Panel session: Detection of Incipient Faults Using Waveform Analytics

Chair: D. Russell, Texas A&M University

Utility companies operate distribution feeders in a reactive mode, waiting for failures to occur and then reacting to make repairs and restore service. This has been necessary largely because utility companies historically have lacked means to provide “visibility” regarding feeder health and operation. Electrical waveforms contain substantial important information regarding feeder faults, incipient equipment failures, and other events, but that “important” information must be extracted from the underlying, mundane data. Extracting appropriate information provides visibility of system health and operation, thereby enabling condition-based maintenance, improvements in reliability, and better operational efficiency. This panel will discuss methods and share actual experiences on incipient fault detection using waveform analytics. Improved visibility of feeder health and operation is an important feature of the future smart grid.

PRESENTATIONS AND PANELISTS:

4PESGM2512: Analysis of Low Magnitude Failure Signatures Using DFA Analytics
J. WISCHKAEMPER, Texas A&M University

14PESGM2513: Incipient Fault Detection Using IEDs and Real-Time Substation Analytics
M. MOUSAVI, ABB, Inc.

14PESGM2511: Detection, Location, and Analysis of Permanent and Incipient Faults at Con Edison
D. SABIN, Electrotek Concepts

14PESGM2514: Operational Use of Incipient Fault Detection Data for Improved Distribution Operations
J. BOWERS, Pickwick Electric Coop.

14PESGM2549: Determining the Location and Cause of Faults in Power Distribution System with an Arc Voltage Evaluation Method
M. TREMBLAY, IREQ