Probabilistic Global Maximum Power Point Tracking Algorithm for Continuously Varying Partial Shading Conditions on Autonomous PV Systems

A photovoltaic (PV) string with multiple modules with bypass diodes frequently deployed on a variety of autonomous PV systems may present multiple power peaks under uneven shading. For optimal solar harvesting, there is a need for a control schema to force the PV string to operate at global maximum power point (GMPP). While a lot of tracking methods have been proposed in the literature, they are usually complex and do not fully take advantage of the available characteristics of the PV array. This work highlights how the voltage at operating point and the forward voltage of the bypass diode are considered to design a global maximum power point tracking (GMPPT) algorithm with a very limited global search phase called Fast GMPPT. This algorithm successfully tracks GMPP between 94% and 98% of the time under a theoretical evaluation. It is then compared against Perturb and Observe, Deterministic Particle Swarm Optimization, and Grey Wolf Optimization under a sequence of irradiance steps as well as a power-over-voltage characteristics profile that mimics the electrical characteristics of a PV string under varying partial shading conditions. Overall, the simulation with the sequence of irradiance steps shows that while Fast GMPPT does not have the best convergence time, it has an excellent convergence rate as well as causes the least amount of power loss during the global search phase. Experimental test under varying partial shading conditions shows that while the GMPPT proposal is simple and lightweight, it is very performant under a wide range of dynamically varying partial shading conditions and boasts the best energy efficiency (94.74%) out of the 4 tested algorithms.

A New Flower Pollination Algorithm Strategy for MPPT of Partially Shaded Photovoltaic Arrays

Photovoltaic(PV)systems utilize maximum power point tracking(MPPT)controllers to optimize power output amidst varying environmental conditions.However,the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation.Under partial shade conditions,the global maximum power point(GMPP)may be missed by most traditional maximum power point tracker.The flower pollination algorithm(FPA)and particle swarm optimization(PSO)are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP.This paper discusses and resolves all issues associated with using the standard FPA method as the MPPT for PV systems.The first issue is that the initial values of pollen are determined randomly at first,which can lead to premature convergence.To minimize the convergence time and enhance the possibility of detecting the GMPP,the initial pollen values were modified so that they were near the expected peak positions.Secondly,in the modified FPA,population fitness and switch probability values both influence swapping between two-mode optimization,which may improve the flower pollination algorithm’s tracking speed.The performance of the modified flower pollination algorithm(MFPA)is assessed through a comparison with the perturb and observe(P&O)method and the standard FPA method.The simulation results reveal that under different partial shading conditions,the tracking time for MFPA is 0.24,0.24,0.22,and 0.23 s,while for FPA,it is 0.4,0.35,0.45,and 0.37 s.Additionally,the simulation results demonstrate that MFPA achieves higher MPPT efficiency in the same four partial shading conditions,with values of 99.98%,99.90%,99.93%,and 99.26%,compared to FPA with MPPT efficiencies of 99.93%,99.88%,99.91%,and 99.18%.Based on the findings from simulations,the proposed method effectively and accurately tracks the GMPP across a diverse set of environmental conditions.

Electrical Power Losses in a Photovoltaic Solar Cell Operating under Partial Shading Conditions

The aim of this paper is to determine the power losses recorded by a PV generator operating under partial shading conditions. These losses are evaluated through two distinct methods. The first method is based on mathematical modeling, while the second is based on Simulink’s physical model. The losses recorded are considerable and increase as a function of the increase in the percentage of shading up to a limit value where they become constant in the case where an ideal by-pass diode is connected in parallel with the modules. This limit value is non-existent in the case where the bypass diode is not ideal, which in fact corresponds to the real model. However, it emerges that the power losses are minimized in a PV system comprising bypass diodes, in particular in the case where the partial shading is considerable.

A Novel Aquila Optimizer Based PV Array Reconfiguration Scheme to Generate Maximum Energy under Partial Shading Condition

This paper develops a real-time PV arrays maximum power harvesting scheme under partial shading condition(PSC)by reconfiguring PV arrays using Aquila optimizer(AO).AO is based on the natural behaviors of Aquila in capturing prey,which can choose the best hunting mechanism ingeniously and quickly by balancing the local exploitation and global exploration via four hunting methods of Aquila:choosing the searching area through high soar with the vertical stoop,exploring in different searching spaces through contour flight with quick glide attack,exploiting in convergence searching space through low flight with slow attack,and swooping through walk and grabbing prey.In general,PV arrays reconfiguration is a problem of discrete optimization,thus a series of discrete operations are adopted in AO to enhance its optimization performance.Simulation results based on 10 cases under PSCs show that the mismatched power loss obtained by AO is the smallest compared with genetic algorithm,particle swarm optimization,ant colony algorithm,grasshopper optimization algorithm,and butterfly optimization algorithm,which reduced by 4.34%against butterfly optimization algorithm.

