Advanced sliding mode speed observer with compensated stray‐load and iron losses for a stand‐alone wind turbine‐driven induction generator

Abstract

Model‐based speed estimation schemes in sensorless field‐oriented control (FOC) of induction machines (IMs) are designed on the basis of a corresponding mathematical model. Thus, a reliable and robust sensorless controller requires using a more detailed IM model. The omission of nonlinear phenomena in real‐time application, such as magnetic saturation, iron losses, and stray‐load losses, certainly leads to inaccuracy in the estimated speed due to the existence of the mentioned phenomena in the IM. In this context, we address here the problem of neglecting the stray‐load and iron losses in a model‐based speed observer, synthesized by means of sliding mode theory, and we evaluate the impact of this omission on the execution of the utilized FOC and maximum power point tracking (MPPT) algorithm applied for a stand‐alone wind energy conversion system (WECS). The performance of the proposed sliding mode speed observer (SMSO), derived from the IM model including the above mentioned nonlinear phenomena, is experimentally evaluated over wide ranges of wind speed and IM magnetizing flux. It is also compared with the model reference adaptive system (MRAS) speed estimator, which is derived from the same IM model, as well as to the simplified SMSO, designed based on the IM model without stray‐load and iron losses. The obtained results show that the proposed SMSO has greater robustness with respect to abrupt variations of the rotor flux in comparison with the MRAS speed estimator, whereas it ensures significantly more stable operation and lower speed estimation error in comparison with the simplified SMSO.

Sensorless model predictive control based on I‐f integrated sliding mode observer for surface permanent magnet synchronous motor

“…During the mid-and high-speed stages, motor speed and rotor position information is obtained by means of counter electromotive force or various state observers, such as methods based on counter electromotive force 11 and its integration, 12 extended Kalman filters, 13,14 and sliding mode observers. 15 The back EMF method is derived strictly according to the electromagnetic theory, and the rotor position information can be obtained effectively due to the relationship between the stator voltage and current. However, when the motor operates at zero or low speed, the value of its back electromotive force is very small, making it difficult to accurately determine the rotor position.…”

Model Predictive Current Control of an Induction Motor Considering Iron Core Losses and Saturation

“…Its validity was experimentally verified by using four IMs of different efficiency classes and rotor cage material. Due to its accuracy, compactness, and simplicity, this model was later used in [28][29][30][31] for the control detuning analysis, estimation of the winding resistances and rotor speed, and loss minimization of field-oriented and sliding-mode controlled induction machines.…”