Loss Minimization In Induction Motor Research Articles

Loss Minimization of Induction Motors in a Transient Regime

This paper presents a novel design of an energy optimization approach for Induction Motor (IM) drives. Differently from former optimal control studies, the proposed control strategy is based on a designed cost function given as a weighting sum of power and energy models. The problem to be considered is to achieve the loss minimization of IM drives for any possible constraints imposed by the machine power quantity, such as ramp inputs in accelerate and decelerate modes and during different torque levels. The suggested energy optimization strategy, using the concept of Rotor Field Oriented Control (RFOC), allows a low-order current-fed IM model by means of a high-gain current control loop, which considers the stator currents as new inputs to the model and the rotor flux as a main state variable. An optimal rotor flux trajectory is determined by an off-line algorithm based on an optimal closed-loop control problem resulting in the Hamilton Jacobi Bellman equation. The resolution leads to an analytical solution which is implemented to RFOC and performs loss energy minimization during transient speed. Aiming to check the validity of the proposed strategy, a comparison study is conducted between RFOC operating with the optimal rotor flux and RFOC using a rated flux norm. The simulation and experimental results obtained for a 1.5 KW laboratory IM demonstrate the effectiveness of the proposed strategy.

Trajectory Optimization for Loss Minimization in Induction Motor Fed Elevator Systems

This paper presents a novel loss minimization technique for induction motor driven elevator systems (IMDES) to determine an energy optimum velocity pattern. The objective function consists of a differential equation relating total power losses in the induction motor to its rotor speed. The constraints are provided in terms of velocity and acceleration governed by passenger comfort. Since the velocity and acceleration are state variables, Pontryagin's minimum principle is used to determine the most optimum trajectory by forming an appropriate Hamiltonian function. Boundary conditions are specified and used to obtain the coefficients of the rotor speed equation. As the velocity is optimized, the total travel time is not fixed and treated as a variable. A fixed-point iterative procedure is used to estimate the travel time. Additional loss minimization is incorporated by optimizing the machine flux for the constrained portion of the velocity pattern. An algorithm for implementing the optimization scheme is explained in detail. The theoretical claims are supported with analytical and experimental results. Proposed technique is developed for IMDES, but it can be easily adapted to other drive applications as well.

Vector control for loss minimization of induction motor using GA–PSO

This paper deals with the GA–PSO (genetic algorithm–particle swarm optimization) based vector control for loss minimization operation of induction motor. It is estimated that more than around 50% of the world electric energy generated is consumed by electric machines such as induction motor, dc motor. So, improving efficiency in electric drives is important and control strategy for minimum energy loss is needed as one of optimal operation strategies. The vector control of induction motor has been widely used to operate in a wide speed range by using flux weakening at rated speed. However, it is still necessary to advance because of coupling is behavior between fluxes in motor. In this paper, vector control approach is suggested for an optimal operation of induction motor using variable acceleration and GA–PSO tuning method through simulation. We can obtain satisfactory results for energy saving control.

Neural network flux optimization using a model of losses in induction motor drives

This paper focuses on loss minimization in induction motor (IM) drives. In many applications Induction Motor drives work below the nominal torque most of the time. In these circumstances the IM efficiency can be improved lowering the flux. For a given torque, this decreases iron looses and increases copper losses. With appropriate algorithms an optimum point for the flux can be achieved in order to minimize IM total power losses. Using an IM model, a neural network (NN) based approach is used to improve efficiency in a vector control of the induction motor drive. A complex loss model of the motor, including magnetic and thermal deviations of its parameters, is used to estimate losses. Based on this model, the neural network is trained to estimate the optimum rotor flux. Inputs to the NN are torque, speed and rotor resistance of the IM and the output is the rotor flux. Analysis, modeling and simulation results are presented to demonstrate the validity of the proposed method.