A Robust Single-Sensor MPPT Strategy for Shaded Photovoltaic-Battery System

A robust single-sensor global maximum power point tracking(MPPT)strategy based on modern optimization for photovoltaic systems considering shading conditions is proposed in this work.The proposed strategy is designed for battery charging applications and direct current(DC)microgrids.Under normal operation,the curve of photovoltaic(PV)output power versus PV voltage contains only a single peak point.This point can be simply captured using any traditional tracking method like perturb and observe.However,this situation is completely different during the shadowing effect where several peaks appear on the power voltage curve.Most of these peaks are local with only a single global.This condition leads to the incapability of traditional tracking approaches to extract the global peak power due to their inability to distinguish between the local and global peak points.They are trapped in the first peak point even when the point is local.Therefore,global tracking approaches based on modern optimization are highly required.A recent marine predators algorithm(MPA)has been used to solve the problem of tracking the global MPP under shadowing influence.Different shadowing scenarios are used to test and evaluate the performance of MPA based tracker.The obtained results are compared with particle swarm optimization(PSO)and ant lion optimizer(ALO).The results of the comparison con-firmed the effectiveness and robustness of the proposed global MPPT-MPA based tracker over PSO and ALO.

利用改进布谷鸟优化算法的光伏全局MPPT方法

为了解决局部阴影下传统最大功率点追踪(maximum power point tracking, MPPT)算法容易陷入局部最优从而降低光伏系统发电效率的问题,本研究提出融合正弦余弦算法和自适应策略的布谷鸟优化算法(cuckoo search algorithm fusing sine cosine algorithm and adaptive strategy, AFCS),并应用于光伏全局MPPT控制中,以改善其收敛速度与追踪精度.设置多种光照情况,并与扰动观察法、花朵授粉算法和粒子群算法进行对比.经过Matlab/Simulink仿真验证,表明本算法拥有较快的收敛速度和较高的追踪精度,在各个光照条件下均能快速追踪到光伏阵列最大功率点,可以有效提高光伏系统的发电效率.

基于定位收缩法的局部阴影条件下光伏最大功率点跟踪

光伏最大功率点跟踪是提高光伏发电效率的重要手段。在局部阴影条件下,光伏阵列的特性曲线呈现多峰形状,常规的传统算法容易陷入局部最优。如何在局部阴影条件下找到全局最大功率点(global maximum power point,GMPP)至关重要。提出了一种定位收缩法(locate and shrink algorithm,LSA),采用收缩边界的思想使得边界逐渐收缩到GMPP。LSA第一阶段提出了一种峰的定位方法,通过自适应采样结合I-V特性曲线能够定位主要峰的占空比范围。定位法能够与其他单峰算法结合,具有较强的扩展性。第二阶段提出了一种基于三点准则的收缩法,能够在单峰范围内通过收缩边界快速找到峰值点,并且具有很强的环境适应性。将LSA与多个算法进行仿真和硬件实验对比,结果表明LSA在跟踪速度、跟踪精度和稳态振荡方面有着明显优势。

基于ICS-IP&O的光伏最大功率跟踪

针对光伏组件存在局部阴影条件(PSC)时,功率输出曲线呈现出的多峰现象,传统最大功率点追踪方法容易陷入局部最优情况。为了解决上述问题,提出一种将自适应布谷鸟搜索(ICS)算法和变步长扰动观察法(IP&O)相结合的复合算法(ICS-IP&O)。对布谷鸟搜索方法(CS)的切换概率、Lévy飞行步长系数进行非线性自适应优化,使其满足迭代前、后期不同的需求,加快收敛进程。在偏好随机游走部分,引入粒子群算法思想,对位置更新公式进行优化,提高其多样性,使算法具有较强的全局搜索随机性,降低陷入局部最优的可能性。算法后期切换成IP&O搜索,减小振荡。通过Simulink进行仿真测试,并与粒子群算法(PSO)、布谷鸟算法(CS)进行对比。结果表明,该算法在静态、局部遮阴、动态遮阴条件下均具有更好的收敛速度和精度。

