SBIR/STTR Research Award

SBIR Phase II: Adaptive Methods for Sensorless Estimation of Induction Motor Efficiency

Award identifier 36758c2382cdea96446851935020ed2f843cabf9a8c21bfd1838e0639a0839aaAwarding agency National Science Foundation

Award amount

$500,000

Program and phase

SBIR · Phase II

Award year

2009

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
Veros Systems, Inc.
Proposal award date
Not reported
Contract end date
Not reported
UEI
Not reported
Solicitation number
NSF 07-586
Topic code
EO

Research abstract

This Small Business Innovation research (SBIR) Phase II project will develop and field-test a system for obtaining accurate on-line, in-service estimates of energy efficiency of industrial electric motors. The effort will further exploit the basic technology at the core of the condition monitoring & assessment (CM&A) product being developed by Veros Systems. Inc., and will become a key feature of that product. This monitoring technology is sensorless, in that only electrical measurements, i.e. voltages and currents available at the motor control centers, are utilized. No information from mechanical sensors, such as speed, torque, vibration or temperature, is necessary. Consequently, this reliable and effective CM&A technology is cost-effective and cost-scalable. The proposed approach to efficiency estimation is based on employing the raw electrical measurements that are collected for use by the existing CM&A product framework, and augmenting them with adaptive filters for accurate estimation. The Phase II research plan calls for the refinement of the online, in-service efficiency estimation algorithms defined in the previous Phase I effort. The broader impacts of the project include awareness of the importance of energy efficiency in industrial motors, which account for about 25% of all electricity sold in the U.S. Widespread adoption of this energy conversion efficiency estimation technology could reduce the total energy consumption by industrial motors up to an estimated 18%. These energy conversion efficiency costs, together with the costs of maintaining electric motors and the costs of lost production associated with motor downtime are among the most significant controllable costs of industrial establishments. Even a modest adoption of more effective CM&A and efficiency estimation technologies would eliminate some fraction of this waste and have a significant impact on the U.S. economy, while enabling clients to reduce their energy costs, increase profitability, reduce fuel imports and lower greenhouse emissions.

Technology keywords

Not reported

Agency intelligence

National Science Foundation

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