Event identification is increasingly recognized as crucial for enhancing the
reliability, security, and stability of the electric power system. With the
growing deployment of Phasor Measurement Units (PMUs) and advancements in data
science, there are promising opportunities to explore data-driven event
identification via machine learning classification techniques. However,
obtaining accurately-labeled eventful PMU data samples remains challenging due
to its labor-intensive nature and uncertainty about the event type (class) in
real-time. Thus, it is natural to use semi-supervised learning techniques,
which make use of both labeled and unlabeled samples. %We propose a novel
semi-supervised framework to assess the effectiveness of incorporating
unlabeled eventful samples to enhance existing event identification
methodologies. We evaluate three categories of classical semi-supervised
approaches: (i) self-training, (ii) transductive support vector machines
(TSVM), and (iii) graph-based label spreading (LS) method. Our approach
characterizes events using physically interpretable features extracted from
modal analysis of synthetic eventful PMU data. In particular, we focus on the
identification of four event classes whose identification is crucial for grid
operations. We have developed and publicly shared a comprehensive Event
Identification package which consists of three aspects: data generation,
feature extraction, and event identification with limited labels using
semi-supervised methodologies. Using this package, we generate and evaluate
eventful PMU data for the South Carolina synthetic network. Our evaluation
consistently demonstrates that graph-based LS outperforms the other two
semi-supervised methods that we consider, and can noticeably improve event
identification performance relative to the setting with only a small number of
labeled samples.

利用半监督学习方法，本研究提出了一种结合未标记的事件数据样本的新型半监督框架，通过基于模态分析提取物理可解释特征，改进了现有事件识别方法。在南卡罗来纳合成网络上的数据生成、特征提取和事件识别结果表明，使用基于图的标签传播方法在事件识别性能上优于其他两种半监督学习方法，并可以明显改善相对于仅使用少量标记样本的情况。