Since electrical machines are the largest consumer of electricity worldwide, their fault diagnostic at the incipient stage and condition monitoring is essential for better reliability, economy, and safety of operation. Out of several condition monitoring techniques, motor current signature analysis is gaining heightened popularity because of its non-invasive nature, the least number of sensors required and versatility of compatible algorithms. In this study, the best characteristics of infinite impulse response (IIR) filter are exploited to observe the broken rotor bar (BRB) frequencies with good legibility in current and voltage spectrum of the grid and inverter-fed motor, respectively. The causes of various harmonics in the stator current spectrum are first investigated for better understanding. The results are taken based on simulation and measurements taken from the laboratory setup. It is observed that a better tuning of IIR filters can make diagnostic algorithms capable of detecting the frequencies of interest by effectively attenuating the fundamental component and reducing its spectral leakage. Moreover, in case of direct torque control-based industrial inverter-fed motors, the current cannot be a good candidate for fault diagnostics rather the phase voltage can be effectively used for the detection of BRBs.

This study presents the modelling and simulation of a squirrel cage induction motor using a modified winding function‐based method. The aim of the model is to compute the motor's performance parameters, which are similar to the results obtained using the finite element method (FEM) with a considerably reduced simulation time. This fact can make this model good for iterations based optimisation and fault diagnostic algorithms. For this purpose, the actual stator and rotor winding functions and the air gap, with the inclusion of rotor and stator slots, are defined as conditional expressions. The resistances and various inductances are calculated with stepping rotor, saved in lookup tables and are used to calculate speed, torque, and currents of the motor. For the validation of the model, the frequency spectrum of stator current is compared with the one calculated using FEM and measurements taken in the laboratory setup under healthy and broken rotor bar conditions.

The uptake of electric vehicles (EV) is increasing every year and will eventually replace the traditional transport system in the near future. This imminent increase is urging stakeholders to plan up-gradation in the electric power system infrastructure. However, for efficient planning to support an additional load, an accurate assessment of the electric vehicle load and power quality indices is required. Although several EV models to estimate the charging profile and additional electrical load are available, but they are not capable of providing a high-resolution evaluation of charging current, especially at a higher frequency. This paper presents a probabilistic approach capable of estimating the time-dependent charging and harmonic currents for the future EV load. The model is based on the detailed travel activities of the existing car owners reported in the travel survey. The probability distribution functions of departure time, distance, arrival time, and time span are calculated. The charging profiles are based on the measurements of several EVs.

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This paper presents a hybrid finite element method (FEM)–analytical model of a three-phase squirrel cage induction motor solved using parallel processing for reducing the simulation time. The growing development in artificial intelligence (AI) techniques can lead towards more reliable diagnostic algorithms. The biggest challenge for AI techniques is that they need a big amount of data under various conditions to train them. These data are difficult to obtain from the industries because they contain low numbers of possible faulty cases, as well as from laboratories because a limited number of motors can be broken for testing purposes. The only feasible solution is mathematical models, which in the long run can become part of advanced diagnostic techniques. The benefits of analytical and FEM models for their speed and accuracy respectively can be exploited by making a hybrid model. Moreover, the concept of cloud computing can be utilized to reduce the simulation time of the FEM model. In this paper, a hybrid model being solved on multiple processors in a parallel fashion is presented. The results depict that by dividing the rotor steps among several processors working in parallel, the simulation time reduces considerably. The simulation results under healthy and broken rotor bar cases are compared with those taken from a laboratory setup for validation.