在不均匀光照下的最大功率点跟踪(MPPT)的原理和基本算法。提出了带扩展记忆的粒子群算法(PSO with Extended Memory,简称PSOEM),与标准粒子群算法的性能进行了比较,针对不均匀光照下光伏阵列输出特性曲线存在多个局部峰值,根据PSOEM在多峰函数的全局寻优、多变量寻优方面的良好性能,将其应用到不均匀光照下光伏阵列的最大功率跟踪控制中,解决了局部遮阴下多峰寻优的问题。     

基于PI调节占空比的自适应MPPT方法

最大功率点跟踪(MPPT)技术是提高光伏发电效率的重要途径之一;扰动观测法是MPPT控制中最常用的方法,针对其无法兼顾跟踪速度与最大功率点跟踪过程的震荡问题,提出了一种基于PI调节占空比的自适应MPPT方法,该方法针对占空比采用基于PI调节的自适应策略;通过Simulink建模仿真,与其他方法进行对比分析,结果显示了该方法可显著地提高了最大功率点跟踪的速度与精度。     

基于粒子群优化的光伏系统MPPT控制方法

局部遮阴条件下光伏阵列P-V特性引起的多个极值点使常规的最大功率点跟踪(MPPT)算法失效。针对上述问题,提出一种基于粒子群优化算法的控制方法,以解决局部遮阴下的最大功率跟踪问题。实验结果显示,光伏模板的输出电压被稳定地控制在最大功率点附近,证明算法是有效的。     

粒子群组合算法跟踪局部遮荫下光伏GMPPT研究

局部遮荫条件下,标准粒子群算法跟踪光伏阵列全局最大功率点容易陷入局部最优或过早收敛等问题。因此,通过引入混沌运动、模拟退火、遗传算法等来改善标准粒子群算法(PSO),并对1×2串联光伏阵列在5种遮荫下的最大功率点进行跟踪与分析。结果表明,首先,组合算法都克服了PSO容易陷入局部最优的缺陷;其次,组合算法都存在牺牲跟踪时间来提高跟踪精度及稳定性的现象;最后,PSO中引入遗传算法的自然选择机制有助于提高跟踪速度,而混沌运动可帮助PSO提高跟踪精度及稳定性。     

A hybrid intelligent GMPPT algorithm for partial shading PV system

Maximum power extraction for PV systems under partial shading conditions (PSCs) relies on the optimal global maximum power point tracking (GMPPT) method used. This paper proposes a novel maximum power point tracking (MPPT) control method for PV system with reduced steady-state oscillation based on improved particle swarm optimization (PSO) algorithm and variable step perturb and observe (P&O) method. Firstly, the grouping idea of shuffled frog leaping algorithm (SFLA) is introduced in the basic PSO algorithm (PSO–SFLA), ensuring the differences among particles and the searching of global extremum. Furthermore, adaptive speed factor is introduced into the improved PSO to improve the convergence of the PSO–SFLA under PSCs. And then, the variable step P&O (VSP&O) method is used to track the maximum power point (MPP) accurately with the change of environment. Finally, the superiority of the proposed method over the conventional P&O method and the standard PSO method in terms of tracking speed and steady-state oscillations is highlighted by simulation results under fast variable PSCs.     

分段变步长MPPT算法在风力发电系统中的应用

针对传统定步长爬山搜索(HCS)法在风力发电系统最大功率跟踪(MPPT)控制过程中的快速性和准确性矛盾,提出了一种基于爬山搜索法和模糊控制的分段变步长MPPT算法.该算法根据发电机P-ω特性曲线对最大功率点(MPP)跟踪过程进行分段,使系统能够根据工作点所在的区域选择合适的跟踪算法和步长完成最大功率跟踪.在Matlab/Simulink中分别对提出的模糊分段变步长算法和传统爬山搜索法进行了仿真.仿真结果表明:所提算法明显地改善了系统跟踪MPP的速度和稳态精度,在MPPT方面明显优于传统的爬山搜索法.     

采用自适应扰动观察法的光伏并网系统最大功率点跟踪

提出了一种自适应扰动观察（P＆O）算法,用于在不同天气条件下太阳能光伏（PV）并网系统的最大功率点跟踪（MPPT）控制策略。该策略对于从太阳能光伏电池板中,获取最大的功率输出是十分重要的。利用一种依赖于功率变化的可变的扰动步长,提出了改进的自适应扰动观察算法。最后将通过仿真所得到的数据与传统的扰动观察算法进行了比较,结果表明所提出MPPT算法的收敛值和速度得到了改善,稳定时间缩短25%,稳态值提高20%以上,在太阳能光伏并网系统的最大功率点跟踪时是有效而实用的。     

三点比较法在风力发电系统中的应用

由于风的随机性,充分利用风能是风力发电系统的本质要求,在研究了各种MPPT算法的基础上,提出将三点比较法应用于风力发电系统,介绍了风力机运行特性和最大功率点跟踪(MPPT)的原理,分析三点比较控制算法在应用到风力发电系统中的缺点和不足,并在此基础上提出了改进型的三点比较法.采用Matlab软件进行仿真实验,仿真结果表明改进型三点比较法具有良好的收敛性和抗干扰性,同时证明了改进型的三点比较法的正确性和可行性.     

A new maximum power tracking in PV system during partially shaded conditions based on shuffled frog leap algorithm

Abstract

Maximum Power Point Trackers (MPPTs) are power electronic conditioners used in photovoltaic (PV) system to ensure that PV structures feed maximum power for the given ambient temperature and sun’s irradiation. When the PV panels are shaded by a fraction due to any environment hindrances then, conventional MPPT trackers may fail in tracking the appropriate peak power as there will be multi power peaks. In this work, a shuffled frog leap algorithm (SFLA) is proposed and it successfully identifies the global maximum power point among other local maxima. The SFLA MPPT is compared with a well-entrenched conventional perturb and observe (P&O) MPPT algorithm and a global search particle swarm optimisation (PSO) MPPT. The simulation results reveal that the proposed algorithm is highly advantageous than P&O, as it tracks nearly 30% more power for a given shading pattern. The credible nature of the proposed SFLA is ensured when it outplays PSO MPPT in convergence. The whole system is realised in MATLAB/Simulink environment.     

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.     

Development and analysis of adaptive fuzzy controllers for photovoltaic system under varying atmospheric and partial shading condition

Control of power electronics converters used in PV system is very much essential for the efficient operation of the solar system. In this paper, a modified incremental conduction maximum power point tracking (MPPT) algorithm in conjunction with an adaptive fuzzy controller is proposed to control the DC–DC boost converter in the PV system under rapidly varying atmospheric and partial shading conditions. An adaptive hysteresis current controller is proposed to control the inverter. The proposed current controller provides constant switching frequency with less harmonic content compared with fixed hysteresis current control algorithm and sinusoidal PWM controller. The modeling and simulation of PV system along with the proposed controllers are done using MATLAB/SIMSCAPE software. Simulation results show that the proposed MPPT algorithm is faster in transient state and presents smoother signal with less fluctuations in steady state. The hardware implementation of proposed MPPT algorithm and inverter current control algorithms using Xilinx spartran-3 FPGA is also presented. The experimental results show satisfactory performance of the proposed approaches.