中文摘要：

考虑风电接入原负荷节点后带来的节点特性不确定性问题,提出了基于概率统计的广义负荷节点稳态特性学习与建模的新方法。为分析风电接入后功率流向的改变,将节点特性分为电源特性与负荷特性;针对节点特性的不确定性变化,基于历史实测数据对有功功率样本空间进行自适应分段细化,统计其概率分布;利用Levenberg-Marquardt神经网络法学习并提取各段节点特征,构建节点特性统一模型,并以风险分析为例说明新模型的应用。仿真结果表明,所提方法不但可精确建模,而且通过统计数据样本引入概率信息,可对不确定性问题按概率分场景分析,弥补了传统方法对随机特征描述能力不足的缺陷,是对传统建模方法在不确定场景应用上的扩展和延伸,从而可为风电接入后的仿真分析与调度控制提供辅助参考。

英文摘要：

By considering the bus characteristic uncertainty after wind power integration at the original load bus,a new method based on probability statistics is proposed to learn about and model steady-state characteristics of generalized load buses.To analyze the change of the power direction after wind power integration, the bus characteristics are divided into source characteristics and load characteristics.In view of the change in the uncertainty of bus characteristics,the sample space of active power is segmented adaptively according to the past measured data. The probability distribution is obtained by probability statistics.Levenberg-Marquardt neural network is used to abstract the bus characteristics prior to the development of the unified model.The application of the new model is described by taking the risk analysis as an example.Simulation results show that the method proposed can not only accurately build the model,but also analyze the uncertainty problem by making use of probability statistics according to the different scenarios.The new method has made up the inadequacy of the traditional method in random characteristic description,which is an extension and supplement to the traditional method in uncertain scenario application useful for simulation analysis and dispatch control following wind power integration.