电力系统保护与控制2025，Vol.53Issue(20)：95-105,11.DOI:10.19783/j.cnki.pspc.241682
物理知识引导下覆冰迭代自迁移预测
Physics knowledge guided iterative self-transfer learning for icing prediction
摘要
Abstract
High impact icing events pose a threat to the stable operation of power systems.However,under small-sample scenarios,traditional physics-knowledge guidance and data-driven prediction methods often have poor performance.Therefore,a physics knowledge guided iterative self-transfer learning(PKG-ISTL)model is proposed,integrating physics informed neural network(PINN)with self-transfer learning(STL).First,the data is divided into source and target domains,and a three-dimensional tensor containing spatial,feature,and temporal dimensions is constructed to achieve sliding-window-based icing prediction.Second,the model is built by combining PINN and STL.In the source domain branch,a PINN is trained to guide the model using physics knowledge.In the self-migration branch,multi-kernel maximum mean discrepancy is applied for domain adaptation.In the target domain branch,knowledge distillation is used to transfer physics knowledge from the expert model to the trained model.Finally,historical icing data from transmission lines in Guangxi province are used for simulation.Through interpretability analysis,the influence of meteorological,mechanical,line,and icing factors on line icing is revealed.Results show that compared to traditional data-driven models,the PKG-ISTL model improves prediction accuracy by 47.69%,verifying its effectiveness in small-sample scenarios.关键词
覆冰预测/物理信息神经网络/自迁移学习/知识蒸馏/三维张量Key words
icing prediction/physics-informed neural network/self-transfer learning/knowledge distillation/three-dimensional tensor引用本文复制引用
王振国,李特,郑文哲,王燕,侯慧,林湘宁..物理知识引导下覆冰迭代自迁移预测[J].电力系统保护与控制,2025,53(20):95-105,11.基金项目
This work is supported by the Key Program of National Natural Science Foundation of China(No.U22B20106). 国家自然科学基金重点项目资助(U22B20106) （No.U22B20106）
浙江省自然科学基金项目资助(LZJMY25D050006) （LZJMY25D050006）
国网浙江省电力有限公司科技项目资助(B311DS24001A) （B311DS24001A）
