Predictive Controller for Power Supply Prognostication

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$99,959.00
Award Year:
2006
Program:
SBIR
Phase:
Phase I
Contract:
FA8650-06-M-2636
Award Id:
79394
Agency Tracking Number:
F061-178-2944
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
27 Drydock Avenue, Boston, MA, 02210
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
157257502
Principal Investigator:
Leo Casey
VP and Chief Technology Officer
(617) 897-2435
leo.casey@satcon.com
Business Contact:
William O'Donnell
General Manager
(617) 897-2408
bill.odonnell@satcon.com
Research Institute:
n/a
Abstract
SatCon will demonstrate the feasibility of prognostic control techniques for switching power converters utilizing emerging Silicon Carbide (SiC) power devices, while using predictive modeling to avoid excessive sensing requirements in the prognostics. Silicon Carbide is an emerging technology, that promises dramatic enhancements in size, weight, and reliability of power converters, and therefore significant work is required to accurately model the aging and wear out mechanisms. The significant benefits of SiC technology, and the increased confidence level in critical survivability of the converter, for manned and unmanned airborne applications, motivates this work. The prognostic control techniques will accelerate adoption and application of this and other new semiconductor technologies, by addressing concerns with the new devices and unknown failure mechanisms. Prognostic techniques focus on detection or prediction of developing failure mechanisms, invariably requiring extensive sensing to form an effective signature of the power converter and its operating environment. Changes in the signature indicate changes or trends system. This type of prognostic system is quite successful but can require excessive use of sensors, which can compromise the overall reliability of the system and increase both cost and complexity. Predictive modeling based on accurate component and system models can greatly reduce the sensing requirements.

* information listed above is at the time of submission.

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