Record Display for the EPA National Library Catalog


Main Title Fuzzy Logic Control of AC Induction Motors.
Author Cleland, J. ; Turner, W. ; Wang, P. ; Espy, T. ; Chappell., P. J. ;
CORP Author Research Triangle Inst., Research Triangle Park, NC. ;Duke Univ., Durham, NC. Dept. of Electrical Engineering. ;Tennessee Univ., Knoxville. Dept. of Electrical Engineering.;Environmental Protection Agency, Research Triangle Park, NC. Air and Energy Engineering Research Lab.
Publisher 1992
Year Published 1992
Report Number EPA-R81-4169-03; EPA/600/A-92/101;
Stock Number PB92-180207
Additional Subjects AC motors ; Induction motors ; Fuzzy logic ; Control systems ; Energy conservation ; Pollution control ; Stationary sources ; Electric motors ; Energy consumption ; Efficiency ;
Library Call Number Additional Info Location Last
NTIS  PB92-180207 Some EPA libraries have a fiche copy filed under the call number shown. 07/26/2022
Collation 10p
The paper discusses the fuzzy logic control (FLC) of electric motors, being investigated under the sponsorship of the U.S. EPA to reduce energy consumption when motors are operated at less than rated speeds and loads. Electric motors use 60% of the electrical energy generated in the U.S. An improvement of 1% in operating efficiency of all electric motors could result in savings of 17 billion kWh per year in the U.S. New techniques are required to extract maximum performance from modern motors. One possibility, FLC, has recently demonstrated success in solving control problems of nonlinear, multivariable systems such as ac induction motors and adjustable motor-speed drives. Simulated results of a microprocessor-based fuzzy logic motor controller (FLMC) are described. The investigation includes a motor stator voltage control scheme to minimize motor input power at specified speed/torque conditions; simulation of ac motor performance; and development of a FLMC for optimized motor efficiency. Simulated FLMC results compare favorably with other motor control approaches. Potential energy savings are quantitated based on the preliminary predictions of FLMC performance.