共查询到19条相似文献,搜索用时 182 毫秒
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传统MPPT算法存在易陷入局部最优的问题,且目前采用的智能优化算法解决该类问题也有追踪精度不足、追踪速度慢等问题。为解决上述问题,该文提出一种基于金枪鱼算法(TSO)与改进黏菌觅食算法(MSMA)的混合优化算法。该方法通过早期金枪鱼算法的抛物线觅食策略来加快搜索速度,对黏菌觅食算法采用基于混沌映射的反向学习策略进行改进,达到扩大算法探索范围的目的,使之不易于陷入局部最优,并提高算法运算速度。将改进后的算法应用于光伏系统MPPT中,仿真实验结果表明:改进后算法相较于单独TSO与MSMA算法,在不同遮光条件下追踪速率有较大提升,精确度高于单独的TSO与MSMA算法,拥有更好的追踪速度与追踪精度。 相似文献
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为优化光伏阵列在部分遮蔽情况下的多峰值MPPT控制,保证光伏发电系统实时功率的最大输出,提出了基于改进BA算法的最大功率追踪控制方法,即在基本BA算法的基础上,融入了小生境技术的共享机制与排挤策略,减少相似个体数量,从而增加了BA算法在迭代过程中的种群多样性,提高了BA算法在MPPT控制中的全局搜索能力,增强了最大功率追踪的稳定性,并将该算法与PSO、PO算法在不同光照及温度条件下的MPPT控制效果进行了仿真试验对比。结果表明,与传统算法相比,改进的BA算法具有更好的追踪效果,不仅避免了光伏系统在遮蔽情况下输出功率陷入局部最大值的问题,且提高了发电效率。 相似文献
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针对传统最大功率点跟踪技术在局部遮阴等天气条件下存在无法追踪到全局最大功率点的问题,提出一种基于改进黏菌算法的MPPT控制。首先,对太阳电池模型及多峰值特性进行分析;其次,在黏菌算法中引入领导者策略和基于最优个体的凸透镜反向学习策略,在提高算法计算精度、收敛速度的同时克服了算法易“早熟”现象;最后,根据光伏阵列最大功率输出特性分别确定算法优化模型、初始化位置及重启机制。仿真与实验结果表明:基于改进黏菌算法的MPPT控制能快速、准确地跟踪到全局最大功率点,有效规避陷入局部最优问题,提高了光伏系统的转换效率。 相似文献
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光伏阵列在复杂遮荫环境下的P-U曲线呈现多个峰值导致最大功率跟踪(MPPT)算法失效,为此提出双层控制模型,在上层模型中将Levy飞行和多项式变异策略嵌入灰狼算法,构建Levy-变异灰狼优化算法(LPGWO)搜索全局最大功率点;在下层模型中采用扰动观察法对最大功率点进行局部跟踪,进而有效降低复杂遮荫环境下的功率振荡。仿真结果表明,在多峰MPPT控制中,所提模型具有跟踪速度快、收敛精度高、整体功率振荡小等特点,能有效提升复杂遮荫环境下光伏阵列的最大功率跟踪效率和精度。 相似文献
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鸡群算法本身存在高维度运算,易出现偏差,对其改进并应用到光伏系统的最大功率点跟踪(MPPT)控制中。引入混沌序列方法进行初始值的分配,可增强种群的均匀性和遍历性;引入自适应惯性权重改善母鸡个体部分寻优策略,可加快算法的全局搜索速度并改善局部搜索能力;将小鸡个体的跟随系数改进为随机量,可增加较差个体的随机性,避免算法因早熟收敛而陷入局部极值;上述改进既可增强算法的搜索速度和寻优精度,也利于避免早熟现象的发生,进一步曾强算法的寻优效率。与其他应用算法比较及在光伏系统最大功率点跟踪中的应用,验证其有效性。 相似文献
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光伏电池最大功率点跟踪方法的研究 总被引:1,自引:0,他引:1
在光伏发电系统中,为了提高光伏电池的利用效率,需要对光伏电池的最大功率点进行跟踪。分析了在跟踪控制中常见的扰动跟踪法和功率数学模型法,比较了它们的优缺点,并基于这两种方法提出了一种改进的跟踪方法,利用MATLAB对该方法进行了仿真研究,仿真结果验证了该方法的有效性。 相似文献
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《全球能源互联网(英文)》2019,2(4):351-360
Maximum power point tracking (MPPT) techniques are used to maintain photovoltaic modules operating points at the local maximum power points under non-uniform irradiance conditions (NUIC). For global maximum power point tracking (GMPPT) within an appropriate period, a hybrid artificial fish swarm algorithm (HAFSA) is proposed in this paper, which was developed using particle swarm optimization (PSO) to reformulate AFSA and improve its principal parameters. Simulation results show that under NUIC, compared with PSO and AFSA, the proposed algorithm has better performance with respect to convergence speed and convergence accuracy. Under NUIC, the average convergence times for 1000 simulation experiments completed with PSO, AFSA, and HAFSA are 0.4830 s, 0.4003 s and 0.3152 s respectively, and the average tracking time of the HAFSA algorithm is reduced by 34.74% and 21.26% compared with PSO and AFSA, respectively. The convergence times of the velocity inertia m relative constant and linear decrement method decreased by 35.48% and 8.19%, the convergence time of the Visual relative constant mode decreased by 10.16%, and the convergence time of the Step relative constant mode decreased by 17.88%. The proposed GMPPT algorithm is simulated in MATLAB, and the algorithm tracks GMPP with excellent efficiency and fast speed. 相似文献
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《International Journal of Hydrogen Energy》2019,44(47):25457-25472
