光伏系统最大功率点跟踪控制仿真模型

介绍了一种简单实用的光伏系统计算机仿真软件设计方法。通过对太阳能电池的物理模型和电特性的分析计算,建立了太阳能电池的数学模型,并结合S函数的编写,在M atlab/S imu link环境下建立其动态仿真模型。考虑到太阳能的波动性和随机性对太阳电池阵列的影响,该模型具有最大功率点跟踪(MPPT)功能。文中还给出了光伏系统仿真所使用的详细参数。仿真结果表明,利用该模型不需要精确的系统内部特性和结构参数,就可以实时模拟任何功率、电压组合的光伏阵列。     

光伏系统中最大功率跟踪算法仿真研究

光伏组件输出具有非线性特性,并且其输出功率受到光照、温度等因素的影响,为了提高系统效率必要对其输出功率进行跟踪控制。首先根据光伏组件的工程模型结合组件技术参数,利用Matlab/Simlink平台建立了光伏组件的计算机仿真模型,并分析了光照、温度等因素对组件输出特性的影响,然后为了消除不利的影响和克服传统定步长功率跟踪算法的缺点,在跟踪中引入了变步长大功率跟踪算法并有效进行了仿真验证。     

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。     

光伏电池最大功率点跟踪方法的研究

随着能源的耗竭,光伏发电技术越来越受到人们的重视,光伏发电的过程主要就是将接收到的太阳辐射利用光伏电池转变为电能的过程。在光伏发电系统中,为了进一步提高光伏电池的转换效率,需要对光伏电池的最大功率点进行跟踪。本文首先分析了在跟踪控制中常见的恒压控制法,扰动跟踪法和功率数学模型法,比较了它们的优缺点,并基于这三种方法提出了一种改进的跟踪方法。     

光伏并网发电系统最大功率点跟踪算法研究

针对神经网络算法在太阳能电池最大功率点跟踪中收敛速度慢,结果容易陷入局部极小等缺点,提出了一种基于遗传算法优化神经网络来实现最大功率点跟踪的控制方法。该算法利用遗传算法具有全局搜索问题解的特性,通过选择、交叉、变异等遗传操作,实现了神经网络权值优化,克服了神经网络初始权值对控制效果的不利影响。实验结果表明：该算法提高了神经网络在最大功率跟踪中的收敛性与非线性逼近能力,在日照强度、环境温度变化时仍能快速、准确地跟踪到太阳能电池的最大功率点,具有较好的稳定性。     

基于FSVM光伏系统最大功率跟踪算法及仿真研究

针对光伏组件中常用的最大功率跟踪方法存在的不足,提出并建立了模糊支持向量机(FSVM)的最大功率点工作电压预测模型.通过对实测气象光强数据进行的仿真分析表明,与BP神经网络模型相比,FSVM的模型实现了结构风险最小化原则,对未来的样本有较好的泛化能力,具有更高的预测精度和稳定性.     

基于极值搜索的光伏电池最大功率点跟踪仿真

针对光伏发电系统中最大功率点跟踪问题,在太阳能电池的数学模型的基础上建立了PV模块的Matlab仿真模型;考虑到了太阳能的波动性和随机性对太阳电池阵列的影响,利用一种基于极值搜索方法的实时MPPT控制原理,控制Buck DC/DC变换电路,结合S函数在Matlab/Simulink环境下建立其动态仿真模型,实现了光伏电池输出的最大功率跟踪;仿真结果表明,该算法具有较好的动态特性和稳态特性,具有一定的实用价值。     

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。     

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。     

部分遮蔽下改进樽海鞘群算法的光伏系统最大功率跟踪

部分遮蔽条件(partial shading condition)会使光伏系统的功率–电压(P--V)特性曲线出现多个峰值,常规的最大功率跟踪(MPPT)算法易陷入局部最大功率点(LMPP)已不再适用.本文提出了一款新型启发式算法,即改进樽海鞘群算法(MSSA),用于部分遮蔽条件下光伏系统MPPT. MSSA在原有樽海鞘群算法(SSA)的基础上,引入了文化基因算法(memetic algorithm),以樽海鞘链为种群单位,采用多个樽海鞘链同时进行独立寻优,以提高算法全局搜索和局部探索的能力;同时,通过群落中所有樽海鞘间的信息交流,重组产生新的樽海鞘链,以提高算法的收敛稳定性.本文通过3个算例对MSSA的优化性能进行了研究,即恒温恒光照强度、恒温变光照强度和变温变光照强度.仿真结果表明,与增量电导法(INC)、遗传算法(GA)、粒子群算法(PSO)、灰狼算法(GWO)和樽海鞘群算法(SSA)相比,所提算法能在部分遮蔽条件下快速、稳定地获取最大光能.最后,基于d Space的硬件在环实验(HIL)验证了所提算法的硬件可行性.     

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.     

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.     

