基于增强生成对抗网络的PMU丢失数据恢复方法

PMU Missing Data Recovery Algorithm Based on Enhanced Generative Adversarial Network

同步相量测量单元(phasor measurement units,PMUs)因能够提供实时测量数据,使得电力系统动态行为的实时监测成为可能。但是,由于现场环境复杂,受干扰、硬件故障等诸多因素影响,PMU数据存在一定程度的数据丢失问题。PMU数据丢失直接影响其在电力系统中的应用,甚至威胁到系统的安全稳定运行。为了提高PMU数据质量,该文提出了一种基于PMU分群的丢失数据恢复算法。为利用相关度高的PMU数据以提高恢复精度,首先提出一种基于层次聚类的PMU分群方法,利用不同PMU数据的相关度来对PMU进行分群。进一步,利用长短期记忆构造了一种增强型生成对抗神经网络丢失数据恢复方法。该算法不需要系统的拓扑结构和参数,可通过将相关度高的数据作为神经网络的输入来恢复不同扰动下的丢失数据,并且在扰动条件下也能高精度恢复。仿真和测试结果表明,该方法能够有效地实现丢失数据的恢复,为提高PMU的数据质量,保证其在电力系统中的应用效果提供基础。

Phasor Measurement Units(PMUs)make it possible to monitor the dynamic behavior of power system in real time because of its synchronization and rapidity.However,due to the complex factors on site,the PMU data can be easily compromised by interferences or hardware malfunctions,resulting in different levels of PMU data loss,which directly affects its application in the power system,and even threatens the safe operation of the system.In order to improve the data quality,a PMU missing data recovery method based on PMU clustering is proposed in this paper.Firstly,a PMU clustering method based on the hierarchical clustering is proposed by analyzing the correlation of different PMUs.Then,an enhanced generative adversarial network data recovery method is constructed by using the long/short-term memory.The proposed method is able to recover the lost data under different disturbances,even under transient conditions,by using the high correlated data as the input of the neural network.The effectiveness of the method is verified by simulation and field data.The results show that the method enables to effectively recover the lost data,improving the PMU data quality to guarantee its applications in the power systems.