论文标题
基于频率的多任务学习，具有注意力系统中故障检测的注意机制
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems
论文作者
论文摘要
电动传输线中的故障和异常的迅速，准确检测是智能电网系统的一个至关重要的挑战。现有方法主要取决于基于模型的方法，这些方法可能无法捕获这些复杂的时间系列的所有方面。最近，使用高级计量设备收集的数据集的可用性，例如微型测量单元（$μ$ PMU），在微秒时间范围内提供了测量，从而增强了数据驱动方法的开发。在本文中，我们介绍了一种新型的基于深度学习的方法来进行故障检测，并在真实的数据集上进行测试，即，Kaggle平台的部分排放检测任务。我们的解决方案采用了带有注意机制的长期术语记忆体系结构来提取时间序列特征，并使用1D横向横向神经网络结构来利用信号的频率信息进行预测。此外，我们根据其频率组件提出了一种无监督的方法来群集信号，并将多任务学习应用于不同的群集。我们建议在Kaggle竞争和其他许多性能指标中的其他最新方法中优于获胜者解决方案的方法，并提高了分析的解释性。
The prompt and accurate detection of faults and abnormalities in electric transmission lines is a critical challenge in smart grid systems. Existing methods mostly rely on model-based approaches, which may not capture all the aspects of these complex temporal series. Recently, the availability of data sets collected using advanced metering devices, such as Micro-Phasor Measurement units ($μ$ PMU), which provide measurements at microsecond timescale, boosted the development of data-driven methodologies. In this paper, we introduce a novel deep learning-based approach for fault detection and test it on a real data set, namely, the Kaggle platform for a partial discharge detection task. Our solution adopts a Long-Short Term Memory architecture with attention mechanism to extract time series features, and uses a 1D-Convolutional Neural Network structure to exploit frequency information of the signal for prediction. Additionally, we propose an unsupervised method to cluster signals based on their frequency components, and apply multi task learning on different clusters. The method we propose outperforms the winner solutions in the Kaggle competition and other state of the art methods in many performance metrics, and improves the interpretability of analysis.
