用于含风电场的电力系统概率潮流计算的高斯混合模型

发布时间：2018-03-20 04:14

本文选题：风电并网　切入点：概率潮流计算　出处：《中国电机工程学报》2017年15期 　论文类型：期刊论文

【摘要】：大规模风电并网使电力系统的不确定性显著增加,给电力系统的安全稳定运行带来更大的挑战,用于系统不确定性分析的概率潮流的研究日益重要。该文针对短期风电功率预测误差不对称甚至多峰的概率密度分布特性,提出采用高斯混合模型对预测误差概率密度分布进行拟合。在此基础上,针对各高斯混合模型中子高斯的随机组合结果,采用改进加权最小二乘法计算各组合对应状态变量的概率密度分布。最后,以各组合中子高斯权重系数的乘积为权重,将各组合对应状态变量的概率密度分布加权整合,得到电力系统概率潮流结果。该方法将高斯混合模型与改进加权最小二乘法相结合,很好地拟合了短期风电功率预测误差的概率分布特性,避免了传统加权最小二乘估计中繁琐的迭代寻优过程,大大简化了电力系统概率潮流求解过程。以改进IEEE14节点系统进行算例分析,验证了该方法的准确性和有效性。
[Abstract]:Large-scale wind power grid connection significantly increases the uncertainty of the power system, and brings greater challenges to the safe and stable operation of the power system. The study of probabilistic power flow for system uncertainty analysis is becoming more and more important. In this paper, Gao Si mixed model is used to fit the probability density distribution of prediction error. The probability density distribution of the corresponding state variables is calculated by using the improved weighted least square method. Finally, the probability density distribution of the corresponding state variables is weighted by the product of the weight coefficients of each combination neutron Gao Si. The probabilistic power flow results of power system are obtained. By combining Gao Si's mixed model with the improved weighted least square method, the probability distribution characteristics of short-term wind power prediction errors are well fitted. It avoids the tedious iterative optimization process in the traditional weighted least square estimation and simplifies the power system probabilistic power flow solution process greatly. The example analysis of the improved IEEE14 node system verifies the accuracy and effectiveness of the method.
【作者单位】：中国农业大学信息与电气工程学院;中国电力科学研究院;
【基金】：国家自然科学基金项目(51477174);国家自然科学基金国际合作交流项目(51711530227) 国家电网公司科技项目(DZB51201503568)~~
【分类号】：TM744

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