Stator winding inter‐turn short‐circuit detection in induction motors by parameter identification

Abstract

The estimation of induction motor (IM) parameters is essential for monitoring, diagnosis and control. Within the framework of the diagnosis of the stator windings faults, this study presents two algorithms for off-line and on-line interturn short-circuits detection by parameter identification. The first approach combines trust-region and Broyden-Fletcher-Goldfard-Shano methods to exploit both of their advantages. This algorithm is used for off-line minimisation of the objective function represented by a quadratic-criterion. The second algorithm, based on the moving horizon estimation, combines off-line measurement and on-line parameter estimation with high sampling time to monitor in real-time the stator inter-turn faults in IMs. Since it requires a mathematical model suited for fault modelling, a faulty IM model is presented. Due to the parameters values of this model present magnitude order very different, the normalisation of these parameters is proposed to obtain the sensitivity functions with the same magnitude order. The estimation results, which used simulated as well as experimental data, are presented to show the effectiveness and the advantage of the proposed algorithms for stator inter-turn short-circuits faults detection in IMs.

A Modified Winding Function Theory and Firefly Algorithm based Optimization for Diagnosis of Eccentricity in Induction Motors

“…In [22], a model was prepared by short-circuiting the inter-turns of windings using short circuiting elements to propose parameter estimation technique for the diagnosis of inter-turn short circuit fault in the stator of squirrel-cage induction motor. However, the key parameters were approximated in both online and offline mode to detect the fault.…”

“…The movement of ith, firefly towards brighter jth firefly is given by: (22) where the second part is due to attractiveness and is given by: (23) with is attraction at and the third part is due to randomization with as the randomization parameter and being a vector for random numbers drawn from either Gaussian or Uniform distribution.…”

Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

“…Electrical fault is influenced by power quality supplied by AC grid, frequency variations, voltage disturbances, and load variation. Another fault is short circuits stator winding [3,[11][12][13][14]. About more than one-third of the total fault occurring in the induction motor is that of stator winding fault.…”

“…Motor current signature analysis (MCSA) is an important technique adopted for condition monitoring. Induction motor faults like bearing problems, broken rotor bar, eccentricity abnormalities, and stator winding faults cause variation in amplitude and frequency of motor current signature [3][4][5][6][7][8][9][11][12][13][14].…”

Mechanical Fault Detection on Electrical Machine: Thermal Analysis of Small Brushed DC Motor with Faulty Bearing

“…Generally, thermal signal can be used to detect mechanical fault on electric motor. The other category is electrical fault it including stator winding fault, rotor broken bar, wire insulated damage and external sensor devices [3]. Indeed, the most recurrent faults which can be found in the stator part such as short circuits between turns or between phases commonly due to an abnormal elevation of temperature.…”