  High voltage power transformers are one of the most critical equipments in
the electric power grid. A sudden failure of a power transformer can
significantly disrupt bulk power delivery. Before a transformer reaches its
critical failure state, there are indicators which, if monitored periodically,
can alert an operator that the transformer is heading towards a failure. One of
the indicators is the signal to noise ratio (SNR) of the voltage and current
signals in substations located in the vicinity of the transformer. During
normal operations, the width of the SNR band is small. However, when the
transformer heads towards a failure, the widths of the bands increase, reaching
their maximum just before the failure actually occurs. This change in width of
the SNR can be observed by sensors, such as phasor measurement units (PMUs)
located nearby. Identifying Code is a mathematical tool that enables one to
uniquely identify one or more {\em objects of interest}, by generating a unique
signature corresponding to those objects, which can then be detected by a
sensor. In this paper, we first describe how Identifying Code can be utilized
for detecting failure of power transformers. Then, we apply this technique to
determine the fewest number of sensors needed to uniquely identify failing
transformers in different test systems.

Signal Processing