融合虚拟对抗训练和均值教师模型的主导失稳模式识别半监督学习框架

Semi-supervised Learning Framework of Dominant Instability Mode Identification Via Fusion of Virtual Adversarial Training and Mean Teacher Model

电网仿真计算对电力系统规划、运行方式和控制决策制定具有重要的指导意义。在仿真计算中,一个重要的步骤是根据海量暂稳仿真数据分析电力系统的稳定性以及主导失稳模式,为后续制定紧急控制决策表提供帮助,该文采用深度学习克服传统方法难以有效区分实际电网中功角失稳和电压失稳的问题。为降低深度神经网络对有标注样本的依赖,进一步提出一种融合虚拟对抗训练(virtual adversarial training,VAT)和均值教师(mean teacher,MT)模型的半监督学习框架进行仿真分析中的主导失稳模式智能识别。VAT-MT模型分别构造一个教师网络和学生网络,通过对样本特征施加扰动后输入到两个网络中计算一致化损失来强化模型训练,同时采用VAT计算最大扰动方向提升模型的性能。在中国电科院36节点系统和东北电网上进行算例研究,结果表明,所提出的方法能够极大降低样本的标注成本,具有适应实际电网的能力。

Power grid simulation is of great significance to power system planning,operating,and control decisionmaking.It is an important step in simulation calculation to analyze the stability of power system and the dominant instability mode(DIM)according to the massive simulation data,so as to provide support for the subsequent formulation of emergency control decision tables.In this paper,deep learning was used to overcome the problem that traditional methods are difficult to effectively distinguish rotor angle instability and voltage instability in the actual power grid.In order to reduce the dependence of the deep neural networks on labeled samples,this paper proposed a semi-supervised learning framework based on mean teacher(MT)with the virtual adversarial training(VAT)model for the intelligent identification of DIM in simulation analysis.The VAT-MT model constructed a teacher network and a student network respectively.The model training was enhanced by applying small disturbance to the features of all samples,and then input into the two networks to calculate the consistency loss.At the same time,the maximum disturbance direction was calculated by VAT to improve the performance of the model.Case studies were conducted on the China Electric Power Research Institute 36-bus system and Northeast China Power Grid.The results show that the proposed method can effectively reduce the labeling cost,and has the ability to adapt to the actual power grid.