Learning from Power Signals: An Automated Approach to Electrical
  Disturbance Identification Within a Power Transmission System
Jonathan D. Boyd Joshua H. Tyler Anthony M. Murphy Donald R. Reising
As power quality becomes a higher priority in the electric utility industry,
the amount of disturbance event data continues to grow. Utilities do not have
the required personnel to analyze each event by hand. This work presents an
automated approach for analyzing power quality events recorded by digital fault
recorders and power quality monitors operating within a power transmission
system. The automated approach leverages rule-based analytics to examine the
time and frequency domain characteristics of the voltage and current signals.
Customizable thresholds are set to categorize each disturbance event. The
events analyzed within this work include various faults, motor starting, and
incipient instrument transformer failure. Analytics for fourteen different
event types have been developed. The analytics were tested on 160 signal files
and yielded an accuracy of ninety-nine percent. Continuous, nominal signal data
analysis is performed using an approach coined as the cyclic histogram. The
cyclic histogram process will be integrated into the digital fault recorders
themselves to facilitate the detection of subtle signal variations that are too
small to trigger a disturbance event and that can occur over hours or days. In
addition to reducing memory requirements by a factor of 320, it is anticipated
that cyclic histogram processing will aid in identifying incipient events and
identifiers. This project is expected to save engineers time by automating the
classification of disturbance events and increase the reliability of the
transmission system by providing near real time detection and identification of
disturbances as well as prevention of problems before they occur.