局部阴影下光伏阵列MPPT算法的研究

针对局部阴影状态下光伏阵列P-U特性曲线多峰值的问题,提出了一种基于搜索-计算-判断的三步法全局最大功率点跟踪(GMPPT)策略。该方法使用改进型扰动观察法反向搜索第一个峰值点,利用计算和判断减小搜索区域,将多峰值间接转化为单峰值搜索,解决了搜索过程中陷入局部极值的问题。通过建立3种阴影状态下的模型,将该方法与现有最大功率点跟踪(MPPT)算法进行仿真对比,并建立相应的试验进行验证。仿真及试验结果表明,该方法在有、无阴影状态下均能稳定实现GMPPT,有效地提高了系统的跟踪精度和跟踪速度。     

电场耦合取能技术的仿真与试验研究

为给输电线路的监测器件提供稳定、便利的电源,基于电磁感应的原理设计了无线电容取电装置,可以最大化利用空间能量,对支撑输电线路的运行状态监测具有重要意义。首先,分析了电容取电的结构,以此得到取能装置输出功率与高压杂散电容、低压杂散电容、负载电阻等参数的计算关系,发现合理增加感应电极面积、减小感应电极电容值和增加负载电阻值可以有效提升取能装置输出功率;其次,利用多物理场仿真软件模拟了电容取电的影响因素,最后,通过Matlab软件设计了高压实验装置,对实际情况进行模拟与实验。实验结果表明,当电源电压为10 k V、耦合电容为14 p F时,1 MΩ的负载可以得到0.12 m W功率。     

可变频率变压器的最大功率传输控制策略

可变频率变压器（VFT）是一种能连接异步电网的新型灵活交流输电系统设备,其可传输功率的容量是重要的性能指标。针对传统控制策略未考虑转差率增大可能导致转子绕组超过额定容量的问题,对不同的转差率和定子无功功率情况下定子有功功率最大值进行研究;详细研究了采用串联变换器的VFT系统运行特性;对不同转差率进行了详细的工况分析。推导出最优定子无功功率给定值与转差率、额定视在功率和实际吸收无功功率之间的数学关系,并提出了VFT的最大功率传输（MPT）控制策略;通过硬件在环实验平台验证提出的MPT控制策略的有效性。实验结果表明,所提MPT控制策略能有效提高VFT的可传输功率,实现VFT的最大功率传输。     

高风电渗透率下考虑电网频率支撑需求的储能系统配置方法

提出了一种高风电渗透率下考虑电网频率支撑需求的储能系统配置方法.以频率变化率和频率偏差为限制条件,建立新能源系统所能承受的最大功率增量与等效惯性常数、调差系数以及风电渗透率等已知参数的联系.通过对3阶虚拟同步机控制策略下的储能系统容量与控制参数进行量化配置,提高不同风电渗透率系统对不平衡功率的消纳能力.以储能配置在频率支撑中贡献的等效单位调节功率为参考,对不同功率增量下储能系统的频率响应贡献、调频出力占比以及输出功率特性进行刻画与分析.仿真验证了该配置方法下的储能系统可控性强,能够较为精准地提供电网所需的有功调节量,有效改善风电并网环境.     

晶化机制与激活能的晶化速率参比法确定

本文根据修正的Kissinger公式提出一种确定非晶材料晶化机制与激活能的最大晶化速率参比法。以Ar_(70)Cu_(30)非晶合金为例,研究了晶化机制与激活能及其与压力的关系。     

机器人视觉引导中标定参数同步求解方法

在分析机器人视觉引导中各坐标系转换关系的基础上,提出一种基于最大似然估计的非线性机器人视觉引导标定参数的计算方法,该方法根据最大似然估计构造计算过程中矩阵变换测度函数,采用Levenberg-Marquardt算法求解非线性最小二乘问题,可一次性求解出摄像机标定参数和手眼标定参数。通过数值仿真和真实机器人标定实验,将计算结果与经典标定方法的计算结果进行了比较。结果表明:提出的算法相对于经典算法在估计误差、对噪声的敏感度和稳定性方面均有所提高。     

TESTING CHANGE-POINTS IN THE EXPLOSIVE GAUSSIAN AUTOREGRESSIVE PROCESSES

Abstract. A functional limit theorem with a particular function class and topology is derived for non-ergodic type time series. This limit theorem allows us to study the asymptotic law of the associated likelihood ratio test (LRT) statistic for testing the presence of a change in the covariance parameter in the explosive Gaussian autoregressive model. We show that the level of the LRT cannot be approximated without introducing appropriate normalization. The limit law of a particular weighted likelihood ratio test is examined through a simulation study and is compared with the well-known Kolmogorov distribution obtained in the stationary case; we conclude that for practical applications when the root is really close to unity one can use the same thresholds as in the stationary case. This procedure is applied to the study of three real time series known to be non-stationary.     

Estimation in Random Coefficient Autoregressive Models

Abstract. We propose the quasi‐maximum likelihood method to estimate the parameters of an RCA(1) process, i.e. a random coefficient autoregressive time series of order 1. The strong consistency and the asymptotic normality of the estimators are derived under optimal conditions.     

ON THE UNIMODALITY OF THE EXACT LIKELIHOOD FUNCTION FOR NORMAL AR(2) SERIES

Abstract. For stationary second-order autoregressive normal processes, the conjecture of uniqueness of the solution of the exact likelihood equations is examined. A sufficient condition for uniqueness is given; this condition is satisfied with very high probability if the number of observations is not extremely small. Moreover, it is shown that not more than two maxima may exist. Examples of data which actually produce a likelihood function with two local maxima are given.     

SOME PROPERTIES OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN THE SIMULTANEOUS SWITCHING AUTOREGRESSIVE MODEL

Abstract. The simultaneous switching autoregressive (SSAR) model proposed by Kunitomo and Sato (A non-linearity in economic time series and disequilibrium econometric models. In Theory and Application of Mathematical Statistics (ed. A. Takemura). Tokyo:University of Tokyo Press (in Japanese), 1994; Asymmetry in economic time series and simultaneous switching autoregressive model. Struct. Change Econ. Dyn. , forthcoming (1994).) is a Markovian non-linear time series model. We investigate the finite sample as well as the asymptotic properties of the least squares estimator and the maximum likelihood (ML) estimator. Due to a specific simultaneity involved in the SSAR model, the least squares estimator is badly biased. However, the ML estimator under the assumption of Gaussian disturbances gives reasonable estimates.