Amuliu Bogdan Proca

Analytical model for permanent magnet motors with surface mounted magnets

This paper presents an analytical method of modeling permanent magnet (PM) motors. The model is dependent only on geometrical and materials data which makes it suitable for insertion into design programs, avoiding long finite element analysis (FEA) calculations. The modeling procedure is based on the calculation of the air gap field density waveform at every time instant. The waveform is the solution of the Laplacian/quasi-Poissonian field equations in polar coordinates in the air gap and takes into account slotting. The model allows the rated performance calculation but also such effects as cogging torque, ripple torque, back-EMF form prediction, some of which are neglected in commonly used analytical models.

Sliding-Mode Flux Observer With Online Rotor Parameter Estimation for Induction Motors

Field orientation techniques without flux measurements depend on the parameters of the motor, particularly on the rotor resistance or rotor time constant (for rotor field orientation). Since these parameters change continuously as a function of temperature, it is important that the value of rotor resistance is continuously estimated online. A fourth-order sliding-mode flux observer is developed in this paper. Two sliding surfaces representing combinations of estimated flux and current errors are used to enforce the flux and current estimates to their real values. Switching functions are used to drive the sliding surfaces to zero. The equivalent values of the switching functions (low-frequency components) are proven to be the rotor resistance and the inverse of the rotor time constant. This property is used to simultaneously estimate the rotor resistance and the inverse of the time constant without prior knowledge of either the rotor resistance or the magnetizing inductance. Simulations and experimental results prove the validity of the proposed approach

Identification of Variable Frequency Induction Motor Models from Operating Data

The parameters of the induction motor model vary as operating conditions change. Accurate knowledge of these parameters and their dependency on operating conditions is critical for optimal field oriented control. This paper presents a systematic approach to modeling an induction motor considering operating conditions. All parameters are assumed to vary as a function of the operating conditions. The parameters are estimated from transient data using a constrained optimization algorithm. The parameters are mapped to the operating conditions using polynomial functions and artificial neural networks. The model is validated for both steady state and transient conditions.

Sensorless sliding-mode control of induction motors using operating condition dependent models

A sensorless torque control system for induction motors has been developed. The system allows for fast and precise torque tracking over a wide range of speed. This paper also presents the identification and parameter estimation of an induction motor model with parameters varying as functions of the operating conditions encountered in hybrid electric vehicles applications. An adaptive sliding-mode speed-flux observer is developed and a cascade of discrete time sliding-mode controllers is used for flux and current control. Simulation and experimental results prove the validity of the approach.

Direct torque control of induction motors for electric propulsion systems

This paper describes an induction motor drive for use in electric propulsion. The specifics of the application (electric vehicle) require fast response and high efficiency for the drive. Direct torque control (DTC) is used to control the motor. In this control strategy, efficiency is optimized by adjusting the magnetic flux of the motor. The sensitivity of the DTC to temperature variations, leading to stator resistance changes, is eliminated by online estimation of stator resistance.

Identification of power steering system dynamic models

This paper presents the identification of the dynamic model for a power steering system constructed using a rotary valve. The steering system is divided into two subsystems: mechanical and hydraulic. Each subsystem is modeled separately. An experimental setup is designed and built to instrument the steering system. The parameters of the steering system are identified using experimental data. Several methods are proposed for model parameter estimation. The steering system is de-coupled for identification of parameters of each subsystem. The Least Square Estimation method (LSE) is used to determine the parameters that are not directly measurable or are not measurable at steady state. The developed dynamic model is validated against experimental results.

Performance Comparison of Optimally Designed Induction Motors with Aluminum and Copper Squirrel-Cages

A significant improvement in motor efficiency is expected when substituting aluminum with copper die-cast rotor in squirrel-cage induction motors due to a reduction in the rotor ohmic losses. Other performance criteria, such as starting torque, power factor, and steady-state current, may also change, and the motor may need to be redesigned to obtain optimal performance. This paper compares the performance of optimally designed squirrel-cage induction motors with aluminum and copper rotor bars with equivalent specifications. The design is optimized using three objective functions.A significant improvement in motor efficiency is expected when substituting aluminum with copper die-cast rotor in squirrel-cage induction motors due to a reduction in the rotor ohmic losses. Other performance criteria, such as starting torque, power factor, and steady-state current, may also change, and the motor may need to be redesigned to obtain optimal performance. This paper compares the performance of optimally designed squirrel-cage induction motors with aluminum and copper rotor bars with equivalent specifications. The design is optimized using three objective functions.

A virtual testbed for instruction and design of permanent magnet machines

This paper presents the application of the novel concept of virtual learning to the design of a permanent magnet machine. The software is organized in learning modules, each module consisting of: (i) an explanation layer; (ii) an exploration layer; and (iii) an (instructional) diagnostic layer. The software combines classical learning tools, such as lecture notes, with computer aided design (CAD) tools. A description of the developed system is presented.

Modeling and parameter identification of switched reluctance motors from operating data using neural networks

An appropriate model of switched reluctance motor is essential to its control implementation. A simple model of SRM consists of a nonlinear inductance and a resistance that can be estimated from standstill test data. However, this model may not represent the characteristics of SRM during operation because of the saturation and losses in phase windings and rotor body. To model this effect, one or two damper windings can be added into the model structure. This paper proposes an application of artificial neural network to identify the nonlinear model of SRMs from operating data. A recurrent neural network has been adopted to estimate the damper(s) currents from operating data. Then the damper(s) parameters can be identified using maximum likelihood estimation techniques.

Closed-loop control stability for permanent magnet synchronous motor

This paper presents a stability study of a closed-loop control for a permanent magnetic synchronous motor (PMSM). The stability of the system is investigated by analysis of the poles and zeros of the system. An optimal control strategy for the whole range of operation of the system is presented. The desired location of the poles and system zeros for the closed-loop control are determined by varying the parameters of the controller. The sensitivity analysis is performed on the system poles and then the optimum values of the controller parameters are determined. It is believed that the present method is accurate and simple without involving complicated calculation, and it may be generalized for other controllers.