中国电机工程学报2026，Vol.46Issue(6)：2165-2178,中插1,15.DOI:10.13334/j.0258-8013.pcsee.242905
多维特征优化提取的电力系统扰动辨识方法
Power System Disturbance Identification Method Based on Optimized Multi-dimensional Feature Extraction
摘要
Abstract
The large-scale integration of renewable energy has increased the penetration of power electronics in power systems.The weak support and low immunity of power systems have augmented the probability of cascading failures.The rapid identification of power system disturbances based on the phasor measurement unit can offer support for system security and stability control.Nevertheless,the complexity of the dynamic behavior of power systems has resulted in an increase in the intra-class variance and inter-class similarity of disturbances,making the extraction of data features for different disturbances increasingly arduous.To address the abovementioned issues,a power system disturbance identification method based on optimized multi-dimensional feature extraction is proposed in this paper.This method presents an optimized feature extraction approach based on variational mode decomposition,utilizes an improved sparrow search algorithm to optimize the number of modal components and the penalty factor,and extracts the most informative and stable multi-dimensional time-domain features from PMU phasor data,laying the foundation for enhancing classification accuracy.On this basis,a disturbance classification method based on a deep learning and neural network fusion model is proposed.By taking the classification accuracy as the objective function and optimizing hyper parameters such as the learning rate through the transit search algorithm,the optimal fitting effect of data training is achieved.The proposed method has undergone simulation tests on the IEEE-39 node and the western China power grid model and has been verified with actual disturbance data of the power system.The results indicate that the proposed method exhibits good accuracy and generalization.关键词
扰动辨识/同步相量测量/特征提取/融合神经网络/变分模态分解Key words
disturbance identification/synchronous phasor measurement/feature extraction/fusion neural network/variational mode decomposition分类
信息技术与安全科学引用本文复制引用
刘灏,廖梦竹,毕天姝..多维特征优化提取的电力系统扰动辨识方法[J].中国电机工程学报,2026,46(6):2165-2178,中插1,15.基金项目
国家自然科学基金项目(52377098). Project Supported by National Natural Science Foundation of China(52377098). （52377098）
