基于稀疏贝叶斯学习的继电保护装置瞬变脉冲信号识别OA

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.