An effective feature extraction method in pattern recognition based high impedance fault detection
An effective feature extraction method in pattern recognition based high impedance fault detection
复制标题
基于高阻抗故障检测的模式识别中有效的特征提取方法
DOI：
10.1109/isap.2017.8071380
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
G. Joós
中科院分区：
文献类型：
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作者：
Qiushi Cui;K. El;G. Joós
高阻抗故障是各种配电系统，特别是农村配电馈线中存在的问题。HIF故障电流具有低幅值、非线性、不对称和随机的特点，因此从HIF电流和电压中提取有用的检测特征是解决这一问题的关键。本文利用离散傅立叶变换和卡尔曼滤波估计的简单信号处理技术，对246个传统电特征及其组合进行了实验，并通过特征排序算法提出了有效的特征集。该系统在6种配电系统中进行了测试，一旦确定了合适的模式识别分类器，该系统在准确性、可靠性和安全性方面都表现出了良好的检测性能。与传统的批处理学习算法相比，该方法在在线机器学习环境中表现出了显著的性能。因此，在未来的智能电网中，对瞬时信号进行处理并自适应地更新其预测模型以检测出更多的hif具有很大的潜力。
High impedance fault (HIF) is problematic in various distribution systems, specially in rural distribution feeders. The fault current of HIF is with low magnitude, non-linear, asymmetrical and random, therefore extracting useful detection features from HIF current and voltage is the key to solve this issue. This paper experiments with 246 conventional electrical features and their combinations and proposes an effective feature set (EFS) via a feature ranking algorithm utilizing simple signal processing technique of discrete Fourier transform and Kalman filter estimation. This EFS is tested in six types of distribution systems and exhibits a promising detection performance in terms of accuracy, dependability and security once a proper pattern recognition classifier is determined. Besides conventional batch learning algorithms, the proposed detection method demonstrates a significant performance in online machine learning environment. Therefore it shows the potential of processing instantaneous signals and updating its prediction model adaptively to detect more HIFs in future smart grid.