信息与控制2026，Vol.55Issue(4)：687-700,14.DOI:10.13976/j.cnki.xk.2025.2553
基于增强液态神经网络的励磁系统故障检测方法
Fault Detection Method for Excitation Systems Based on Enhanced Liquid Neural Networks
摘要
Abstract
Excitation system faults pose a serious threat to the secure and stable operation of power systems,while commonly used discrete-time data-driven models exhibit clear limitations for early fault detection in multi-bridge parallel excitation systems.Such models,relying on fixed samp-ling sequences,struggle to accurately capture the subtle evolution of continuous electromagnetic processes and the spatial coupling among multiple measurement points.Moreover,they are sensitive to noise interference and data gaps,which makes it difficult to promptly identify weak fault features and multi-timescale degradation processes.To address these issues,we,for the first time,introduce liquid neural networks(LNNs)into excitation system fault detection and proposes an enhanced liquid neural network(E-LNN)model tailored for multi-bridge paralleled excitation devices.The model describes neuron state evolution in continuous time via ordinary differential equations and incorporates three enhancement mechanisms spatio-temporal coupling attention,a multi-scale variable time-constant scheme,and fault-sensitive sparse connectivity,to jointly characterize electromagnetic coupling among bridges,dynamic features ranging from microsecond-level transients to hour-level slow degradation,and the distinctiveness of abnormal patterns.Experimental results show that E-LNN achieves a detection accuracy of 94.83%on a dataset covering 12 fault modes,outperforming the second-best method by 2.69%.Under heavy noise and severe missing data,it still maintains stable performance,effectively overcoming the shortcomings of traditional discrete-time models in robustness and early fault capture and demonstrating strong potential for engineering applications.关键词
励磁系统/故障检测/液态神经网络/空间-时间注意力/多尺度建模Key words
excitation system/fault detection/liquid state neural network/spatio-temporal attention/multiscale modeling分类
信息技术与安全科学引用本文复制引用
马明叶,谢燕军,张舸,程建,张敬,赵学健..基于增强液态神经网络的励磁系统故障检测方法[J].信息与控制,2026,55(4):687-700,14.基金项目
国家自然科学基金项目(62303239) （62303239）
三峡金沙江云川水电开发有限公司宁南白鹤滩电厂科技项目(Z532302044) （Z532302044）
