Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach
Synthetic Dynamic PMU Data Generation: A Generative Adversarial Network Approach
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DOI：
10.1109/sgsma.2019.8784681
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Xiangtian Zheng;Bin Wang-;Le Xie
中科院分区：
文献类型：
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作者：
Xiangtian Zheng;Bin Wang-;Le Xie
本文讨论了用于研究和教育目的的合成相量测量单元(PMU)数据的产生。由于PMU真实数据的保密性，以及无法公开获取真实的电力系统基础设施信息，缺乏可信的现实数据成为一个日益令人担忧的问题。我们提出了一种无模型的直接生成合成PMU数据的方法，而不是构建合成电网，然后通过时间仿真产生合成PMU测量数据。我们用真实的PMU数据训练产生式对抗网络(GAN)，它可以用来生成捕捉系统动态行为的合成PMU数据。为了验证GaN模拟PMU数据的时序生成能力，我们从理论上分析了GaN的系统动力学学习能力。进一步通过对合成PMU数据的量化评估，验证了GaN在合成真实样品方面的潜力，同时也认识到本文提出的GaN模型仍有改进的空间。此外，将这种产生式模型应用于PMU数据合成尚属首次。
This paper concerns with the production of synthetic phasor measurement unit (PMU) data for research and education purposes. Due to the confidentiality of real PMU data and no public access to the real power systems infrastructure information, the lack of credible realistic data becomes a growing concern. Instead of constructing synthetic power grids and then producing synthetic PMU measurement data by time simulations, we propose a model-free approach to directly generate synthetic PMU data. we train the generative adversarial network (GAN) with real PMU data, which can be used to generate synthetic PMU data capturing the system dynamic behaviors. To validate the sequential generation by GAN to mimic PMU data, we theoretically analyze GAN's capacity of learning system dynamics. Further by evaluating the synthetic PMU data by a proposed quantitative method, we verify GAN's potential to synthesize realistic samples and meanwhile realize that GAN model in this paper still has room to improve. Moreover it is the first time that such generative model is applied to synthesize PMU data.