Due to the alteration of power-voltage characteristics of solar module output under multiple environmental conditions such as solar irradiation and ambient temperature, these systems hardly function at maximum power point (MPP). However, maximum power point tracking (MPPT) plays a significant role in their efficiency. On the other hand, solar module characteristics are extremely nonlinear and their slope on either side of MPP is asymmetric. Thus using a nonlinear control method which has the potential of adapting the operating point of the system to MPP seems useful. This has motivated authors to present MPPT method which maximizes PV's output power by tracking MPP continuously. In the present study, a fuzzy logic controller (FLC) is presented for MPPT in photovoltaic systems. Four optimization algorithms are presented in this paper for optimizing fuzzy membership functions (MFs) and generating proper duty cycle for MPPT. The presented algorithms include: Teaching Learning Based Optimization (TLBO), Firefly Algorithm (FFA), Biogeography based optimization (BBO), and Particle Swarm Optimization (PSO), which are all described and simulated. Finally, to validate performance of the proposed optimized FLC, it is compared with other algorithms such as symmetrical fuzzy logic controller (SFLC) and conventional Perturbation and Observation (P&O). According to the simulation results, P&O algorithm shows significant oscillations, energy loss, and in some cases, it cannot obtain MPP. Simulation results also indicate that TLBO and FFA based asymmetric fuzzy MFs not only increase MPPT convergence speed but also enhance tracking accuracy in comparison with symmetric fuzzy MFs and asymmetric fuzzy MFs based on BBO and PSO. 相似文献
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以太阳电池的物理数学模型为研究对象,通过对太阳电池输出特性的分析,提出一种基于电导增量法的无级电压扰动叠加自适应的最大功率点跟踪(MPPT)简洁控制方法,在PSIM仿真环境下,与常规的固定步长电导增量法进行对比分析。仿真研究及实验结果表明,该方法在太阳光照强度或负载突变及稳态时均能快速有效实现最大功率跟踪控制,为光伏发电MPPT控制的设计应用提供了参考。 相似文献
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光伏电池最大功率点跟踪控制方法的对比研究及改进 总被引:1,自引:0,他引:1
光伏发电系统中光伏电池的输出特性具有唯一的最大功率点(MPP),需要对光伏电池的最大功率点进行跟踪(MPPT)。文中分析了几种常见的最大功率点跟踪控制方法对比分析了它们的优缺点。针对MPPT控制方法中存在的启动特性较差、跟踪过程不稳定、精度不高等特点,采用一种改进爬山法,该法以恒定电压法作为启动特性及采用变步长进行跟踪控制,并利用Matlab/Simulink搭建了改进爬山法的MPPT控制模型,仿真结果验证该方法的有效性。 相似文献
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针对最大功率点跟踪技术在光伏发电系统中的应用和研究现状,提出一种改进功率预测变步长扰动法。首先分析光照强度在一个采样周期内非线性变化时功率预测法会因功率预估值偏差较大而产生误判这一现象,为减少功率振荡损失,基于牛顿插值法建立三点采样函数模型以减少预测功率值与实际功率值的偏差。其次在确定变步长的过程中纳入光照强度变化这一动态因素,通过引入对光照强度变化的修正系数提出一种变步长的确定方法。最后搭建光伏系统模型及车载光伏实验平台进行最大功率点追踪仿真,结果表明改进功率预测变步长扰动法在改善误判和失效问题的同时能更加平稳地追踪最大功率点。 相似文献
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《全球能源互联网(英文)》2022,5(6):627-644
Photovoltaic (PV) systems are adversely affected by partial shading and non-uniform conditions. Meanwhile, the addition of a bypass shunt diode to each PV module prevents hotspots. It also produces numerous peaks in the PV array’s power-voltage characteristics, thereby trapping conventional maximum power point tracking (MPPT) methods in local peaks. Swarm optimization approaches can be used to address this issue. However, these strategies have an unreasonably long convergence time. The Grey Wolf Optimizer (GWO) is a fast and more dependable optimization algorithm. This renders it a good option for MPPT of PV systems operating in varying partial shading. The conventional GWO method involves a long conversion time, large steady-state oscillations, and a high failure rate. This work attempts to address these issues by combining Cuckoo Search (CS) with the GWO algorithm to improve the MPPT performance. The results of this approach are compared with those of conventional MPPT according to GWO and MPPT methods based on perturb and observe (P&O). A comparative analysis reveals that under non-uniform operating conditions, the hybrid GWO CS (GWOCS) approach presented in this article outperforms the GWO and P&O approaches. 相似文献