Abstract Islanding is the condition in which a distributed generator (DG) continues to power an area even though the electrical grid power is no longer present. This can be extremely dangerous to utility workers, and many techniques are dedicated to detect such a situation. This work presents a novel technique for islanding detection based on intelligent tools. Initially, the S-Transform is used to extract the frequency spectrum and calculate the energy from the signals of the three-phase voltages. Thus, the linear combinations of the energies for each phase are submitted to a feature selection algorithm in order to reduce the data dimensionality. Afterwards, the reduced subset of attributes is used as inputs for a predictive model based on Extreme Learning Machine. Very interesting results are presented and compared to conventional tools. The main contributions of the proposed approach are: (i) a fast islanding detection method incurring in low computational burden; and (ii) great generalization capability concerning the topology adaptation. These characteristics result in a reliable solution for islanding detection, which justifies its use in a real-time application.

中文翻译：

---

具有电网拓扑自适应和最小非检测区域的智能孤岛检测

摘要 孤岛是分布式发电机 (DG) 继续为一个区域供电的情况，即使电网电源不再存在。这对公用事业工作人员来说可能是极其危险的，许多技术都致力于检测这种情况。这项工作提出了一种基于智能工具的孤岛检测新技术。最初，S 变换用于从三相电压的信号中提取频谱并计算能量。因此，每个相位的能量的线性组合被提交给特征选择算法以减少数据维度。之后，减少的属性子集被用作基于极限学习机的预测模型的输入。展示了非常有趣的结果，并与传统工具进行了比较。所提出方法的主要贡献是：（i）一种计算负担低的快速孤岛检测方法；(ii) 关于拓扑适应的强大泛化能力。这些特性为孤岛检测提供了可靠的解决方案，这证明了其在实时应用中的使用是合理的。