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...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。展开更多
阴影条件(Partial Shading Condition,PSC)下光伏系统的功率-电压(P-V)特性曲线呈多峰性,易造成常规最大功率跟踪(Maximum Power Point Tracking,MPPT)算法陷入局部最大功率点(Local Maximum Power Point,LMPP)的问题。文章采用一种新...阴影条件(Partial Shading Condition,PSC)下光伏系统的功率-电压(P-V)特性曲线呈多峰性,易造成常规最大功率跟踪(Maximum Power Point Tracking,MPPT)算法陷入局部最大功率点(Local Maximum Power Point,LMPP)的问题。文章采用一种新颖的启发式算法,即交互式教-学优化算法(Interactive Teaching-Learning Optimization,ITLO)来实现光伏系统PSC下的MPPT。ITLO在原始教-学优化算法(Teaching-Learning Based Optimization,TLBO)的基础上,采用多个班级同时进行教与学,以实现更广泛的全局搜索,提高最优解的质量;同时,所有班级的教师与教师、学生与学生间引入小世界网络(Small World Network,SWN)机制进行交互学习,以实现更深入的局部探索,有效避免了算法陷入LMPP,并提高其收敛速度和收敛稳定性。文中进行了恒温变光照强度和变温变光照强度两个算例的研究。仿真结果表明,与增量电导法(Incremental Conductance,INC)和遗传算法(Genetic Algorithm,GA)相比,ITLO能在PSC下最快速,最稳定地获取最大光能。展开更多
Video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is...Video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is proposed, which models spatio-temporal constraints of video sequence. In order to improve the segmentation quality, the weights of spatio-temporal con- straints are adaptively updated by on-line learning for HCRFs. Shadows are the factors affecting segmentation quality. To separate foreground objects from the shadows they cast, linear transform for Gaussian distribution of the background is adopted to model the shadow. The experimental results demonstrated that the error ratio of our algorithm is reduced by 23% and 19% respectively, compared with the Gaussian mixture model (GMM) and spatio-temporal Markov random fields (MRFs).展开更多
文摘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。
文摘阴影条件(Partial Shading Condition,PSC)下光伏系统的功率-电压(P-V)特性曲线呈多峰性,易造成常规最大功率跟踪(Maximum Power Point Tracking,MPPT)算法陷入局部最大功率点(Local Maximum Power Point,LMPP)的问题。文章采用一种新颖的启发式算法,即交互式教-学优化算法(Interactive Teaching-Learning Optimization,ITLO)来实现光伏系统PSC下的MPPT。ITLO在原始教-学优化算法(Teaching-Learning Based Optimization,TLBO)的基础上,采用多个班级同时进行教与学,以实现更广泛的全局搜索,提高最优解的质量;同时,所有班级的教师与教师、学生与学生间引入小世界网络(Small World Network,SWN)机制进行交互学习,以实现更深入的局部探索,有效避免了算法陷入LMPP,并提高其收敛速度和收敛稳定性。文中进行了恒温变光照强度和变温变光照强度两个算例的研究。仿真结果表明,与增量电导法(Incremental Conductance,INC)和遗传算法(Genetic Algorithm,GA)相比,ITLO能在PSC下最快速,最稳定地获取最大光能。
基金Project supported by the National Natural Science Foundation of China (Nos. 60473106, 60273060 and 60333010)the Ministry of Education of China (No. 20030335064)the Education Depart-ment of Zhejiang Province, China (No. G20030433)
文摘Video object segmentation is important for video surveillance, object tracking, video object recognition and video editing. An adaptive video segmentation algorithm based on hidden conditional random fields (HCRFs) is proposed, which models spatio-temporal constraints of video sequence. In order to improve the segmentation quality, the weights of spatio-temporal con- straints are adaptively updated by on-line learning for HCRFs. Shadows are the factors affecting segmentation quality. To separate foreground objects from the shadows they cast, linear transform for Gaussian distribution of the background is adopted to model the shadow. The experimental results demonstrated that the error ratio of our algorithm is reduced by 23% and 19% respectively, compared with the Gaussian mixture model (GMM) and spatio-temporal Markov random fields (MRFs).
