# Licensing Opportunity: Envelope Detector-based Phase Fault Detection for Smart Grid Systems Canonical: https://abierto.us/opportunities/20240911j - Solicitation number: 2024-09-11-J - Notice type: Special notice - Status: Closed. Deadline was October 26, 2024 at 5:00 PM EDT - Department: Department of Energy - Contracting office: ORNL Ut-Battelle LLC-DOE Contractor (899042) - Place of performance: Oak Ridge, Tennessee - County: Anderson County (FIPS 47001). https://abierto.us/counties/anderson-county-tn-47001 - City: Oak Ridge. https://abierto.us/cities/oak-ridge-tn-4755120 - First posted: September 11, 2024 - Last posted: September 11, 2024 - SAM.gov: https://sam.gov/workspace/contract/opp/dffceecba0a24f7b907b57a8d6b10c9e/view ## Description **Invention Reference Number:** 202405667 Faults in the power grid cause many problems that can result in catastrophic failures. Real-time fault detection in the power grid system is crucial to sustain the power systems' reliability, stability, and quality. However, conventional fault detection systems are vulnerable and do not work on real-world data. This technology is an envelope-detector-based phase fault detection algorithm that can accurately identify the fault region compared to conventional methods. Description This technology is a phase fault detection algorithm for power grids developed by employing an envelope detector method. This method diagnoses faults among phases and defines fault areas in the incoming signal. The designed algorithm consists of three steps: analytical signal conversion, complex magnitude, and fault detection. First an analytical signal is obtained from the incoming power signal to determine the amplitude and phase of the signal. Then a complex magnitude operation is applied to display changes in amplitude. Then it identifies the distortion signal in terms of the type of error and size. The technology not only detects the fault region but also diagnoses whether the detected fault is a ground error or not. This technology works with both simulated substation data containing faults that occurred in the power sensor and real-world data. The technology can be used to train machine learning algorithms, providing good data without excess data that’s not useful. Benefits Accurately detects fault region compared to conventional methods Can also diagnose if fault is a ground error Detects distortions and anomalies accurately and precisely Can detect problems even with no previous knowledge about data Captures area of interest to provide good data for machine learning Increases prediction accuracy of machine learning algorithm Clips out unnecessary parts of the signal Applications and Industries Power grid operators Electric utilities Contact To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051. ## Publications - September 11, 2024: Special notice, due October 26, 2024 at 5:00 PM EDT. Notice dffceecba0a24f7b907b57a8d6b10c9e. https://sam.gov/workspace/contract/opp/dffceecba0a24f7b907b57a8d6b10c9e/view ## Points of contact - Alex DeTrana, detranaag@ornl.gov, 8653410423 --- Source: SAM.gov Contract Opportunities bulk extract. Confirm deadlines on SAM.gov before responding. Cite https://abierto.us/opportunities/20240911j.