高电压技术2024，Vol.50Issue(11)：4864-4874,11.DOI:10.13336/j.1003-6520.hve.20231189
基于潮流引导神经网络的配电网贝叶斯状态估计
Bayesian State Estimation for Distribution Networks Based on Power Flow-informed Neural Networks
摘要
Abstract
Driven by the low accuracy problem of existing distribution network state estimation(SE)methods when measurements are limited,a Bayesian SE method is proposed for distribution networks based on a novel power flow-informed neural network(PFINN).Firstly,the two-dimensional Gaussian mixture probability distribution of real and reactive power injection is learned for each node using historical data;thereby,abundant samples can be obtained for neural network training by Monte Carlo sampling and power flow calculation.Then,with the goal of minimizing the SE error and power flow equation violation,a Bayesian SE model is established for distribution networks based on the PFINN.Physics loss penalty is introduced into the loss function to constrain the output to be consistent with system oper-ating constraints.Furthermore,the BOHB method is adopted to optimize the hyperparameters of the neural network,while transfer learning is introduced to adapt to changes of network topologies and on-load tap changers.Finally,test re-sults using field data and balanced/unbalanced distribution networks show that the proposed method has better estimation accuracy than the pseudo-measurement-based SE method and the Bayesian SE method without power flow informing.Meanwhile,the proposed method achieves good adaptation performance to topology and tap changes.关键词
潮流引导/神经网络/贝叶斯状态估计/配电网/迁移学习Key words
power flow informing/neural network/Bayesian state estimation/distribution network/transfer learning引用本文复制引用
梁栋,刘啸宇,曾林,孙智卿,王守相..基于潮流引导神经网络的配电网贝叶斯状态估计[J].高电压技术,2024,50(11):4864-4874,11.基金项目
河北省自然科学基金(E2021202053) （E2021202053）
天津市自然科学基金(22JCQNJC00160) （22JCQNJC00160）
河北省省级科技计划(20311801D) （20311801D）
中央引导地方科技发展资金(226Z2102G).Project supported by Natural Science Foundation of Hebei Province(E2021202053),Natural Science Foundation of Tianjin City(22JCQNJC00160),S&T Pro-gram of Hebei Province(20311801D),Central Funds Guiding the Local Science and Technology Development(226Z2102G). （226Z2102G）
