Title :
Induction Machine Fault Identification using Particle Swarm Algorithms
Author :
Etny, S.A. ; Acarlney, P.P. ; Zahawi, B. ; Giaouris, D.
Author_Institution :
Sch. of Electr. & Comput. Eng., Newcastle Univ.
Abstract :
The principles of a new technique using particle swarm algorithms for condition monitoring of the stator and rotor circuits of an induction machine is described in this paper. Using terminal voltage and current data, the stochastic optimization technique is able to indicate the presence of a fault and provide information about the location and nature of the fault. The technique is demonstrated using experimental data from a laboratory machine with both stator and rotor winding faults.
Keywords :
asynchronous machines; condition monitoring; fault location; particle swarm optimisation; stochastic programming; condition monitoring; fault identification; fault location; induction machine; particle swarm algorithm; stochastic optimization; Circuit faults; Condition monitoring; Fault diagnosis; Induction machines; Laboratories; Machine windings; Particle swarm optimization; Stator windings; Stochastic processes; Voltage; Condition monitoring; induction machine; stochastic optimization; swarm algorithms;
Conference_Titel :
Power Electronics, Drives and Energy Systems, 2006. PEDES '06. International Conference on
Conference_Location :
New Delhi
Print_ISBN :
0-7803-9772-X
Electronic_ISBN :
0-7803-9772-X
DOI :
10.1109/PEDES.2006.344310