Maximum Power Point Tracking Technology for PV Systems: Current Status and Perspectives

Maximum power point tracking(MPPT)technology plays a key role in improving the energy conversion efficiency of photovoltaic(PV)systems,especially when multiple local maximum power points(LMPPs)occur under partial shading conditions(PSC).It is necessary to modify the operating point efficiently and accurately with the help of MPPT technology to maximize the collected power.Even though a lot of research has been carried out and impressive progress achieved for MPPT technology,it still faces some challenges and dilemmas.Firstly,the mathematical model established for PV cells is not precise enough.Second,the existing algorithms are often optimized for specific conditions and lack comprehensive adaptability to the actual operating environment.Besides,a single algorithm may not be able to give full play to its advantages.In the end,the selection criteria for choosing the suitable MPPT algorithm/converter combination to achieve better performance in a given scenario is very limited.Therefore,this paper systematically discusses the current research status and challenges faced by PV MPPT technology around the three aspects of MPPT models,algorithms,and hardware implementation.Through in-depth thinking and discussion,it also puts forward positive perspectives on future development,and five forward-looking solutions to improve the performance of PV systems MPPT are suggested.

Optimal PV array reconfiguration under partial shading condition through dynamic leader based collective intelligence

This paper applies the innovative idea of DLCI to PV array reconfiguration under various PSCs to capture the maxi-mum output power of a PV generation system.DLCI is a hybrid algorithm that integrates multiple meta-heuristic algo-rithms.Through the competition and cooperation of the search mechanisms of different metaheuristic algorithms,the local exploration and global development of the algorithm can be effectively improved to avoid power mismatch of the PV system caused by the algorithm falling into a local optimum.A series of discrete operations are performed on DLCI to solve the discrete optimization problem of PV array reconfiguration.Two structures(DLCI-I and DLCI-II)are designed to verify the effect of increasing the number of sub-optimizers on the optimized performance of DLCI by simulation based on 10 cases of PSCs.The simulation shows that the increase of the number of sub-optimizers only gives a relatively small improvement on the DLCI optimization performance.DLCI has a significant effect on the reduction in the number of power peaks caused by PSC.The PV array-based reconstruction system of DLCI-II is reduced by 4.05%,1.88%,1.68%,0.99%and 3.39%,when compared to the secondary optimization algorithms.

基于跃变探索式电导增量法的光伏阵列全局最大功率点跟踪控制研究

实现光伏阵列最大功率点跟踪(Maximum power point tracking, MPPT)的传统算法已经较为成熟,但是在局部阴影出现后会发生寻优失效,难以实现全局最大功率跟踪(Global maximum power tracking, GMPPT)。为解决该问题,研究人员提出将粒子群(Particle swarm optimization, PSO)等群搜索算法应用在MPPT控制过程中,虽然能够控制工作点稳定在全局最大功率点处,但由于该算法收敛能力依赖于核心参数,在应用过程中有一定概率会导致系统振荡。针对以上问题,在电导增量法(Incremental conductance, INC)的基础上提出跃变探索式电导增量法(Jump explore incremental conductance, JEINC),相较于传统电导增量法而言,具有较强的探索能力,能够在局部阴影下实现全局最大功率点跟踪控制,同时所提算法具有较好的收敛能力,在工作点位于最大功率点附近能够快速稳定。在三种光照环境下进行Matlab仿真,从稳定时间、暂态过程能量损耗率和振荡幅值三个方面验证了所提算法相较于电导增量法和粒子群算法的优越性。

A novel reconfiguration of the solar array to enhance peak power and efficiency under partial shading conditions:experimental validation

