关于多峰值最大功率点跟踪(maximum power point tracking,MPPT)算法的研究例如粒子群MPPT算法和全局扫描法以及其改进算法,往往只关注其静态搜索能力和辐照度突变情况下的扫描过程。但外界环境改变造成的特性曲线持续变化过程中的动态...关于多峰值最大功率点跟踪(maximum power point tracking,MPPT)算法的研究例如粒子群MPPT算法和全局扫描法以及其改进算法,往往只关注其静态搜索能力和辐照度突变情况下的扫描过程。但外界环境改变造成的特性曲线持续变化过程中的动态性能研究有些欠缺。为了提升多峰值MPPT算法的动态性能,文中提出一种基于功率闭环法的改进动态多峰值MPPT算法。该算法结合三点法和粒子群算法,快速搜索出全局最大功率点,动态响应能力好,具有较好的实际应用价值。通过实验室和实际电站测试验证了该算法的正确性和有效性。展开更多
In this paper, we introduce a new extension of the power Lindley distribution, called the exponentiated generalized power Lindley distribution. Several mathematical properties of the new model such as the shapes of th...In this paper, we introduce a new extension of the power Lindley distribution, called the exponentiated generalized power Lindley distribution. Several mathematical properties of the new model such as the shapes of the density and hazard rate functions, the quantile function, moments, mean deviations, Bonferroni and Lorenz curves and order statistics are derived. Moreover, we discuss the parameter estimation of the new distribution using the maximum likelihood and diagonally weighted least squares methods. A simulation study is performed to evaluate the estimators. We use two real data sets to illustrate the applicability of the new model. Empirical findings show that the proposed model provides better fits than some other well-known extensions of Lindley distributions.展开更多
文摘关于多峰值最大功率点跟踪(maximum power point tracking,MPPT)算法的研究例如粒子群MPPT算法和全局扫描法以及其改进算法,往往只关注其静态搜索能力和辐照度突变情况下的扫描过程。但外界环境改变造成的特性曲线持续变化过程中的动态性能研究有些欠缺。为了提升多峰值MPPT算法的动态性能,文中提出一种基于功率闭环法的改进动态多峰值MPPT算法。该算法结合三点法和粒子群算法,快速搜索出全局最大功率点,动态响应能力好,具有较好的实际应用价值。通过实验室和实际电站测试验证了该算法的正确性和有效性。
文摘In this paper, we introduce a new extension of the power Lindley distribution, called the exponentiated generalized power Lindley distribution. Several mathematical properties of the new model such as the shapes of the density and hazard rate functions, the quantile function, moments, mean deviations, Bonferroni and Lorenz curves and order statistics are derived. Moreover, we discuss the parameter estimation of the new distribution using the maximum likelihood and diagonally weighted least squares methods. A simulation study is performed to evaluate the estimators. We use two real data sets to illustrate the applicability of the new model. Empirical findings show that the proposed model provides better fits than some other well-known extensions of Lindley distributions.
