Creation of Synthetic Networked PMU Data: A Generative Adversarial Network Approach

Abstract

This paper introduces a machine learning-based approach to synthetically creating realistic phasor measurement unit (PMU) data streams of multiple transient types. In contrast to the existing literature of transient simulation-based data generation methods, we propose a generative adversarial network (GAN) based approach to learning directly from the historical data and simultaneously reproduce multiple PMU data streams. The synthetic PMU data streams reflect meaningful dynamic characteristics which observe first principles such as Kirchhoff's laws. The efficacy of this approach is demonstrated by numerical studies on the IEEE 39-bus system. We validate the fidelity and flexibility of the synthetic data via statistical resemblance and modal analysis approaches. Finally we illustrate a practical application scenario for the usage of the synthetic PMU data, i.e. leverage the synthetic data to improve the performance of the event classification algorithms.

arXiv commentThis manuscript has been submitted to IEEE Transactions on Power Systems

References in corpus (6)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Which Training Methods for GANs do actually Converge?
• MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
• Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
• Predicting Real-Time Locational Marginal Prices: A GAN-Based Video Prediction Approach
• Generative Adversarial Networks for Real-time Stability of Inverter-based Systems