关于风力发电系统短期风速预测仿真

及时并准确地预测风力发电系统短期风速,对于电网调度具有重大意义。由于风速具有随机性、间歇性,且含有大量噪声,因此会对预测精度造成较大误差,为了对短期风速进行精确、稳定地预测,提出了一种基于变分模态分解和高斯过程回归的短期风速预测模型。首先,对原始风速序列进行变分模态分解,获得多个子序列;然后对各子序列分别建立高斯过程回归预测模型,并引入量子粒子群算法代替共轭梯度法,改进协方差函数的超参数寻优过程。最后将各序列的预测值进行叠加得到风速预测结果,并与高斯过程回归、经验模态分解-高斯过程回归模型进行对比。仿真结果表明,变分模态分解和高斯过程回归组合模型能够有效提高预测精度,并为类似工程提供借鉴。

基于状态转移随机模型的SSADT可靠性评估方法

为评估步进应力加速退化试验(step stress accelerated degradation test,SSADT)中具有非线性退化趋势的产品的可靠性,提出一种基于状态转移的随机退化建模与可靠性评估方法。首先将状态转移关系与高斯随机过程相结合建立步进应力随机退化模型,以表征微观缺陷的产生和发展对宏观性能的影响;然后应用最佳线性逼近和随机过程曲线边界首达理论建立可靠性模型;最后将其应用在介质振荡器(dielectric resonator oscilla-tor,DRO)贮存可靠性评估中。以修正高斯牛顿算法和Powell共轭梯度算法相结合的方法对退化模型参数进行识别,其结果表明,状态转移随机退化模型能够有效地刻画产品SSADT中性能退化的非线性趋势特性和随机特性,得到较为准确的评估结果。

Linear and Nonlinear QSPR Models for Predicting Thermal Stabilities of Nitroaromatic Compounds

Quantitative structure-property relationships（QSPRs） have been developed to predict the thermal stability for a set of 22 nitroaromatic compounds by means of the theoretical descriptors derived from electrostatic potentials on molecular surface. Several techniques, including partial least squares regression（PLS）, least-squares support vector machine（LSSVM） and Gaussian process（GP） have been utilized to establish the relationships between the structural descriptor and the decomposition enthalpy. The nonlinear LSSVM and GP models have proven to own a better predictive ability than the linear PLS method. Moreover, owing to its ability to handle both linear- and nonlinear-hybrid relationship, GP gives a stronger fitting ability and a better predictive power than LSSVM, and therefore could be well applied to developing QSPR models for the thermal stability of nitroaromatic explosives.