Non-homogeneous irradiation patterns and temperature levels immensely affect the performance of solar photovoltaic arrays.Partial shading conditions on solar arrays reduce the peak power and efficiency.This paper provides a new remedy called a novel Ramanujan reconfiguration(NRR)to eliminate this physical shading problem in solar photovoltaic systems.NRR is a static-based reconfigured technique that is built using a three-diode model with the help of the MATLAB®/Simulink®tool.The special feature of the proposed NRR technique is that when shade occurs on the solar modules,it gets realigned in a particular row,column,diagonal,corner,centre and middle peripheral cages.This helps over a wide range of shade dispersion on the solar array.The novel topology is tested against the conventional total cross-tied(TCT)model and recently introduced advanced reconfigured models,namely odd–even topology(OET)and Kendoku topology(KDT).The results are tested under certain shading conditions.The proposed NRR technique increases the peak power by 4.45,2.15 and 2.17 W under the first shading condition regarding TCT,OET and KDT.Its efficiency is improved by 0.51–2.18%under the third shading condition compared with other considered models in this study.In addition,NRR leads to smooth output curves under the second,third and fourth shading conditions,effectively mitigating the local power peaks.The experimental results show the proposed enhanced performance of the novel model against the other models.Graphical Abstract Remedy for physical problem correlated with solar photovoltaics Comparison with traditional and recent solar models Conclusion:NRR has effectively handled the problem related with solar models.It has improved the efficiency up to 31.44%under S4.Also,smooth output curves under S2-S4 shows its effectiveness in mitigating the local power peaks.Greater power gain at 3.94%under S4 is achieved by novel model.Real-time verification proves the supremacy of novel proposed model over other considered models in this work.

基于改进黏菌优化算法的光伏多峰MPPT控制策略

当光伏阵列在局部阴影遮挡条件下时,传统最大功率点追踪控制策略易陷入局部最优解,降低光伏系统发电效率.为解决该问题,本文提出了一种基于改进黏菌优化算法的最大功率点追踪策略.该策略在黏菌优化算法的基础上,基于光伏阵列特性提出了新的边界条件,减小超出边界黏菌的迭代次数,增大算法的收敛速度;同时,引入了Lévy飞行策略优化其收敛准则,提升算法的随机搜索能力,进一步提高了算法的追踪速度.仿真和实验结果表明,该策略拥有较高的追踪速度和追踪精度,在各个光照条件下均能快速追踪到最大功率点,有效提高光伏系统的发电效率.

局部阴影下光伏电池最大功率点跟踪方法

随着能源和环境问题的日益严重,太阳能作为一种绿色可再生能源得到大力发展。光伏发电是太阳能利用最常见的形式,最大功率点跟踪(MPPT)是提高光伏发电效率的有效途径之一。由于光伏发电系统所处环境复杂,部分组件被遮挡的问题难以避免,传统的跟踪算法可能出现误判。针对此问题,文中以局部阴影下最大功率点跟踪控制为主线展开研究,分析光伏发电原理,搭建了4串2并结构的光伏阵列。通过仿真和编程探究了阵列在局部阴影下的输出特性并总结规律。在此基础上,对最大功率点跟踪进行了理论分析,并基于粒子群算法设计出了有效的控制方法,实现了局部阴影下最大功率点跟踪。最后利用Simulink进行仿真,验证了算法的可行性。

局部遮荫下基于IP&O-SSA的MPPT控制研究

为了解决传统最大功率点跟踪(maximum power point tracking,MPPT)控制算法在局部遮荫环境中易陷入局部最优的问题,以及智能优化算法寻优速度慢的问题,提出了一种基于自适应扰动观察(IP&O)和改进麻雀搜索算法(sparrow search algorithm,SSA)的复合IP&O-SSA。该算法对SSA加入了Tent序列初始化,对预警者加入了Levy飞行策略,再对P&O进行了自适应和滤波处理。该算法采用双层控制结构,先通过改进后的SSA进行全局搜索到最大功率点附近,再通过改进后的IP&O进行小步平缓搜索到跟踪最大功率点。通过在Simulink仿真标准环境、局部遮荫、环境突变3种情形,仿真结果表明:在标准环境下,该算法最先跟踪到最大功率点,收敛时间比改进前的扰动观察(P&O)和SSA缩短了3 ms、16 ms,跟踪效率高达99.99%;局部遮荫条件下,只有P&O会陷入局部最优,无法有效跟踪到系统的最大功率点,相较于改进前的SSA,该文算法的平均收敛时间缩短了8 ms,同时跟踪效率高达99.68%,提升了0.09%。验证了该算法适用于日常大部分应用情景,为提升光伏阵列的发电效率提供了理论控制算法基础,为之后的光伏阵列并网减少了不必要的功率损耗。

State-of-the-art review of MPPT techniques for hybrid PV-TEG systems:modeling,methodologies,and perspectives

