This paper addresses the distribution system state estimation (DSSE) with unknown topology change. A specific kernel that can transfer across tasks is adopted to find relevant patterns from samples under different topologies and induce knowledge transfer. This enables the proposed method to achieve effective inductive reasoning when only limited data are available under a new topology. The Bayesian inference inherently allows us to quantify the uncertainties of the DSSE results. Comparative results with other methods on IEEE test systems demonstrate the improved accuracy and robustness against topology change.

中文翻译：

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基于高斯过程的拓扑变化感知数据驱动概率分布状态估计


本文讨论了拓扑变化未知的配电系统状态估计（DSSE）。采用可以跨任务迁移的特定内核，从不同拓扑下的样本中找到相关模式并诱导知识迁移。这使得所提出的方法能够在新拓扑下只有有限数据可用时实现有效的归纳推理。贝叶斯推理本质上允许我们量化 DSSE 结果的不确定性。与 IEEE 测试系统上的其他方法的比较结果表明，针对拓扑变化的准确性和鲁棒性有所提高。