The partial discharge (PD) detection is of critical importance in the stability and continuity of power distribution operations. Although several feature engineering methods have been developed to refine and improve PD detection accuracy, they can be suboptimal due to several major issues: 1) failure in identifying fault-related pulses; 2) the lack of inner-phase temporal representation; and 3) multiscale feature integration. The aim of this article is to develop a learning-based multiscale feature engineering (LMFE) framework for PD detection of each signal in a three-phase power system, while addressing the above issues. The three-phase measurements are first preprocessed to identify the pulses together with the surrounded waveforms. Next, our feature engineering is conducted to extract the global-scale features, i.e., phase-level and measurement-level aggregations of the pulse-level information, and the local-scale features focusing on waveforms and their inner-phase temporal information. A recurrent neural network (RNN) model is trained, and intermediate features are extracted from this trained RNN model. Furthermore, these multiscale features are merged and fed into a classifier to distinguish the different patterns between faulty and nonfaulty signals. Finally, our LMFE is evaluated by analyzing the VSB ENET dataset, which shows that LMFE outperforms existing approaches and provides the state-of-the-art solution in PD detection.

中文翻译：

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LMFE：局部放电检测中基于学习的多尺度特征工程


局部放电（PD）检测对于配电运行的稳定性和连续性至关重要。尽管已经开发了几种特征工程方法来改进和提高局部放电检测精度，但由于以下几个主要问题，它们可能不是最佳的：1）无法识别与故障相关的脉冲； 2）缺乏内相时间表示； 3）多尺度特征集成。本文的目的是开发一种基于学习的多尺度特征工程（LMFE）框架，用于三相电力系统中每个信号的局部放电检测，同时解决上述问题。首先对三相测量进行预处理，以识别脉冲以及周围的波形。接下来，我们进行特征工程来提取全局尺度特征，即脉冲级信息的相位级和测量级聚合，以及关注波形及其内相位时间信息的局部尺度特征。训练循环神经网络 (RNN) 模型，并从该训练的 RNN 模型中提取中间特征。此外，这些多尺度特征被合并并输入到分类器中，以区分故障信号和非故障信号之间的不同模式。最后，通过分析 VSB ENET 数据集对我们的 LMFE 进行了评估，这表明 LMFE 优于现有方法，并提供了最先进的局部放电检测解决方案。