中国电机工程学报2024，Vol.44Issue(15)：5858-5870,中插2,14.DOI:10.13334/j.0258-8013.pcsee.230464
基于配电网PMU的无监督电力系统扰动特征提取与分类
Unsupervised Power System Disturbance Feature Extraction and Classification Using PMUs in Distribution Network
摘要
Abstract
To address the challenges posed by distributed renewable energy sources connecting to the distribution network,synchrophasor measurement has been introduced to the distribution level.The problem that urgently needs to be addressed is how to effectively utilize this massive amount of unlabeled data to identify disturbances and provide data support for power grid operation and control.This paper proposes an unsupervised feature extraction framework called long-short-term time generative adversarial network(LST-TimeGAN)to tackle this problem.The proposed method uses time-series generative adversarial networks(TimeGAN)and introduces an improved framework based on the least squares decision loss function to extract features that can reflect the degree of abnormality of events and provide a basis for accurate classification.Also,a feature extraction unit based on attention mechanism is proposed to improve the efficiency of spatial feature extraction.Furthermore,a long-short three-window parallel framework is established to acquire sensitivity to disturbance features of different time scales.Finally,disturbance identification is completed using a pre-classification and re-identification classification strategy.Verification in simulations and field data shows that this method can accurately identify disturbances even when there are no or few labels.Moreover,it can identify not only disturbances in the transmission network but also local power quality disturbances.关键词
同步相量测量/扰动识别/无监督/特征提取/时间序列生成对抗网络Key words
synchronous phasor measurement/disturbance identification/unsupervised/feature extraction/time-series generative adversarial networks(TimeGAN)分类
信息技术与安全科学引用本文复制引用
陈徵粼,刘灏,毕天姝..基于配电网PMU的无监督电力系统扰动特征提取与分类[J].中国电机工程学报,2024,44(15):5858-5870,中插2,14.基金项目
国家重点研发计划(2022YFB4202303). National Key R&D Program of China(2022YFB4202303). （2022YFB4202303）
