Waveform Analytics-based Improvements in Situational Awareness, Feeder Visibility, and Operational Efficiency | Conference Paper individual record
abstract

© 2014 IEEE. The proliferation of \"smart\" devices on distribution feeders in the past decade has resulted in a deluge of data. Most utilities recognize that recorded waveforms and other data contain information that could enable them to operate more effectively. In practice, however, most utilities find themselves confronted with an intimidating amount of, without tools or expertise to differentiate important data from pedestrian. Typically, data analysis is performed manually, off-line, in response to specific perceived problems. Even if the utility has the requisite expertise and the time necessary to identify and interpret the relevant data, post hoc analysis does not generally provide real-time system visibility or situational awareness that would enable operational improvements. For multiple years, supported by EPRI and EPRI-member utility companies, Texas A&M researchers have used sensitive, high-fidelity waveform recorders to collect data from scores of feeders, using technology readily achievable with modern electronics. This has created the most comprehensive extant database of waveforms related to incipient failures and feeder events. Based on that database and experience, they developed sophisticated waveform analytics and reporting methods. Dubbed distribution fault anticipation (DFA) technology, the system acquires high-fidelity waveforms from conventional CTs and PTs and then uses automated processes to apply analytics to those waveforms and thus report events and conditions. This provides personnel with real-time visibility of feeder events and conditions, including incipient failures. This newfound visibility, or awareness, enables improved reliability, improved operational efficiency, and true condition-based maintenance.

author list (cited authors)
Wischkaemper, J. A., Benner, C. L., Russell, B. D., Manivannan, K. M., & IEEE, ..
publication date
2014
keywords
  • Incipient Faults
  • Condition Based Maintenance
  • Fault Anticipation
  • Smart Grid