0 Infinity Ltd., Imperial Offices, London E6 2JG, UK.
Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece.
Sensors (Basel). 2021 Feb 27;21(5):1650. doi: 10.3390/s21051650.
Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result.
有意孤岛是一种纠正措施，旨在通过将电网划分为多个分区并隔离可能导致级联故障的元件，来保护电力系统在紧急情况下的稳定性。本文提出了一种端到端的深度学习方法来解决有意孤岛问题。针对图分区任务检查了两种类型的损失函数，并在深度学习模型上添加了一个损失函数，旨在最小化形成的孤岛中的负荷-发电不平衡。此外，所提出的解决方案还采用了一种技术，即在聚类后，如果存在孤立的总线，则将它们合并到最近的邻居总线中，从而在大型和复杂系统中提高最终结果。多项实验表明，所提出的深度学习方法为有意孤岛提供了有效的聚类结果，成功地保持了低功率不平衡，并创建了稳定的孤岛。最后，所提出的方法是动态的，依赖于实时系统条件来计算结果。
