基于改进残差网络的两阶段电力系统频率安全多级预警

Two-stage Multi-level Early Warning for Power System Frequency Safety Based on Improved Residual Network

随着“双碳”目标的提出,可再生能源集群大规模并网,导致电力系统频率安全问题再次凸显。因此,借鉴深度学习方法,提出一种基于改进残差网络的两阶段电力系统频率安全多级预警模型。首先,对电力系统的频率安全进行多级精细划分,提出并构建频率安全多级预警模型。该模型在第1阶段利用基于改进残差网络的分类评估器对受扰后的频率是否会超出安全预警值进行评估,并给出预警等级;在第2阶段利用回归预测器进一步给出预警样本的危险程度。最后,以改进IEEE 10机39节点系统和美国伊利诺伊州200节点系统为算例对该模型进行测试,结果表明该模型具有较高的安全预警准确率,不但优于其他浅层学习方法和深度学习模型,而且可以精确地预测频率危险程度,并具有良好的鲁棒性和抗噪能力。

With the proposal of the“carbon emission peak and carbon neutrality”goal and the large-scale grid connection of renewable energy clusters, the problem of power system frequency safety is highlighted again. Therefore, by referring to the deep learning method, a two-stage multi-level early warning model for power system frequency safety based on improved residual network is proposed. Firstly, the multi-level fine division for power system frequency safety is carried out, and the multi-level early warning model for power system frequency safety is proposed and constructed. In the first stage, the proposed model uses the classification evaluator based on the improved residual network to evaluate whether the disturbed frequency will exceed early warning value for the safety, and gives the early warning level. In the second stage, the regression predictor is used to further give the risk degree of early warning samples. Finally, the improved IEEE 10-machine 39-bus system and Illinois 200-bus system are taken as examples to test the model. The results show that the model has high early warning accuracy for the safety, which is not only better than other shallow learning methods and deep learning models, but also can accurately forecast the frequency risk degree, and has good robustness and anti-noise ability.