The tracking of induction motor´s faulty lines through particle swarm optimization using chaos

This paper focuses on the tracking of faulty lines of induction motors operating under fault. The approach suggested in this paper deals with the particle swarm optimization to explore the search space which incorporates chaos. This one should be an opportunity to extract the principal faulty lines, in a current spectrum, dealing with a rotor broken bar whatever the motor operates at variable frequency or not. The algorithm uses an adaptive inertia weight which is added a chaotic process. The ability of chaos to estimate the line frequency and the fault line frequencies is made. The induction motor operates at steady state and under one full broken bar. Experiments prove that particle swarm optimization approach is an efficient method both to solve the optimization problem and to quickly extract information from a spectrum. Moreover, the paper shows the interest of chaos since it enlarges the searching space in quasi steady state.