Currently, research work is primarily dependent on the collection of large sets of data from systems and making predictions based on the knowledge obtained from the data, which is generally termed as ‘data mining’. These data mining algorithms are of great importance in improving the performance of different applications. In this regard, Machine Learning (ML) algorithms have been demonstrated to be excellent tools to cope with difficult problems. In this paper, a classification learner based supervised ML algorithm is proposed for intentional islanding of DERs based on the live data collected from supervisory control and data acquisition (SCADA) system in post disaster situations. Literature presents various islanding detection techniques and also intentional islanding algorithms to address different problems in AC networks. These algorithms majorly work based on the control of current source or voltage source inverters. On the other hand, a low voltage DC distribution system allowing the removal of inverter is proposed, which is supposed to be more advantageous by reducing losses and is also economical when working with DERs. In this paper, ML based intentional islanding algorithm for DERs based low voltage DC distribution system is proposed by considering the effects of natural disasters. The learner models trained are fine tree, linear SVM, quadratic SVM and Gaussian SVM. The training of fine tree model is achieved with higher accuracy of 99.8%. The main objective of this work is to achieve a faster and accurate decision making. The performance of the ML based intentional islanding algorithm is compared with the earlier proposed artificial intelligence (AI) based intentional islanding algorithms. The AI algorithms proposed earlier are fuzzy inference systems (FIS), artificial neural networks (ANN) and adaptive network based fuzzy inference system (ANFIS). The comparison shows that, the decision making with ML based intentional islanding algorithm is faster and accurate than all other algorithms.

中文翻译：

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基于机器学习的灾难管理中 DER 的有意孤岛算法

目前，研究工作主要依赖于从系统中收集大量数据并根据从数据中获得的知识进行预测，这通常被称为“数据挖掘”。这些数据挖掘算法对于提高不同应用程序的性能非常重要。在这方面，机器学习 (ML) 算法已被证明是处理难题的出色工具。在本文中，基于从监督控制和数据采集 (SCADA) 系统在灾后情况下收集的实时数据，提出了一种基于分类学习器的监督 ML 算法，用于 DER 的有意孤岛。文献提出了各种孤岛检测技术和有意孤岛算法，以解决交流网络中的不同问题。这些算法主要基于电流源或电压源逆变器的控制工作。另一方面，提出了一种允许拆除逆变器的低压直流配电系统，这被认为在减少损耗方面更有优势，并且在使用 DER 时也很经济。在本文中，通过考虑自然灾害的影响，提出了基于 DER 的低压直流配电系统的基于 ML 的有意孤岛算法。训练的学习器模型有细树、线性 SVM、二次 SVM 和高斯 SVM。以99.8%的更高准确率实现了细树模型的训练。这项工作的主要目标是实现更快、更准确的决策。将基于 ML 的有意孤岛算法的性能与早期提出的基于人工智能 (AI) 的有意孤岛算法进行了比较。早先提出的人工智能算法有模糊推理系统（FIS）、人工神经网络（ANN）和基于自适应网络的模糊推理系统（ANFIS）。对比表明，基于机器学习的有意孤岛算法的决策速度比其他所有算法都快、准确。