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Reliable Islanding Detection Scheme for Distributed Generation Based on Pattern-Recognition

机译：基于模式识别的分布式发电可靠的岛屿检测方案

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摘要

This article presents a reliable islanding detection scheme for distributed generation (DG) to minimize the nondetection zone using a pattern-recognition method. A hybrid time-frequency signal decomposition along with a machine learning processes the voltage signal retrieved at the DG to make final decision. Amalgamation of time-varying filter and time domain decomposition obtains a modified intrinsic mode functions (MIMF), which enhances the time-frequency resolution of nonstationary signals. Moreover, the adaptive nature of the proposed hybrid signal decomposition makes it more advisable over other decomposition techniques to frame the input feature vector. Further, the random subspace ensemble framework based on ensemble k-nearest neighbor classifier is used among different machine learning techniques to identify the islanding condition by applying the feature vector generated using MIMF. The proposed scheme is thoroughly verified on two standard test systems for identifying the typical islanding condition of zero power mismatch and the proposed scheme discriminates the islanding from large scale disturbances such as capacitor switching and faults. The performance of the scheme is assessed through reliability analysis and it is also compared to other machine learning techniques.
机译：本文介绍了用于分布生成（DG）的可靠岛检测方案，以使用模式识别方法最小化非检测区域。混合时频信号分解以及机器学习处理在DG处检索的电压信号进行处理以进行最终决定。时变滤波器和时域分解的融合获得了改进的内在模式功能（MIMF），其增强了非间断信号的时频分辨率。此外，所提出的混合信号分解的自适应性质使得更易于帧输入特征向量的其他分解技术。此外，基于集合K-最近邻分类的随机子空间集合框架在不同的机器学习技术中使用，通过应用使用MIMF生成的特征向量来识别岛状条件。在两个标准测试系统中彻底验证了所提出的方案，用于识别零功率不匹配的典型岛状条件，所提出的方案判断来自大规模扰动的岛屿，如电容器切换和故障。通过可靠性分析评估该方案的性能，并且还与其他机器学习技术进行了评估。

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