The development of alternative renewable energy technologies is crucial for alleviating climate change and promoting energy transformation.Of the currently available technologies,solar energy has promising application prospects owing to its merits of being clean,safe,and sustainable.Solar energy is converted into electricity through photovoltaic(PV)cells;however,the overall conversion efficiency of PV modules is relatively low,and most of the captured solar energy is dissipated in the form of heat.This not only reduces the power generation efficiency of solar cells but may also have a negative impact on the electrical parameters of PV modules and the service life of PV cells.To overcome the shortcomings,an efficient approach involves combining a PV cell with a thermoelectric generator(TEG)to form hybrid PV-TEG systems,which simultaneously improve the energy conversion efficiency of the PV system by reducing the operating temperature of the PV modules and increasing the power output by utilizing the waste heat generated from the PV system to generate electricity via the TEGs.Based on a thorough examination of the literature,this study comprehensively reviews 14 maximum power point tracking(MPPT)algorithms currently applied to hybrid PV-TEG systems and classifies them into five major categories for further discussion,namely conventional,mathematics-based,metaheuristic,artificial intelligence,and other algorithms.This review aims to inspire advanced ideas and research on MPPT algorithms for hybrid PV-TEG systems.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor stability.It is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of output power quality and efficiency.It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms,and their performance in tracking the Global Maximum Power Point(GMPP)varies.Thus,a Cuckoo search algorithm(CSA)combined with the Incremental conductance Algorithm(INC)is proposed(CSA-INC)is put forward for the MPPT method of photovoltaic power generation.The method can improve the tracking speed by more than 52%compared with the traditional Cuckoo Search Algorithm(CSA),and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization(PSO)and the Gravitational Search Algorithm(GSA).CSA-INC has an average tracking efficiency of 99.99%and an average tracking time of 0.19 s when tracking the GMPP,which improves PV power generation’s efficiency and power quality.

Maximum power point tracking of PV system under partial shading conditions through flower pollination algorithm

For maximum utilization of solar energy,photovoltaic(PV)power systems should be operated at the maximum power point(MPP)which can be achieved using maximum power point tracking(MPPT)methods.However,the occurrence of multi-peak on P-V curve of a PV array due to the changing environmental conditions such as being partially shaded increases the complexity of the tracking process.The global MPP cannot always be achieved by the conventional MPPT methods.Therefore a novel MPPT method for PV systems using flower pollination(FP)algorithm is proposed in this paper and the Levy flight is used to improve the convergence of FP algorithm.MPPT model of the PV system is established in MATLAB to verify the effectiveness of the proposed method,and the proposed method is compared with two well established MPPT methods.The simulation results indicate that the proposed MPPT method can quickly track the changes in external environment and effectively handle the partially shaded condition.

A Novel Triple-tied-cross-linked PV Array Configuration with Reduced Number of Cross-ties to Extract Maximum Power Under Partial Shading Conditions

Partial shadings cause output power reduction from Photovoltaic(PV)arrays due to mismatch losses.The selection of PV array configurations play a vital role in maximum power generation.This paper proposes a novel Triple-Tied-Cross-Linked(T-T-C-L)configuration to extract maximum power with a lesser number of cross ties than a Total-Cross-Tied(T-C-T)configuration.The performance of the proposed T-T-C-L configuration has been compared with various conventional PV array configurations,such as Series(S),Parallel(P),Series-Parallel(S-P),Bridge-Link(B-L),Honey-Comb(H-C),and T-C-T under Partial Shading Conditions(PSCs)by considering the 9×9 PV array.The PSCs considered are uneven row,column,diagonal,random,short&narrow,short&wide,long&narrow,long&wide shadings and uniform half module shading.The measures,such as open circuit voltage,short circuit current,maximum power,voltages and currents at maximum power,mismatch losses,fill factor and efficiency have been used for performance analysis of various configurations.From the results,it can be concluded that the performance of the proposed T-T-C-L configuration is optimal compared to other configurations.

基于混合式蝴蝶算法的局部阴影光伏MPPT研究

针对传统最大功率跟踪(MPPT)算法在跟踪局部阴影时的光伏最大功率失效问题,以及目前元启发式MPPT算法中较多初始种群数导致算法计算负担过大,寻优时间过长的现象,提出了一种新的蝴蝶算法(BOA)-爬山法(HC)混合MPPT控制算法。该算法利用BOA进行全局寻优,在搜索至全局最大功率附近时采用HC进行后续搜索。利用传统MPPT方法的快速收敛性来提高元启发式算法的搜索速度,减小BOA的搜索空间,加快整体算法的全局跟踪速度。利用MATLAB/Simulink仿真软件搭建了局部阴影下的光伏发电系统,并在相同种群数目下对粒子群(PSO)和BOA算法进行测试对比,验证了所提算法的有效性。