This paper presents an improved method for the broken rotor bar detection in a squirrel-cage induction motor (IM). The method is based on the spectral analysis of the transient stator current signal during the counter-current braking (CCB). Contrary to the classical CCB, the proposed method results in the low braking current, which is a small fraction of the rated value and serves as a broken bar detection test signal only. This kind of the broken rotor bar fault diagnosis is independent of loading conditions and can be carried out even for a free shaft motor. The existence of spectral components in the low CCB current signal indicating the faulty conditions is first proven with the generalized theory of symmetrical components. The method is then verified via simulations, using an IM model based on the finite-element analysis and the magnetically coupled multiple circuits approach. Afterward, the experiments are performed, showing a good agreement with both the theoretical prediction and the simulation results, confirming the presence of the fault-induced components in the stator current spectra.

Power systems proliferated by distributed generation sources are becoming increasingly prone to frequency and voltage disturbances. These problems are exacerbated in microgrids since they have fewer intrinsic disturbance-rejecting measures and features. To increase the reliability and stability of emerging power systems, the advanced control structures of the distributed generation sources based on power electronics devices must be deployed during suboptimal operating conditions. The aggravating circumstance is that both voltage and frequency excursion can be transient and long-lasting and consequently can occur simultaneously. The algorithm proposed in this paper integrates voltage support (nominal voltage restoration) and inertia emulation features with the comprehensive current references management scheme, thus securing improved grid operating conditions during several different faults and occurrences. The control algorithm is developed and tested in the context of a small microgrid, but it can be applied with minimal alterations in traditional grids, also. To prove that it is possible to decrease simultaneously voltage unbalances and frequency deviations, a test microgrid consisting of a synchronous generator, photovoltaic system, battery storage system, and controllable balanced and unbalanced loads was developed in a hardware-in-the-loop environment.

In this paper, an effective procedure for broken rotor bar (BRB) fault detection in a three-phase squirrel-cage induction machine (SCIM) is proposed. This approach relies on a motor current signature analysis (MCSA) by observing the specific fault-related current component generated by applying the DC injection braking method. Unlike the traditional MCSA, which is commonly focused on the detection of BRB sidebands around the fundamental current component, the proposed methodology introduces a new BRB feature in the current spectrum which makes it much easier for identification. The distinctive time-frequency evolution pattern of this feature provides the reliable identification of BRBs, even under no-load operating conditions, thus overcoming the major drawback of traditional MCSA-based methods. Fault severity classification is easily performed through the magnitude inspection of the BRB fault-related current component. In addition, the proposed approach does not require high-complexity signal processing algorithms to achieve reliable results. The proposed concept is presented theoretically, assisted by a magnetically coupled multiple circuit model of the SCIM, both with healthy and faulty rotor bars. Finally, the experimental tests validate the proposed methodology and demonstrate its effectiveness and usefulness.

With the increase in power installed from distributed generation sources (DGS), many problems have appeared following the utilisation of novel topologies and structures. Higher-level controllers for the safe operation of power grids with a high penetration of DGS must be developed. This task becomes difficult as the size and complexity of modern power systems have increased. The development and testing of these systems under strict human supervision controlling each step of the testing procedure is no longer viable. This paper presents a comprehensive integrated platform based on an HIL emulator for the development and testing of electric power systems rich in DGS. Essentially, DGS is considered a controllable black-box system. A detailed calculation of the power references for an arbitrary DGS is shown. The paper also describes the utilisation of enhanced Python scripting which was designed to load all network-related data, start the emulation, collect the data, and generate reports. Furthermore, fully developed controller built-in oscilloscope functionality is introduced. The incorporated oscilloscope facilitates non-intrusive DGS controller data acquisition and analysis, reducing the need for external measurements. The proposed framework was verified by a case study in which a grid topology consisting of 26 nodes was emulated on parallel CHIL infrastructure. Four active and twenty-two passive nodes were instantiated and their reaction to a distribution network grid faults was examined. DGS converter control was executed in external controllers outside of the dedicated HIL environment. All DGSs were governed by different control strategies, regarding maximisation of active/reactive power production, minimisation of the active/reactive power oscillations, adjustable positive/negative components ratio, or without any limitations under voltage disturbances. Real-time experiments confirmed the usefulness and flexibility of the proposed framework concept.

In this paper, the enhanced nonlinear magnetic model of the low voltage interior permanent magnet synchronous machine (IPMSM) is developed using the frozen permeability (FP) technique in finite element analysis (FEA) FEMM 4.2 software. The magnetic model is derived by obtaining flux saturation maps for a wide range of dq stator currents. Furthermore, the FEA FP technique accounts for the corresponding offset in the flux maps due to the excitation of the permanent magnets, and well as for fitting the coefficients for the curve-fitting procedure. In order to demonstrate the usefulness of the proposed magnetic model, a nonlinear control strategy based on the maximum torque per ampere (MTPA) optimal algorithm for IPMSM is employed. The magnetic model and the MTPA control strategy are validated through a variety of computer simulations based on FEMM 4.2 and MATLAB R2023a software, as well as on a real IPMSM electric vehicle (EV) traction drive experimental setup.

This paper addresses the need for a precise assessment of the impact of magnetic slot wedges on no-load loss distribution by developing a localized loss calculation model based on Finite Element Analysis. The developed model demonstrated accurate prediction in quantifying losses, as validated through experimental results. Experimental validation utilized a grid-supplied, 1250 kW, 690 V, 6-pole squirrel cage induction motor by measuring total no-load losses before and after the insertion of magnetic slot wedges. Applying magnetic slot wedges significantly reduces rotor loss components, primarily in the cage winding and iron core. In contrast, the stator's magnetic core exhibited a less pronounced loss reduction. The main results of this research show that the specific construction of low-voltage and high-power induction machines can lead to unexpected increases in commonly negligible no-load rotor losses, mainly when magnetic slot wedges are not applied. Only through the developed local loss model is a comprehensive evaluation of the benefits of magnetic slot wedges achieved, highlighting their role in optimizing overall loss distribution within high-power induction machines.