THE IDENTIFICATION OF INDUCTION MOTOR INTERNAL QUANTITIES

In the presented work a new identification method of difficult measured internal quantities of IM, such as components of magnetic flux vector and electromagnetic torque, is proposed. Commonly measurable quantities of IM like stator currents, stator voltage frequency and mechanical angular speed are used for identification to determine a feedback effect of the rotor flux vector on vector of stator currents of IM. Based on this feedback it is also possible to identify actual value of the rotor resistance, which can alter during IM operation. This has a significant impact on precision of identified quantities as well as on master control of IM. Stability of the identification structure is guaranteed by position of roots of characteristic equation of its linear transfer function. Results obtained from simulation measurements confirm quality, effectivity, feasibility, and robustness of the proposed identification method.

Cascade Control of the Ground Station Module of an Airborne Wind Energy System

An airborne wind energy system (AWES) can harvest stronger wind streams at higher altitudes which are not accessible to conventional wind turbines. The operation of AWES requires a controller for the tethered aircraft/kite module (KM), as well as a controller for the ground station module (GSM). The literature regarding the control of AWES mostly focuses on the trajectory tracking of the KM. However, an advanced control of the GSM is also key to the successful operation of an AWES. In this paper we propose a cascaded control strategy for the GSM of an AWES during the traction or power generation phase. The GSM comprises a winch and a three-phase induction machine (IM), which acts as a generator. In the outer control-loop, an integral sliding mode control (SMC) algorithm is designed to keep the winch velocity at the prescribed level. A detailed stability analysis is also presented for the existence of the SMC for the perturbed winch system. The rotor flux-based field oriented control (RFOC) of the IM constitutes the inner control-loop. Due to the sophisticated RFOC, the decoupled and instantaneous control of torque and rotor flux is made possible using decentralized proportional integral (PI) controllers. The unknown states required to design RFOC are estimated using a discrete time Kalman filter (DKF), which is based on the quasi-linear model of the IM. The designed GSM controller is integrated with an already developed KM, and the AWES is simulated using MATLAB and Simulink. The simulation study shows that the GSM control system exhibits appropriate performance even in the presence of the wind gusts, which account for the external disturbance.

An Online Parameter Estimation Using Current Injection with Intelligent Current-Loop Control for IPMSM Drives

An online parameter estimation methodology using the d-axis current injection, which can estimate the distorted voltage of the current-controlled voltage source inverter (CCVSI), the varying dq-axis inductances, and the rotor flux, is proposed in this study for interior permanent magnet synchronous motor (IPMSM) drives in the constant torque region. First, a d-axis current injection-based parameter estimation methodology considering the nonlinearity of a CCVSI is proposed. Then, during current injection, a simple linear model is developed to model the cross- and self-saturation of the dq-axis inductances. Since the d-axis unsaturated inductance is difficult to obtain by merely using the recursive least square (RLS) method, a novel tuning method for the d-axis unsaturated inductance is proposed by using the theory of the maximum torque per ampere (MTPA) with the combination of the RLS method. Moreover, to improve the bandwidth of the current loop, an intelligent proportional-integral-derivative (PID) neural network controller with improved online learning algorithm is adopted to replace the traditional PI controller. The estimated the dq-axis inductances and the rotor flux are adopted in the decoupled control of the current loops. Finally, the experimental results at various operating conditions of the IPMSM in the constant torque region are given.

A 24-sectors direct power control-feedforward neural network method of DFIG integrated to dual-rotor wind turbine

<p class="Abstract">In this work, a 24-sector direct power control (24-sector DPC) of a doubly-fed induction generator (DFIG) based dual-rotor wind turbine (DRWT) is studied. The major disadvantage of the 24-DPC control is the steady-state ripples in reactive and active powers. The use of 24 sectors of rotor flux, a feedforward neural network (FNN) algorithm is proposed to improve traditional 24-sector DPC performance and minimize significantly harmonic distortion (THD) of stator current and reactive/active power ripple. The proposed method is modeled and simulated by using MATLAB/Simulink software under different tests and compared with conventional 24-sector DPC.</p>

Speed Control with Indirect Field Orientation for Low Power Three-Phase Induction Machine with Squirrel Cage Rotor

Induction machines are widely used in the industry due to their many advantages compared to other industrial machines. This article presents the study and implementation of speed control applied to a Three-phase Induction Machine (MIT) of the squirrel cage type. The induction motor was modeled using the rotor flux in the synchronous reference to design Proportional-Integral (PI) type controllers for the current and velocity control loops. It is the objective of the article also to present in detail the development of converter hardware that comprises the stages of power, acquisition, and conditioning of engine signals. The system was simulated using computational tools and validated using a prototype designed, constructed, and commissioned.