光伏发电功率持续时间特性的概率分布定量分析

随着光伏发电渗透率在电力系统中的快速增加,电网的规划和调度过程中均有必要对其进行精细化的考虑。但光伏发电功率具有较强的随机性,对其稳态功率特性的描述需要采用概率统计手段。首先根据光伏出力运行点及其波动限值定义了光伏出力状态,然后针对光伏出力维持在某一状态不变的时间长度进行了定量研究,提出了光伏发电功率持续时间分布的概念。基于德国和中国实测数据的分析表明:光伏出力的持续时间分布存在明显的双峰值特征,适合采用逆高斯分布和广义极值分布函数进行加权混合拟合;双峰值特征表明,光伏发电可能在局部云层扰动下出现快速功率波动,从而只能在数分钟内保持出力状态不变,也可能在天气条件良好时跟随日地运动缓慢变化,其出力可维持某一状态长达数小时。所述研究可用于光伏发电功率调度控制过程中测量和决策周期的选定、光伏电站输出波动限值检验等,进而为输电系统中设备的设计选型提供重要的参考依据。

基于AP聚类-跳转持续MC的风电出力时间序列模拟生成方法研究

模拟生成概率等效条件下的风电出力时间序列对分析未来场景下风电出力不确定性以及提高风电消纳水平具有重要意义。针对传统马尔科夫链存在的问题,文中提出一种基于AP聚类-跳转持续马尔科夫链(Affinity Propagation Clustering-Transition and Persistence Markov Chain,AP-TP MC)的风电出力时间序列模拟生成方法。首先,对历史风电出力数据进行AP聚类,并对每一聚类类别下的风电序列建立相应的状态跳转矩阵;其次,依据马尔科夫链模型,结合风电出力状态持续时间特性,抽样并叠加符合混合高斯分布的波动分量,形成某一聚类类别下的风电出力时间序列;然后,依托类间转移矩阵,模拟生成风电出力时间序列;最后,通过对比传统马尔科夫链法与文中方法生成的风电出力时间序列与历史序列之间的统计指标、概率分布指标及自相关性指标,验证所提方法的有效性和准确性。

Characteristics of Far-field Precursory Anomalies Before the M_S8.1 Earthquake in the West of Kunlun Mountains Pass

In this study, a number of typical precursory anomalies recorded by stations in Qinghai, Gansu, Sichuan, Xinjiang, Ningxia, Hebei and Shaanxi provinces and autonomous regions before the Ms8.1 earthquake in the west of Kunlun Mountains Pass are collected and checked. According to the standards of earthquake cases in China, the criteria of the precursory anomalies are determined, and 53 distinguished. The characteristics of these anomalies before the Ms S. 1 earthquake are analyzed, with results showing a very large earthquake affected area. The precursory anomalies recorded by instruments were 2900 km away from the epicenter, and according to the study in this paper, reached 2100 km away. The results also show that the anomalies present characteristics of long duration, multi-measurement items and large-amplitude variation. The authors believe that in large earthquake monitoring, attention should be paid to the variation of data over a large area, ranging up to thousands kilometers, with much denser earthquake observation networks.

An investigation of the persistence property of wind power time series

Mining the inherent persistence property of the time series of wind power is crucial for forecasting and controlling wind power.Few common methods exist that can fully depict and quantify the persistence property.Based on the definition of the active power output state of a wind farm,this paper describes the statistical persistence property of the duration time and state transition.Based on the results of our analysis of significant amounts of wind power field measurements,it is found that the duration time of wind power conforms to an inverse Gaussian distribution.Additionally,the state transition matrix of wind power is discovered to yield a ridge property,the gradient of which is related to the time scale of interest.A systemaic methodology is proposed accordingly,allowing the statistical characteristics of the wind power series to be represented appropriately.

Feature analysis of LIDAR waveforms from forest canopies

Airborne light detection and ranging (LIDAR) can detect the three-dimensional structure of forest canopies by transmitting laser pulses and receiving returned waveforms which contain backscatter from branches and leaves at different heights.We established a solid scatterer model to explain the widened durations found in analyzing the relationship between laser pulses and forest canopies,and obtained the corresponding rule between laser pulse duration and scatterer depth.Based on returned waveform characteristics,scatterers were classified into three types:simple,solid and complex.We developed single-peak derivative and multiple-peak derivative analysis methods to retrieve waveform features and discriminate between scatterer types.Solid scatterer simulations showed that the returned waveforms were widened as scatterer depth increased,and as space between sub-scatterers increased the returned waveforms developed two peaks which subsequently developed into two separate sub-waveforms.There were slight differences between the durations of simulated and measured waveforms.LIDAR waveform data are able to describe the backscatter characteristics of forest canopies,and have potential to improve the estimation accuracy of forest parameters.

The effect of finite duration inputs on the dynamics of a system： Proposing a new approach for cancer treatment

In this paper, a principal question regarding the effect of inputs on the characteristics of dynamic systems is discussed. Whether an input implemented only for a limited duration, can change the characteristics of a dynamic system such that the behavior of the free system, after eliminating the input, differs from that before acting the input？ In this paper, it is shown that a limited time acted input is not able to change the dynamical properties of a system after its elimination. Regarding the proposed approach, a novel finite duration treatment method is developed for a tumor-immune system. The vaccine therapy is used to change the parameters of the system and the chemotherapy is applied for pushing the system to the domain of attraction of the healthy state. For optimal chemotherapy, an optimal control is used based on state-dependent Riccati equation （SDRE）. It is shown that, in spite of eliminating the treatment （therapeutic inputs）, the system approaches to healthy state conditions. The present analysis suggests that a proper treatment method must change the dynamics of the cancer instead of only reducing the population of cancer cells.