71 research outputs found
Learning Exact Topology of a Loopy Power Grid from Ambient Dynamics
Estimation of the operational topology of the power grid is necessary for
optimal market settlement and reliable dynamic operation of the grid. This
paper presents a novel framework for topology estimation for general power
grids (loopy or radial) using time-series measurements of nodal voltage phase
angles that arise from the swing dynamics. Our learning framework utilizes
multivariate Wiener filtering to unravel the interaction between fluctuations
in voltage angles at different nodes and identifies operational edges by
considering the phase response of the elements of the multivariate Wiener
filter. The performance of our learning framework is demonstrated through
simulations on standard IEEE test cases.Comment: accepted as a short paper in ACM eEnergy 2017, Hong Kon
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
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