2 research outputs found
An MILP Approach for Distribution Grid Topology Identification using Inverter Probing
Although knowing the feeder topology and line impedances is a prerequisite
for solving any grid optimization task, utilities oftentimes have limited or
outdated information on their electric network assets. Given the rampant
integration of smart inverters, we have previously advocated perturbing their
power injections to unveil the underlying grid topology using the induced
voltage responses. Under an approximate grid model, the perturbed power
injections and the collected voltage deviations obey a linear regression setup,
where the unknown is the vector of line resistances. Building on this model,
topology processing can be performed in two steps. Given a candidate radial
topology, the line resistances can be estimated via a least-squares (LS) fit on
the probing data. The topology attaining the best fit can be then selected. To
avoid evaluating the exponentially many candidate topologies, this two-step
approach is uniquely formulated as a mixed-integer linear program (MILP) using
the McCormick relaxation. If the recovered topology is not radial, a second,
computationally more demanding MILP confines the search only within radial
topologies. Numerical tests explain how topology recovery depends on the noise
level and probing duration, and demonstrate that the first simpler MILP yields
a tree topology in 90% of the cases tested.Comment: Accepted at IEEE PowerTech 201
Using Neural Networks to Detect Line Outages from PMU Data
We propose an approach based on neural networks and the AC power flow
equations to identify single- and double-line outages in a power grid using the
information from phasor measurement unit sensors (PMUs) placed on only a subset
of the buses. Rather than inferring the outage from the sensor data by
inverting the physical model, our approach uses the AC model to simulate sensor
responses to all outages of interest under multiple demand and seasonal
conditions, and uses the resulting data to train a neural network classifier to
recognize and discriminate between different outage events directly from sensor
data. After training, real-time deployment of the classifier requires just a
few matrix-vector products and simple vector operations. These operations can
be executed much more rapidly than inversion of a model based on AC power flow,
which consists of nonlinear equations and possibly integer / binary variables
representing line outages, as well as the variables representing voltages and
power flows. We are motivated to use neural network by its successful
application to such areas as computer vision and natural language processing.
Neural networks automatically find nonlinear transformations of the raw data
that highlight useful features that make the classification task easier. We
describe a principled way to choose sensor locations and show that accurate
classification of line outages can be achieved from a restricted set of
measurements, even over a wide range of demand profiles