The integration of distributed energy resources (DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method (IIDM) using an intrinsic mode function (IMF) feature-based grey wolf optimized artificial neural network (GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

中文翻译：

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基于本征函数特征的人工神经网络对多分布发电的智能孤岛检测

将分布式能源（DER）集成到配电网络中变得越来越重要，因为它支持可再生能源发电，热电联产以及存储系统的持续采用。然而，无意的孤岛操作是连接到DER的配电网中的主要保护问题之一。这项研究提出了一种基于固有模式函数（IMF）基于特征的灰狼优化人工神经网络（GWO-ANN）的智能孤岛检测方法（IIDM）。在提出的IIDM中，模态电压信号通过变模分解进行预处理，然后在每个IMF上进行希尔伯特变换，以得出高度复杂的特征。然后，IMF的能量和标准偏差用于训练/测试GWO-ANN模型，以从其他非离岛事件中识别离岛操作。为了评估所提出的IIDM的性能，将各种孤岛和非孤岛条件（例如故障，电压骤降，线性和非线性负载以及切换）视为训练和测试数据集。此外，在噪声条件下针对所测电压信号评估了建议的IIDM。仿真结果表明，所提出的IIDM能够区分孤岛事件和非孤岛事件，并且在测试信号的噪声条件下也没有任何敏感性。线性和非线性负载与切换被视为训练和测试数据集。此外，在噪声条件下针对所测电压信号评估了建议的IIDM。仿真结果表明，所提出的IIDM能够区分孤岛事件和非孤岛事件，并且在测试信号的噪声条件下也没有任何敏感性。线性和非线性负载与切换被视为训练和测试数据集。此外，在噪声条件下针对所测电压信号评估了建议的IIDM。仿真结果表明，所提出的IIDM能够区分孤岛事件和非孤岛事件，并且在测试信号的噪声条件下也没有任何敏感性。