基于主动学习的电力系统暂态稳定评估方法

随着泛在电力物联网概念的提出,暂态稳定在电力系统运行控制中扮演着越来越重要的角色.由于相量测量单元(Phasor Measurement Unit,PMU)的广泛配置,基于机器学习的暂态稳定实时评估方法展现出了巨大的发展潜力.针对这类方法在应用中离线训练数据生成耗时及造成的难以在网架发生变化后快速更新模型的问题,论文提...

A Power System Assessment Method Based on Active Learning

With the concept of ubiquitous power internet of things,the transient stability plays an increasingly important role in the operation and control of power system.Due to the extensive configuration of the phasor measurement unit(PMU),the real-time evaluation method of machine-based transient stability shows great potential for development.Aiming at the problem that data generation of the offline training is time-consuming and it is difficult to update the model quickly after the grid changes,this paper proposes a power system transient stability assessment method based on active learning.Firstly,implementing short-time simulations(simulation to fault clearing time)to generate unlabeled samples in different kind of operations and faults;and then,randomly select a part of samples for long-term simulation to mark these samples;Then,a part of samples are randomly selected for long-term simulation to be labeled with transient stability status,and support vector machine is further trained to build the transient stability assessment model;Finally,the data with high information entropy in the remaining unlabeled samples is re-trained until the model accuracy does not change.The simulation of the New England 10-machine 39-bus test power system shows that the proposed method can effectively reduce the time of offline simulation,which greatly improves the efficiency of model configuration,and it is robust to wide-area noise.