Coordinated Power Grid Protection based on Machine Learning Methods

脱碳过程导致电网中新电网设备的大量增加，例如基于可再生能源的挥发性馈入系统、能量存储和高动态负载。此外，配电网的高网度或多支路布置、输电网的临时高负荷治疗性再调度等非常规的网络结构和运行方式也越来越多。仍然迫切需要协调电网保护，以维持供电可靠性和电网安全。由于运行和故障场景之间的分离以及多变量网络结构和电网运行模式越来越缺乏清晰度，经典的电网保护方法正在达到其极限。即使是适应性保护概念也无法充分应对这一挑战。在申请的研究项目中，将采取一种全新的电网保护方法。将神经网络机器学习方法的非线性分类、能力学习和泛化能力等基本特性巧妙地运用到电网保护技术中，可以得到一种通用的、自动化的电网保护解决方案。过电流保护、距离保护或差动保护之间的功能区别在这里不再有效。保护协调的先前规划过程也正在变成神经网络结构的自动训练过程，神经网络结构具有标记的真实的和模拟的操作和故障数据的时间序列。经过适当培训的特工随后在现场更换保护装置。集中或分散的解决方案是可能的。采用“已知算子学习”方法将电网物理知识引入神经网络结构，保证了保护技术对鲁棒性和可追溯性的重要要求。新的方法是实施和测试的实验室模型的电网与数字开关配置和实时网络仿真器（RTDS）的帮助下。随着该研究项目的实施，为维护未来网络的供电可靠性和网络安全做出了重要贡献，最后但并非最不重要的是，促进了网络的数字化。

The process of decarbonization leads to a massive increase in new grid devices in the electrical power grid, such as volatile feed-in systems based on regenerative energy sources, energy storage and highly dynamic loads. In addition, there are more and more unconventional network structures and grid operating modes, such as high degrees of meshing or multi-leg arrangements in the distribution grids and curative redispatch with temporary higher loadings in the transmission grids. The urgent need for coordinated grid protection to maintain supply reliability and grid security remains. Due to the increasing lack of clarity in the separation between operational and fault scenarios as well as multivariate network structures and grid operating modes, classic grid protection methods are reaching their limits. Even adaptive protection concepts cannot adequately meet this challenge in its full form. In the research project applied for, a fundamentally new grid protection approach will be taken. The basic properties of non-linear classification, competence learning and the generalization ability of machine learning methods based on neural networks should be used skillfully in grid protection technology and lead to a universally applicable and automated grid protection solution. The functional distinction between overcurrent protection, distance protection or differential protection is no longer effective here. The previous planning process of protection coordination is also becoming an automated training process for neural network structures with labeled time series of real and simulated operating and fault data. Appropriately trained agents then replace the protective devices in the field. Centralized or decentralized solutions are possible. The introduction of physical knowledge about the electrical grid into the neural network structure with the "Known Operator Learning" method ensures the important requirement of protection technology for robustness and traceability. The new approach is to be implemented and tested on a laboratory model of a power grid with digital switchgear configuration and with the help of a real-time network simulator (RTDS). With the implementation of this research project, an important contribution is made to maintaining the reliability of supply and network security in future networks and, last but not least, the digitization of the networks is promoted.