基于时频特征融合与GWO-ELM的棒控电源早期故障状态辨识方法

Early fault state identification method of the rod control system power equipment based on time-frequency characteristics fusion and GWO-ELM

针对核电棒控系统电源(PWE)早期故障状态辨识问题,提出一种基于融合时域与时频域的故障特征和灰狼优化算法(GWO)的极限学习机(ELM)辨识方法。首先,根据棒控电源PWE工作原理和控制棒驱动机构的驱动电流,利用电流上升时间分析了早期波形形态与早期故障模式。然后,构建融合电流上升时间、均方根-差分和和小波包奇异熵的故障时频特征,分析了特征的可区分性。进而,采用GWO算法进行ELM分类器参数择优,建立GWO-ELM模型实现PWE早期故障状态的辨识,以提高辨识精度。最后,通过开展不同特征组合和辨识模型比对试验,结果表明所提方法能有效实现棒控电源早期故障识别诊断,且平均辨识准确度可达98.86%。

To address the problem of early fault state identification of the nuclear rod control system and rod position system power equipment(PWE),this article proposes an identification method based on the fusion of fault features in time domain and time-frequency domain and extreme learning machine(ELM)of grey wolf optimizer(GWO).Firstly,according to the working principle of PWE and the driving current of control rod drive mechanism,the early waveform shape and early fault mode are analyzed by using the current rise time.Then,the fault time-frequency features are constructed,which are fused with current rise time,root mean square difference summation and wavelet packet singular entropy.The discriminability of the features is analyzed.Then,the GWO algorithm can optimize parameters of the ELM classifier.The GWO-ELM model is formulated to realize the identification of early fault states of PWE,which can improve the identification accuracy.Finally,through the comparison test of different feature combinations and identification models,the results show that the proposed method can effectively realize the early fault identification and diagnosis of rod control system power supply,and the average identification accuracy can reach 98.86%.