Event detection in electrical grids is a challenging problem for machine learning methods due to spatiotemporally nonstationary systems and the inability to automate event labeling in high-volume data such as PMU measurements. As a result, the existing historical event logs created manually do not correlate well with the corresponding PMU measurements due to scarce and temporally imprecise labels. Trying to overcome this problem by extending event logs to a complete set of labeled events is very costly and often infeasible. We focused on utilizing a transfer learning model to reduce the need for additional data labeling by leveraging some labeled data instances available from a small number of well-defined event detection task. To demonstrate the feasibility, we tested our approach on a large dataset collected by 38 PMUs from the Western Interconnection of the U.S.A. over two years. The model evaluation performed based on varying percentages of labeled source data corresponding to [Math Processing Error]\sim 20 -700 characteristic events on different sizes of time windows ranging from 2-seconds to 1-minute demonstrates that the developed method can significantly improve automated event detection based on PMU measurements when extensive labeling is costly or impossible to obtain. When compared to the state-of-the-art machine learning algorithms (unsupervised, semi-supervised, and supervised), the results show that the transfer learning method has significantly better performances when detecting events by learning from as low as 20 representative labeled data instances.

中文翻译：

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成像逆问题的模型自适应


由于时空非平稳系统以及无法自动标记 PMU 测量等大容量数据中的事件，电网中的事件检测对于机器学习方法来说是一个具有挑战性的问题。因此，由于标签稀缺且时间上不精确，手动创建的现有历史事件日志与相应的 PMU 测量值无法很好地关联。尝试通过将事件日志扩展到完整的标记事件集来克服此问题的成本非常高并且通常不可行。我们专注于利用转移学习模型，通过利用少量明确定义的事件检测任务中可用的一些标记数据实例来减少对额外数据标记的需求。为了证明可行性，我们在美国西部互联网络的 38 个 PMU 在两年内收集的大型数据集上测试了我们的方法。基于不同百分比的标记源数据（对应于 [数学处理误差]\sim 20 -700 个特征事件）在 2 秒到 1 分钟的不同时间窗口大小上进行的模型评估表明，所开发的方法可以显着提高自动化当大量标签成本高昂或无法获得时，基于 PMU 测量进行事件检测。与最先进的机器学习算法（无监督、半监督和监督）相比，结果表明，迁移学习方法在通过从低至 20 个代表性标记数据中学习来检测事件时具有显着更好的性能实例。