Application of XGBoost in Identification of Power Quality Disturbance Source of Steady-state Disturbance Events
Application of XGBoost in Identification of Power Quality Disturbance Source of Steady-state Disturbance Events
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DOI：
10.1109/iceiec.2019.8784554
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Liu Yang;Yi Li;Cheng Di
中科院分区：
文献类型：
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作者：
Liu Yang;Yi Li;Cheng Di
在物联网时代，考虑到其快速动态变化的特性，时间序列模式识别并不是一项简单的任务。这些时间序列数据（例如电流或电压）的分布和趋势变化可以反映新出现的环境事件。针对电网中日益增加的电能质量扰动，采用XGBoost算法来识别多源电能质量。首先，采用统计方法从电能质量扰动源中提取特征。这些特征是通过不同的统计方法计算出来的，旨在反映时间序列分布。其次，构建训练数据集，并基于生成的训练数据集训练XGBoost分类器。进而在此基础上增加一些干扰源的先验知识，进而应用于电能质量干扰源识别。实验结果表明，该方法能够有效识别电能质量扰动源，且该方法具有良好的鲁棒性和抗噪声能力。
In the era of IoT, time-series pattern recognition is not a trivial task considering their fast dynamically changing characteristic. The distribution and trend changes of these time-series data such as current or voltage could reflect emerging environment event. In response to the increasing power quality disturbances in the power grid, the XGBoost algorithm are used to identify multiple sources of power quality. Firstly, statistical methods are used to extract features from power quality disturbance sources. These features are computed through different statistic method with aims to reflect the time-series distribution. Secondly, a training data set is constructed and a XGBoost classifier is trained based on the generated training data sets. Furthermore, the prior knowledge of some interference sources is added on this basis, and then it is applied to power quality interference source identification. Experimental results show that this method can effectively identify power quality disturbance sources, and the proposed method has good robustness and noise immunity.