![]() Armed with this information, an operator can use any Fault Current Locating software to pinpoint where exactly the problem is (the prediction accuracy depends on accurate, and updated GIS or network model data). This automatic comparision (machine learning) helps DFA to pin point the type of fault recorded. It also attempts to compare the sinewave of the fault to DFA's large library of sine waves which correspond to known phenomena. For instance a clamp burning on a line has a different waveform than vegetation touching the power lines. When DFA detects a fault or an anomaly on the grid, it logs the sine wave, phase, feeder, amps, and the load affected. This graphic shows the traditional operational paradigm for most utilities Used with permission from Texas A&M University. If used with the coop’s other existing streams of data like AMI, GIS, SCADA, and OMS, DFA has the potential to give an even more complete picture of grid conditions. This provides greater visibility of feeder health and events, which enables improved reliability, operational efficiency, and safety. ![]() Using DFA, the coop will be able to detect multiple types of line apparatus failures and incipient failures thus enabling proactive response to some failures and quicker response to others. In a nutshell, DFA applies sophisticated signal processing analytics to high-fidelity current and voltage waveforms that are passively measured from conventional current and potential transformers typically available in substations. The coop is doing this by using Geographic Information Systems (GIS), SCADA, OMS, AMI and just recently a new technology called Distribution Fault Anticipation (DFA) which was developed by Texas A&M University in collaboration with the Electric Power Research Institute (EPRI). Mid-South Synergy Electric Coop in Navasota Texas is one coop that is trying to utilize technology and data to improve operational efficiency, reliability and grid resilience.
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