Nominal and adversarial synthetic PMU data for standard IEEE test systems

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GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data.


The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes.        


A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in


Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at

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People (5)
Dr. Sai Pushpak Nandanoori

Dr. Sai Pushpak Nandanoori is a staff research engineer at PNNL. His research interests lie in developing novel system theoretic methods and data-driven methods involving Koopman operator theory to solve challenging problems in the areas of power systems, microgrids, and cyber-physical systems.

Dr. Soumya Kundu

Dr. Soumya Kundu is a Staff Research Engineer in the Optimization and Control Group at the Pacific Northwest National Laboratory. Soumya is primarily interested in solving complex engineering problems by applying tools from systems theory, control and optimization. His current research focus is...

Dr. Seemita Pal

Dr. Seemita Pal is the Team Leader for the Architecture Team in the Distributed Systems Group at Pacific Northwest National Laboratory (PNNL). Her research interests include grid cybersecurity, grid architecture, cybersecurity-focused verification and validation, power systems, and synchrophasor...

Dr. Sutanay Choudhury

Dr. Sutanay Choudhury (PI) is a Chief Scientist at PNNL with 10+ years of experience in large-scale graph analytics and machine-learning. His research focuses on learning high-fidelity representations of structure from complex data sources and development of methods for reasoning and prediction on...

Khushbu Agarwal

Khushbu Agarwal is a Senior Research Scientist in the Data Sciences and Machine Intelligence group at the Pacific Northwest National Laboratory. Her primary research interests are at the intersection of graph analytics, deep learning, and their applications to solving real-world application needs...