SBIR/STTR Research Award
Advanced Transformer Level Failure Prevention And Load Balancing Through Embedded AI and IoT
Award amount
$199,415Program and phase
SBIR · Phase IAward year
2024Award details
- Awarding agency
- Department of Energy
- Branch
- Not reported
- Recipient company
- RESONET ENERGY CORPORATION
- Proposal award date
- 2024-07-22
- Contract end date
- 2025-04-21
- UEI
- ZEG4VUBJKC55
- Solicitation number
- DE-FOA-0003202
- Topic code
- C58-09a
Research abstract
The Dept of Energy has highlighted a problem that is blighting many American Citizens of assured electricity supply ľ The nation is running out distribution transformers and post COVID 19 the global supply chain is compromised meaning that even if utilities wanted to buy them at an inflated price of 900% they cannot. That means when a transformer stops working those attached homes may not have supply for anywhere up to two years. Resonet Energy systems of Oregon will research, prototype and deliver ready-to-scale designs for a system that will extend the operational lives of transformers and ensure a degree of service to the consumers when the system is used as designed. The Resonet systems is also designed to be scaled fast across the nation and therefore plan to make a major impact sooner on American energy consumers lives. We have designed that the system can be attached via a hot pole system within 6 minutes for a single-phase transformer. Each phase would add about 2 minutes. Once the system is tested and effective in Phase 1 we plan to refine the design for hardware, build a resilient microservices cloud platform and then scale with partner utilities to bring protection and relief to the high tension in the electricity distribution marketplace.
Technology keywords
Not reported
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
- Transformer Condition Monitoring
- Extending Distribution Transformer Lifetime and Reliability with Passive Thermal Management
- Distribution Transformer Asset Health Prediction and Lifetime Extension via Integrated Real-Time Sensing and Advanced Analytics