Post-fault dynamics of short-term voltage stability (SVS) present spatial-temporal characteristics, but the existing data-driven methods for online SVS assessment fail to incorporate such characteristics into their models effectively. Confronted with this dilemma, this paper develops a novel spatial-temporal graph convolutional network (STGCN) to address this problem. The proposed STGCN utilizes graph convolution to integrate network topology information into the learning model to exploit spatial information. Then, it adopts one-dimensional convolution to exploit temporal information. In this way, it models the spatial-temporal characteristics of SVS with complete convolutional structures. After that, a node layer and a system layer are strategically designed in the STGCN for SVS assessment. The proposed STGCN incorporates the characteristics of SVS into the data-driven classification model. It can result in higher assessment accuracy, better robustness and adaptability than conventional methods. Besides, parameters in the system layer can provide valuable information about the influences of individual buses on SVS. Test results on the real-world Guangdong Power Grid in South China verify the effectiveness of the proposed network.
翻译:短期电压稳定性(SVS)的后空动态显示空间-时空特征,但现有的SVS在线评估数据驱动方法未能有效地将此类特征纳入模型中。面对这一困境,本文件开发了一个全新的空间-时图演化网络(STGCN)来解决这一问题。拟议的STGCN利用图解变动将网络地形信息纳入利用空间信息的学习模型中。然后,它采用单维演动来利用时间信息。这样,它就模拟了具有完整动态结构的SVS空间-时空特征。此后,STGCN为SVS评估设计了一个节点层和系统层。拟议的STGCN将SVS的特征纳入数据驱动分类模型,可以提高评估的准确性、稳健性和适应性。此外,系统层的参数可以提供关于SVS单个客车影响的宝贵信息。对中国南部真实世界广东电力网的测试结果将核实拟议网络的有效性。