基于自适应时间窗的数据-模型融合驱动暂态频率预测

Transient Frequency Prediction Driven by Data-model Fusion Based on Adaptive Time Window

新能源大规模并网使得新型电力系统的暂态频率响应特征更加复杂,现有频率在线预测方法难以兼顾准确性和及时性。基于此,提出基于自适应时间窗的数据-模型融合驱动暂态频率预测方法。首先,基于长短期记忆网络,离线训练多个具有不同长度时序数据输入的频率曲线循环预测模型;其次,利用参数辨识方法离线建立各发电集群的通用等值频率响应模型,在此基础上构建系统有功-频率物理机理快速分析模型;最后,串行融合前述频率曲线循环预测模型与有功-频率物理机理快速分析模型,并提出“可信度量化评估指标”,实时分析在线预测过程中不同评估时刻下预测结果的精度,自适应调整输入时序数据长度,直至预测结果满足要求并输出。含风电的IEEE39节点系统的仿真结果表明,所提方法在不同风电渗透率或不同扰动下均能快速、准确地预测暂态频率响应曲线,相较于其他在线预测方法具有更优的评估性能。

The large-scale grid connection of new energy makes the transient frequency response characteristics of new power systems more complex,and the existing online frequency prediction methods make it challenging to balance accuracy and timeliness.Based on this,a transient frequency prediction method based on an adaptive time window driven by data-model fusion is proposed in this paper.Firstly,several frequency curve cyclic prediction models with different length time series data input are trained offline based on a long short-term memory network.Secondly,each power generation cluster's general equivalent frequency response model is established offline using the parameter identification method.Then,a fast analysis model of the system's active power-frequency physical mechanism is constructed.Finally,the frequency curve cyclic prediction model and the active power-frequency physical mechanism rapid analysis model are serial integrated,and a"reliability quantitative evaluation index"is proposed to analyze the accuracy of prediction results at different evaluation moments in the online prediction process in real-time,and adaptively adjust the length of input time series data until the prediction results meet the requirements and output.The simulation results of the IEEE39-node system with wind power show that the proposed method can predict transient frequency response curves quickly and accurately under different wind power permeability or different disturbance and has better evaluation performance than other online prediction methods.