Traditional islanding detection methods present improper action when the power mismatch between generation and load in the islanding system is small. The minimum power mismatch for correct islanding detection defines the limits os a Non-Detection Zone. This paper proposes an efficient method for islanding detection based on angular velocity harmonic patterns. To accomplish this, the measured frequency is decomposed by a Fourier Transform and applied to a Perceptron artificial neural network for pattern classification. The performance of the proposed method is evaluated by tests on a modified IEEE 34 node test system. Several islanding and non-islanding cases were simulated. The proposed method achived a performance of 88.16% in the classification of harmonic patterns with a small training set. Compared with the Under/Over frequency method, the proposed method represents a performance improvement of 17%.

中文翻译：

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基于角速度谐波模式和感知器神经网络的被动孤岛检测

当孤岛系统中发电和负载之间的功率失配较小时，传统的孤岛检测方法会表现出不正确的动作。正确孤岛检测的最小功率失配定义了非检测区的限制。本文提出了一种基于角速度谐波模式的孤岛检测的有效方法。为此，测量的频率通过傅立叶变换分解并应用于感知器人工神经网络以进行模式分类。通过在修改后的 IEEE 34 节点测试系统上进行测试来评估所提出方法的性能。模拟了几个孤岛和非孤岛情况。所提出的方法在训练集较小的谐波模式分类中取得了 88.16% 的性能。与欠/过频方法相比，