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

为应对大量分布式新能源接入给电网运行控制带来的挑战,同步相量测量技术被引入配电网。然而,配电网PMU数据量巨大且缺乏标签信息,如何合理利用海量无标签数据识别扰动为电网运行控制提供数据支撑是亟需解决的问题。针对该问题,该文提出一种长短时序生成对抗网络无监督特征提取框架(long-short-term time generative adversarial network,LST-TimeGAN)。该方法在传统时序对抗生成网络(time-series generative adversarial networks,TimeGAN)架构上,提出一种基于最小二乘决策损失函数的改进框架,使所提取特征能够反应数据异常程度并为分类提供可靠依据。同时,提出一种基于注意力机制的特征提取单元,提高了空间特征提取效率;进一步,建立长短时三窗并行框架,以对不同时间尺度的扰动特征具备敏感性;最后,以一种预分类、再识别的分类策略完成扰动识别。仿真和现场数据验证表明,该方法可实现无标签、少标签情形下的准确扰动识别;且该方法提取的特征不但能对输电网扰动进行识别,还能对本地电能质量扰动进行识别。

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.