Variational mode decomposition-subspace-K-nearest neighbour based islanding detection in distributed generation system,International Transactions on Electrical Energy Systems

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Variational mode decomposition-subspace-K-nearest neighbour based islanding detection in distributed generation system
International Transactions on Electrical Energy Systems ( IF 1.9 ) Pub Date : 2021-04-26 , DOI: 10.1002/2050-7038.12900
Bhaskar Patnaik ₁ , Manohar Mishra ₂ , Ranjan Kumar Jena ₃

Affiliation

Owing to the increased penetration of renewable energy resources, the issue of unintentional islanding in distributed generation systems has been one of the most common topics of research over the last decade. In this connection, the present work is intended for a solid addition to the existing literature of unintentional islanding detection approaches (UIDA). This work provides a novel application of variational mode decomposition (VMD) and subspace-K-nearest neighbour (SSKNN) method for the recognition of un-intended islanding events. Initially, the proposed UIDA extracts the 3-phase voltage signals at the targeted distributed generator (DG) location, and uses VMD to acquire the principal modes. Afterward, four major feature indices such as relative mode energy ratio, mode instantaneous amplitude, number of zero crossings and centre frequency are formulated considering the first three modes. Lastly, the SSKNN based classifier is trained and tested for effective recognition of islanding events. The performance of the proposed method is tested under several diverse microgrid operating conditions comprising of both islanding and non-islanding events. The obtained result demonstrates the effectiveness of the proposed UIDA compared to other similar approaches preserving the requirements of IEEE 1547 islanding detection standard.

中文翻译：

基于变分模式分解-子空间-K-最近邻的分布式发电系统孤岛检测

由于可再生能源的渗透率不断提高，分布式发电系统中的无意孤岛问题已成为过去十年中最常见的研究课题之一。在这方面，目前的工作旨在对现有的无意孤岛检测方法（UIDA）文献进行补充。这项工作提供了一种新的应用变分模式分解 (VMD) 和子空间 K 最近邻 (SSKNN) 方法来识别意外孤岛事件。最初，提议的 UIDA 在目标分布式发电机 (DG) 位置提取三相电压信号，并使用 VMD 获取主要模式。之后，相对模态能量比、模态瞬时幅度、考虑前三种模式，制定零交叉数和中心频率。最后，训练和测试基于 SSKNN 的分类器以有效识别孤岛事件。在包括孤岛和非孤岛事件在内的几种不同的微电网运行条件下测试了所提出方法的性能。获得的结果证明了所提出的 UIDA 与其他类似方法相比的有效性，同时保留了 IEEE 1547 孤岛检测标准的要求。

更新日期：2021-06-02

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