Analysis of Electric Motor Magnetic Core Loss under Axial Mechanical Stress.

The electrical machine core is subjected to mechanical stresses during manufacturing processes. These stresses include radial, circumferential and axial components that may have significant influence on the magnetic properties and it further leads to increase in iron loss and permeability in the stator core. In this research work, analysis of magnetic core iron loss under axial mechanical stress is investigated. The magnetic core is designed with Magnetic Flux Density (MF) ranging from 1.0 T to 1.5 T with estimated dimensions under various input voltages from 5 V to 85 V. Iron losses are predicted by the axial pressure created manually wherever required and is further applied to the designed magnetic core in the range of 5 MPa to 50 MPa. Finite element analysis is employed to estimate the magnetic core parameters and the magnetic core dimensions. A ring core is designed with the selected dimensions for the experimental evaluation. The analysis of iron loss at 50Hz frequency for non-oriented electrical steel of M400-50A is tested experimentally using the Epstein frame test and force-fit setup test. Experimental evaluation concludes that the magnetic core saturates when it reaches its knee point of the B-H curve of the chosen material and also reveals that the axial pressure has a high impact on the magnetic properties of the material.

ANN-based dynamic control and energy management of inverter and battery in a grid-tied hybrid renewable power system fed through switched Z-source converter.

The multidimensional purposes of grid-tied hybrid renewable system such as tracking of maximum power, increasing the power conversion efficiency, reducing the harmonic distortions in the injected current and control over power injected into the grid are presented in this paper by developing a laboratory-scale setup. To ensure continuous current operation at the shoot through mode of grid connected inverter, a switched Z-source converter is utilized at the PV side. The PWM rectifier connected with the wind turbine transforms AC power into dc. Individual power converters with conventional PI controllers have been dedicated for each power source, and control strategy uses only one reference voltage so as to increase the maximum power tracking speed from both PV and wind sources. The battery energy management is performed by artificial neural network (ANN) to enhance the stable power flow and increase the lifespan of the storage system. Finally, the voltage at the point of common coupling is fed to ANN-based space vector-modulated three-phase inverter and the converted AC power is injected to the grid. The overall system performance is measured by estimating the quality of injected power. A stable operation of the proposed microgrid system is verified by varying input and load at the grid. A continuous-time simulation model is realized in MATLAB and is validated using experimental prototype. This benchmark system provides various research scopes for the future smart grids.