6 research outputs found
Online Learning of Power Transmission Dynamics
We consider the problem of reconstructing the dynamic state matrix of
transmission power grids from time-stamped PMU measurements in the regime of
ambient fluctuations. Using a maximum likelihood based approach, we construct a
family of convex estimators that adapt to the structure of the problem
depending on the available prior information. The proposed method is fully
data-driven and does not assume any knowledge of system parameters. It can be
implemented in near real-time and requires a small amount of data. Our learning
algorithms can be used for model validation and calibration, and can also be
applied to related problems of system stability, detection of forced
oscillations, generation re-dispatch, as well as to the estimation of the
system state.Comment: 7 pages, 4 figure
Learning from power system data stream: phasor-detective approach
Assuming access to synchronized stream of Phasor Measurement Unit (PMU) data
over a significant portion of a power system interconnect, say controlled by an
Independent System Operator (ISO), what can you extract about past, current and
future state of the system? We have focused on answering this practical
questions pragmatically - empowered with nothing but standard tools of data
analysis, such as PCA, filtering and cross-correlation analysis. Quite
surprisingly we have found that even during the quiet "no significant events"
period this standard set of statistical tools allows the "phasor-detective" to
extract from the data important hidden anomalies, such as problematic control
loops at loads and wind farms, and mildly malfunctioning assets, such as
transformers and generators. We also discuss and sketch future challenges a
mature phasor-detective can possibly tackle by adding machine learning and
physics modeling sophistication to the basic approach
Locating line and node disturbances in networks of diffusively coupled dynamical agents
A wide variety of natural and human-made systems consist of a large set of
dynamical units coupled into a complex structure. Breakdown of such systems can
have dramatic impact, as for instance neurons in the brain or lines in an
electric grid. Preventing such catastrophic events requires in particular to be
able to detect and locate the source of disturbances as fast as possible. We
propose a simple method to identify and locate disturbances in networks of
coupled dynamical agents, relying only on time series measurements and on the
knowledge of the (Kron-reduced) network structure. The strength and the appeal
of the present approach lies in its simplicity paired with the ability to
precisely locate disturbances and even to differentiate between line and node
disturbances. If we have access to measurement at only a subset of nodes, our
method is still able to identify the location of the disturbance if the
disturbed nodes are measured. If not, we manage to identify the region of the
network where the disturbance occurs.Comment: 15 pages, 5 figure