Squirrel cage induction motors suffer from numerous faults, for example cracks in the rotor bars. This paper aims to present a novel algorithm based on Least Squares Support Vector Machine (LS-SVM) for detection partial rupture rotor bar of the squirrel cage asynchronous machine. The stator current spectral analysis based on FFT method is applied in order to extract the fault frequencies related to rotor bar partial rupture. Afterward the LS-SVM approach is established as monitoring system to detect the degree of rupture rotor bar. The training and testing data sets used are derived from the spectral analysis of one stator phase current, containing information about characteristic harmonics related to the partial rupture rotor bar. Satisfactory and more accurate results are obtained by applying LS-SVM to fault diagnosis of rotor bar.
This paper proposes a 3D quasi-static numerical model for the magnetic induction calculation produced by the high voltage overhead power lines by using the Current Simulation Technique (CST) combined with the Particle Swarm Optimization Algorithm (PSO), in order to determine the appropriate position and number of the filamentary current loops for an accurate computation. The exact form of the catenary of the power line conductors is taken into account in this calculation. From the simulation results, the effect of the conductor sag is largely noticed on the magnetic induction distribution, especially at the mid-span length of the power line where the magnetic induction becomes very significant, the maximum magnetic induction strength at 1 m above the ground level recorded at mid-span point is 8.87 μT, at the pylon foot, the maximum value is significantly reduced to 3.94 μT. According to these values, we note that the limits set by the ICNIRP guidelines for magnetic induction strength are respected for occupational and public exposure. The simulation results of magnetic induction are compared with those obtained from the 3-D Integration method, a fairly good agreement is found.
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