Radio location of partial discharge sources: a support vector regression approach
Radio location of partial discharge sources: a support vector regression approach
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DOI：
10.1049/iet-smt.2017.0175
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发表时间：
2018-03-01
影响因子：
1.4
通讯作者：
Atkinson, Robert C.
中科院分区：
文献类型：
--
作者：
Iorkyase, Ephraim T.;Tachtatzis, Christos;Atkinson, Robert C.
局部放电是变电站设备故障的一种有效预警手段。很大一部分资产容易受到PD的影响，因为它们的可持续性在初期就很弱。本研究探讨了一种低成本的方法，不间断地监测PD使用廉价的无线电传感器网络采样的PD接收信号强度的空间模式。机器学习技术被提出用于局部放电源的定位。具体来说，两个模型基于支持向量机的开发：支持向量回归（SVR）和最小二乘支持向量回归（LSSVR）。这些模型在高维特征空间中构造了一个显式的回归曲面，用于函数估计。它们的性能进行了比较与人工神经网络（ANN）模型。结果表明，支持向量回归机和最小二乘支持向量回归机方法的精度均上级人工神经网络。LSSVR方法由于其低复杂度，特别推荐作为PD源定位的实用替代方案。
Partial discharge (PD) can provide a useful forewarning of asset failure in electricity substations. A significant proportion of assets are susceptible to PD due to incipient weakness in their dielectrics. This study examines a low cost approach for uninterrupted monitoring of PD using a network of inexpensive radio sensors to sample the spatial patterns of PD received signal strength. Machine learning techniques are proposed for localisation of PD sources. Specifically, two models based on support vector machines are developed: support vector regression (SVR) and least-squares support vector regression (LSSVR). These models construct an explicit regression surface in a high-dimensional feature space for function estimation. Their performance is compared with that of artificial neural network (ANN) models. The results show that both SVR and LSSVR methods are superior to ANNs in accuracy. LSSVR approach is particularly recommended as practical alternative for PD source localisation due to its low complexity.