Thermal modelling of induction machine using the lumped parameter model

Thesis Abstract

Temperature rise is of much concern in the short and long term operations of induction machine, the most useful industrial work icon. This work examines induction machines mean temperatures at the different core parts of the machine. The system’s thermal network is developed, the algebraic and differential equations for the proposed models are solved so as to ascertain the thermal performances of the machine under steady and transient conditions. The lumped parameter thermal method is used to estimate the temperature rise in induction machine. This method is achieved using thermal resistances, thermal capacitances and power losses. To analyze the thermal process, the 7.5kW machine is divided geometrically into a number of lumped components, each component having a bulk thermal storage and heat generation and interconnections to adjacent components through a linear mesh of thermal impedances. The lumped parameters are derived entirely from dimensional information, the thermal properties of the materials used in the design, and constant heat transfer coefficients. The thermal circuit in steady-state condition consists of thermal resistances and heat sources connected between the components nodes while for transient analysis, the thermal capacitances were used additionally to take into account the change in internal energy of the body with time. In the course of the simulation using MATLAB, the response curves showing the predicted temperature rise for the induction machine core parts were obtained. To find out the effect of the decretization level on the symmetry, the two different thermal models, the SIM and the LIM models having eleven and thirteen nodes respectively were considered and the results from the two models were compared. The resulting predicted temperature values together with other results obtained in this work provide useful information to designers and industries on the thermal characteristics of the induction machine.