Improving RF-Based Partial Discharge Localization via Machine Learning Ensemble Method
Improving RF-Based Partial Discharge Localization via Machine Learning Ensemble Method
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DOI：
10.1109/tpwrd.2019.2907154
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发表时间：
2019-08
影响因子：
4.4
通讯作者：
E. Iorkyase;C. Tachtatzis;I. Glover;P. Lazaridis;D. Upton;B. Saeed;R. Atkinson
中科院分区：
文献类型：
--
作者：
E. Iorkyase;C. Tachtatzis;I. Glover;P. Lazaridis;D. Upton;B. Saeed;R. Atkinson
局部放电（PD）被认为是工厂故障的前兆，因此是工厂状况的有效指示。在故障发生前准确定位局部放电源是保证电力系统有效维护和提高可靠性的关键。提出了一种低成本、自主式局部放电无线电定位机制，以提高局部放电定位精度。所提出的基于射频的技术使用小波包变换（WPT）和机器学习集成方法来定位PD。更具体地，接收到的信号由WPT分解并分析，以便在存在噪声的情况下识别局部PD信号模式。在基于WPT的局部放电特征的基础上，分别采用回归树算法、自举聚合算法和回归随机森林算法建立局部放电定位模型。所提出的PD定位方案已被发现，成功地定位PD与可忽略不计的误差。此外，已经使用单独的测试数据集验证了PD定位方案的原理。数值结果表明，WPT随机森林PD定位方案产生的上级性能，由于其对噪声的鲁棒性。
Partial discharge (PD) is regarded as a precursor to plant failure and therefore, an effective indication of plant condition. Locating the source of PD before failure is key to efficient maintenance and improving reliability of power systems. This paper presents a low cost, autonomous partial discharge radiolocation mechanism to improve PD localization precision. The proposed radio frequency-based technique uses the wavelet packet transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the WPT and analyzed in order to identify localized PD signal patterns in the presence of noise. The regression tree algorithm, bootstrap aggregating method, and regression random forest are used to develop PD localization models based on the WPT-based PD features. The proposed PD localization scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the PD location scheme has been validated using a separate test dataset. Numerical results demonstrate that the WPT-random forest PD localization scheme produced superior performance as a result of its robustness against noise.