计算技术与自动化2026，Vol.45Issue(2)：159-163,5.DOI:10.16339/j.cnki.jsjsyzdh.202602025
基于稀疏贝叶斯学习的继电保护装置瞬变脉冲信号识别
Transient Pulse Signal Recognition of Relay Protection Devices Based on Sparse Bayesian Learning
甘永平 1李刚1
作者信息
- 1. 北京国网信通埃森哲信息技术有限公司,北京 100053
- 折叠
摘要
Abstract
To fully grasp the changes in transient pulse signals and accurately measure the operating status of relay pro-tection devices,a method for identifying transient pulse signals in relay protection devices based on sparse Bayesian learning is proposed.Collect transient pulse signals from relay protection devices and extract key features of their distribution curve kurtosis.Introduce the expectation maximization algorithm to iteratively optimize the hyperparameters of the sparse Bayesian learning model,determine reasonable sparsity parameter values,and use the sparse Bayesian learning model to effectively identify transient pulse signals.Through experimental verification,when the sparsity parameter value of this method is 0.6,it can effectively identify pulse signals generated by different types of single faults and multiple faults together.The recogni-tion accuracy is above 98.5％,and the average recognition time of pulse signals is less than 4.46 ms,effectively improving the safety and stability of power system operation.关键词
稀疏贝叶斯学习/继电保护装置/瞬变脉冲信号识别/峰度/期望最大化算法/超参数Key words
sparse Bayesian learning/relay protection device/transient pulse signal recognition/kurtosis/expectation maximization algorithm/hyperparameter分类
信息技术与安全科学引用本文复制引用
甘永平,李刚..基于稀疏贝叶斯学习的继电保护装置瞬变脉冲信号识别[J].计算技术与自动化,2026,45(2):159-163,5.
