适用于电力系统非平稳功率振荡信号特征提取的自适应迭代滤波算法研究

规模化新能源并网及大容量电力电子设备的应用使得电力系统功率振荡表征出很强非平稳特性。该文提出了一种适用于电力系统非平稳功率振荡信号特征提取的自适应迭代滤波算法。自适应迭代滤波算法利用Fokker-Planck方程构建滤波函数,经滤波筛选获取具有平稳特征的本征模态分量,算法具有坚实的数学基础,能够有效避免经验模态分解算法存在的模态混叠问题。首先利用自适应迭代滤波算法分解得到振荡分量,对各分量进行Hilbert变换,实现振荡特征参数的提取。测试信号、仿真信号及实测数据分析结果证明了该文方法的有效性,与经验模态分解的对比结果充分验证了该方法在电力系统非平稳功率振荡特征提取中的强适应性。

生物种群灰色非线性变换通用模型及其应用

基于灰色高阶增量动态GMS(n,1)建模原理和方法,提出Logistic扩展模型—LM(n,1)模型、理查兹生长函数扩展模型-LLM(n,1)模型、库柏茨生长函数扩展模型—LE(n,1)模型,能有效描述生物种群数量非平稳、振荡性增长过程。应用本文模型对福建三明格氏栲种群数量增长序列进行模拟,复相关系数R^2>0.9974,平均误差率为<4.0343％。拟合优度、精度均佳。文献[1]～[3]中3种模型模拟值在序列前期存在系统偏差,本文3种模型模拟值有效消除了这一系统偏差,更适合格氏栲种群动态增长特征。本文模型信息包容量极为丰富,为非线性生物种群离散时间序列建模、预测、控制提供了新的有效规范方法,在生物、生态等领域具有广泛适用性与广阔应用前景。

Importance Sampling Strategy for Oscillatory Stochastic Processes

This paper contributes to the structural reliability problem by presenting a novel approach that enables for identification of stochastic oscillatory processes as a critical input for given mechanical models. Identification development follows a transparent image processing paradigm completely independent of state-of-the-art structural dynamics, aiming at delivering a simple and wide purpose method. Validation of the proposed importance sampling strategy is based on multi-scale clusters of realizations of digitally generated non-stationary stochastic processes. Good agreement with the reference pure Monte Carlo results indicates a significant potential in reducing the computational task of first passage probabilities estimation, an important feature in the field of e.g., probabilistic seismic design or risk assessment generally.