This paper proposes a data-driven method to pinpoint
the source of a new emerging dynamical phenomenon in
the power grid, referred to “forced oscillations” in the difficult
but highly risky case where there is a resonance phenomenon. By
exploiting the low-rank and sparse properties of synchrophasor
measurements, the localization problem is formulated as a
matrix decomposition problem, which can be efficiently solved by
the exact augmented Lagrange multiplier algorithm. An online
detection scheme is developed based on the problem formulation.
The data-driven nature of the proposed method allows for a very
efficient implementation. The efficacy of the proposed method is
illustrated in a 68-bus power system. The proposed method may
possibly be more broadly useful in other situations for identifying
the source of forced oscillations in resonant systems.
Index Terms—Forced oscillations, resonant systems, phasor
measurement unit (PMU), robust principal component analysis
(RPCA), Big Data.
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Realistic Synchrophasor Data Generation for Anomaly Detection and Event Classification
The push to automate and digitize the electric grid has led to widespread installation of Phasor Measurement Units (PMUs) for improved real-time wide-area system monitoring and control. Nevertheless, transforming large volumes of highresolution PMU measurements into actionable insights remains challenging. A central challenge is creating flexible and scalable online anomaly detection in PMU data streams. PMU data can hold multiple types of anomalies arising in the physical system or the cyber system (measurements and communication networks). Increasing the grid situational awareness for noisy measurement data and Bad Data (BD) anomalies has become more and more significant. Number of machine learning, data analytics and physics based algorithms have been developed for anomaly detection, but need to be validated with realistic synchophasor data. Access to field data is very challenging due to confidentiality and security reasons. This paper presents a method for generating realistic synchrophasor data for the given synthetic network as well as event and bad data detection and classification algorithms. The developed algorithms include Bayesian and change-point techniques to identify anomalies, a statistical approach for event localization and multi-step clustering approach for event classification. Developed algorithms have been validated with satisfactory results for multiple examples of power system events including faults and load/generator/capacitor variations/switching for an IEEE test system. Set of synchrophasor data will be available publicly for other researchers.
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- NSF-PAR ID:
- 10187033
- Date Published:
- Journal Name:
- 8th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
