基于人工神经网络的电力系统精细化安全运行规则

随着大规模可再生能源不断并网，对电网的实时调控能力提出了更高的要求。传统的基于在线关键断面自动发现以及基于连续潮流的在线极限传输容量计算方法，模型复杂、计算周期长，难以做到在线运行。从数据驱动的角度出发，首先将电网实时运行状态的潮流量抽象为该时刻电网的运行特征；然后对所有特征进行聚类和分布式特征选择；最后运用人工神经网络建立所选特征与关键断面极限传输容量之间的对应关系。算例分析表明，所提基于人工神经网络的电力系统精细化安全运行规则，在保证时间效率的前提下，能够在一定程度上提高关键断面极限传输容量的预测准确度。

Fine security rule for power system operation based on artificial neural network

As renewable energy is being integrated into power system, it brings big challenges to power system, especially for online operation. Online key power flow interface automatic discovery and total transfer capability (TTC) calculation based on continuation power flow are too complex and rather time-consuming to be applied online. From the perspective of data-driven, firstly feature sets based on power flow to illustrate the power system status are established, then clustering methods are used to divide the features into several sets which fit in distributed setting. Then a feature selection method is used to select the most valuable features. Finally, artificial neural networks are utilized to map the selected features to TTC. Numerical tests show that the proposed method can significantly improve the forecasting performance of TTC and save time. This work is supported by the Science and Technology Program of China Southern Power Grid:the Key Technology and Demonstration Application for Security Feature Selection and Knowledge Discovery in Complex Large-scale Power System based on Big Data (No. GDKJ00000058).