计及源荷不确定性的交直流大电网动态安全分级滚动预警

Graded Rolling Early Warning of Dynamic Security for Large-scale AC/DC Power Grid Considering Uncertainties on Source and Load Sides

滚动预警可辨识电网未来的不安全运行状态并提供决策指导信息,是化解系统性安全风险的有效手段。考虑源荷双侧不确定性,提出一种数据驱动的交直流混联大电网动态安全分级滚动预警方法。首先,提出基于控制代价的预警分级策略,分析典型控制措施的控制代价,根据为保障系统安全所需要的控制措施类型区分预警等级;然后,建立多时间尺度协调的滚动预警框架,利用源荷预测精度随待预测时刻临近而提高的特点提高预警结果的精准性,同时配合不同动作时序的控制措施;最后,提出基于增量学习的机器学习模型在线更新方法,利用动态新增的样本数据提高模型评估的准确性,通过有放回抽样训练多个模型以计算给定误差水平下的预警结果可信度。实际电网仿真表明,所提方法能够有效追踪动态安全风险态势,指导适配的防控决策。

Rolling early warning can identify future insecure operation status of power grids and provide decision-making guiding information, which is an effective technique to resist systemic security risks. Considering the uncertainties on source and load sides,a data-driven graded rolling early warning method of dynamic security for large-scale hybrid AC/DC power grid is proposed. First,a control cost based early warning grading strategy is proposed and the control cost of typical control measures is analyzed. The early warning levels are distinguished according to the type of control measures that are needed to ensure the system security.Then, a coordinated multi-time-scale rolling early warning framework is established. Using the characteristic that the prediction accuracy on source and load sides increases as the time to be predicted approaches, the accuracy of the early warning results is improved and it is matched with the control measures of different action sequences. Finally, an incremental learning based online update method of machine learning model is proposed, which uses dynamically added sample data to improve the accuracy of model evaluation, and trains multiple models through replacement sampling to calculate the credibility of early warning results within a given error tolerance. Simulation results of a real power grid demonstrate that the proposed method can effectively track the dynamic security risk situation, and guide the matched control decisions.