Speed sensorless direct vector control of squirrel caga induction machines

Abstract

Squirrel cage induction machines (SCIM) are the workhorses of the industry due to its ruggedness, low cost and free maintenance properties. In spite of its well-known usage, there are some constraints in their control. So, in 1969 and 1972 flux oriented vector control techniques are innovated in order to let SCIM control as a DC machine control. In the following years, SCIM high performance control has another prospect with the innovation of Direct Torque Control. Speed and flux data are needed in order to achieve these new challenging techniques. But, getting flux and speed feedbacks increase the cost or in same cases it is impossible to get the speed feedback because of physical difficulties. Flux feedback usage is not preferred due to requirement of the changes in the construction of the machine. When sensorless high performance SCIM control is considered, there are many constraints in the fifth order, recurrent, nonlinear and parameter varying SCIM dynamic model. Sensorless high performance control can be mentioned in a wide speed range, if model uncertainties are estimated in a high resolution.

When parameter uncertainties of an electrical machine are considered, there are two basic uncertainties: mechanical and electrical. When SCIM is considered, it is known the negative effects in variations in the rotor and stator resistances as well as motor fluxes. Rotor resistance is especially affected from skin effect with the variations in the frequency. Stator resistance is also affected from motor temperature. On the other hand, mechanical uncertainties as friction and load are another affects that are making sensorless high performance control difficult. There are many methods to achieve these problems. Model Reference Systems, Luenberger Observer, Sliding Mode Observer, Artificial Intelligence based methods and Extended Kalman Filter are very commonly used. EKF based observer is a very appropriate method for SCIM dynamic model due to its stochastic behavior in spite of its computational complexity. In this study, uncertainties of SCIM as load, rotor and stator resistances of SCIM are estimated by using an EKF based observer in order to get accurate speed and flux feedback. On the other hand, different than previous schemes, order of EKF is increased and an eight order EKF is designed in order to estimate rotor and stator resistances simultaneously. Besides, electrical and mechanical uncertainties are estimated experimentally by combining a digital signal processor and an open loop voltage source inverter.