电力系统及其自动化学报2026，Vol.38Issue(6)：151-158,8.DOI:10.19635/j.cnki.csu-epsa.001676
考虑逆变器动态行为累积的改进高斯混合模型故障诊断方法
Method of Improved Gaussian Mixture Model for Fault Diagnosis Considering Accumulation of Inverter Dynamic Behavior
郑雪筠 1宋洪亮 1高石磊 1王燕武 1黄呈阳2
作者信息
- 1. 华能澜沧江水电股份有限公司黄登·大华桥水电厂,怒江傈僳族自治州 671407
- 2. 西华大学电气与电子信息学院,成都 610039
- 折叠
摘要
Abstract
The open-circuit fault signals of grid-connected inverters exhibit non-Gaussian and multi-modal distribution characteristics,and they are also susceptible to noise and variations in operational conditions,resulting in inadequate performance of the traditional diagnostic methods.To solve these problems,a robust fault diagnosis method with en-hanced feature discriminability is proposed in this paper.First,discrete state equations are constructed based on a uni-fied circuit model under grid-connected inverter faults to derive the system state space representations for fault feature extraction.Second,kernel mean embedding is employed to measure the distribution discrepancies between fault fea-tures,thus realizing an effective evaluation on the feature discriminability.On this basis,a Bayesian information criteri-on(BIC)-optimized Gaussian mixture model(GMM)is studied to characterize the nonlinear mapping relationships be-tween fault features and categories.In addition,the optimal hyperparameters in GMM are selected according to the BIC,so as to enhance the model's fault classification performance.Experimental results demonstrate that the proposed meth-od achieves excellent performance in terms of accuracy and robustness,with the highest accuracy reaching 99.92%.关键词
并网逆变器/开路故障/状态空间/贝叶斯信息量准则/高斯混合模型/鲁棒性Key words
grid-connected inverter/open-circuit fault/state space/Bayesian information criterion(BIC)/Gaussian mixture model(GMM)/robustness分类
信息技术与安全科学引用本文复制引用
郑雪筠,宋洪亮,高石磊,王燕武,黄呈阳..考虑逆变器动态行为累积的改进高斯混合模型故障诊断方法[J].电力系统及其自动化学报,2026,38(6):151-158,8.