Application of improved bat algorithm for solar PV maximum power point tracking under partially shaded condition

The power output curves of solar photovoltaic (PV) system have multiple peaks under partially shaded condition. As the same as traditional MPPT (Maximum Power Point Tracking) search methods, bat algorithm often makes optimized results fall into local extremum. So an improved bat algorithm is proposed. Chaos search strategy is introduced in initial arrangement to improve the uniformity and ergodicity of population. Adapting weight is introduced to balance the global searching ability and the local searching ability. Dynamic contraction regain decreases the search range more effectively. Compared with the original algorithm, the rapidity and accuracy of algorithm have been improved. The simulation shows that improved bat algorithm can find the globally optimal point fast, with high precision, under the partially shaded condition.     

局部遮阴下光伏系统MPPT研究与优化

为了解决光伏发电系统中,光伏电池在环境中被树叶、建筑物、云层等遮挡造成局部阴影,导致光伏电池出现运行不稳定和输出功率降低的问题,提出了一种基于改进自适应动态惯性权重并引入粒子寻优目标适应度评判系数的优化粒子群算法(GPPSO).将GPPSO应用于复杂自然环境条件下的最大功率点跟踪(MPPT),结果表明:双重优化后的算法有效提高了局部精确搜索和寻优空间全局收敛能力,在目标函数最优求解过程中,精度和收敛速度都明显提高,较快地适应环境遮阴变化,能够在复杂的自然环境中准确地对光伏发电系统最大功率点进行跟踪,提高光伏系统发电效率.     

频分多址系统分布式强化学习功率控制方法

近年来,深度强化学习作为一种无模型的资源分配方法被用于解决无线网络中的同信道干扰问题。然而,基于常规经验回放策略的网络难以学习到有价值的经验,导致收敛速度较慢;而人工划定探索步长的方式没有考虑算法在每个训练周期上的学习情况,使得对环境的探索存在盲目性,限制了系统频谱效率的提升。对此,提出一种频分多址系统的分布式强化学习功率控制方法,采用优先经验回放策略,鼓励智能体从环境中学习更重要的数据,以加速学习过程;并且设计了一种适用于分布式强化学习、动态调整步长的探索策略,使智能体得以根据自身学习情况探索本地环境,减少人为设定步长带来的盲目性。实验结果表明,相比于现有算法,所提方法加快了收敛速度,提高了移动场景下的同信道干扰抑制能力,在大型网络中具有更高的性能。     

Modified Particle Swarm Optimization technique based Maximum Power Point Tracking for uniform and under partial shading condition

Generation of electricity from solar energy has gained worldwide acceptance due to its abundant availability and eco-friendly nature. Even though the power generated from solar looks to be attractive; its availability is subjected to variation owing to many factors such as change in irradiation, temperature, shadow etc. Hence, extraction of maximum power from solar PV using Maximum Power Point Tracking (MPPT) method was the subject of study in the recent past. Among many methods proposed, Hill Climbing and Incremental Conductance MPPT methods were popular in reaching Maximum Power under constant irradiation. However, these methods show large steady state oscillations around MPP and poor dynamic performance when subjected to change in environmental conditions. On the other hand, bio-inspired algorithms showed excellent characteristics when dealing with non-linear, non-differentiable and stochastic optimization problems without involving excessive mathematical computations. Hence, in this paper an attempt is made by applying modifications to Particle Swarm Optimization technique, with emphasis on initial value selection, for Maximum Power Point Tracking. The key features of this method include ability to track the global peak power accurately under change in environmental condition with almost zero steady state oscillations, faster dynamic response and easy implementation. Systematic evaluation has been carried out for different partial shading conditions and finally the results obtained are compared with existing methods. In addition, simulations results are validated via built-in hardware prototype.     

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.     

Attention control with reinforcement learning for face recognition under partial occlusion

In this paper a new method for handling occlusion in face recognition is presented. In this method the faces are partitioned into blocks and a sequential recognition structure is developed. Then, a spatial attention control strategy over the blocks is learned using reinforcement learning. The outcome of this learning is a sorted list of blocks according to their average importance in the face recognition task. In the recall mode, the sorted blocks are employed sequentially until a confident decision is made. Obtained results of various experiments on the AR face database demonstrate the superior performance of proposed method as compared with that of the holistic approach in the recognition of occluded faces.     

Modeling and simulation of photovoltaic (PV) system during partial shading based on a two-diode model

This paper proposes accurate partial shading modeling of photovoltaic (PV) system. The main contribution of this work is the utilization of the two-diode model to represent the PV cell. This model requires only four parameters and known to have better accuracy at low irradiance level, allowing for more accurate prediction of PV system performance during partial shading condition. The proposed model supports a large array simulation that can be interfaced with MPPT algorithms and power electronic converters. The accurateness of the modeling technique is validated by real time simulator data and compared with the three other types of modeling, namely Neural Network, P&O and single-diode model. It is envisaged that the proposed work is very useful for PV professionals who require simple, fast and accurate PV model to design their systems.