基于物理信息的神经网络最小化DC最优潮流最坏违规
Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow
摘要：
物理信息神经网络利用底层物理系统的现有模型，以更少的数据生成更高精度的结果。这种方法可以帮助大大减少计算时间，并产生一个很好的估计计算密集型过程中的电力系统，如动态安全评估或最优潮流。与神经网络性能的最坏情况保证的提取相结合，这样的神经网络可以应用于电力系统中的安全关键应用中，并且在电力系统操作员之间建立高度的信任。本文采取的第一步，并适用于第一次，我们的知识，物理信息的神经网络与最坏情况下的保证直流最优潮流问题。我们寻找与（i）最大约束违反，（ii）预测和最佳决策变量之间的最大距离，以及（iii）整个输入域中的最大次优性相关的保证。在一系列PGLib-OPF网络中，我们展示了物理信息神经网络如何提供最坏情况的保证，以及与传统神经网络相比，它们如何减少最坏情况的违规。
英文摘要：
Physics-informed neural networks exploit the existing models of the underlying physical systems to generate higher accuracy results with fewer data. Such approaches can help drastically reduce the computation time and generate a good estimate of computationally intensive processes in power systems, such as dynamic security assessment or optimal power flow. Combined with the extraction of worst-case guarantees for the neural network performance, such neural networks can be applied in safety-critical applications in power systems and build a high level of trust among power system operators. This paper takes the first step and applies, for the first time to our knowledge, Physics-Informed Neural Networks with Worst-Case Guarantees for the DC Optimal Power Flow problem. We look for guarantees related to (i) maximum constraint violations, (ii) maximum distance between predicted and optimal decision variables, and (iii) maximum sub-optimality in the entire input domain. In a range of PGLib-OPF networks, we demonstrate how physics-informed neural networks can be supplied with worst-case guarantees and how they can lead to reduced worst-case violations compared with conventional neural networks.
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