Sensorless Vector Control of Induction Generators for Variable-Speed Wind Turbines Using Micro-2407


A sensorless vector-control strategy for an induction generator in a grid-connected wind energy conversion system is presented. The sensorless control system is based on a model reference adaptive system (MRAS) to estimate the rotational speed. In order to tune the MRAS observer and compensate for the parameter variation and uncertainties, a separate estimation of the speed is obtained from the rotor slot harmonics using an algorithm for spectral analysis. This algorithm can track fast dynamic changes in the rotational speed, with high accuracy. Two back to back pulse width modulated (PWM) inverters are used to interface the induction generator with the grid. The front-end converter is also vector controlled. The dc link voltage is regulated using a PI fuzzy controller. The proposed sensorless control strategy has been experimentally verified on a 2.5-kW experimentally set up with an induction generator driven by a wind turbine emulator. The emulation of the wind turbine is performed using a novel strategy that allows the emulation of high-order wind turbine models, preserving all of the dynamic characteristics. The experimental results show the high level of performance obtained with the proposed sensorless vector-control method.

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R. Parida, B. Nanda and J. Mishra, "Sensorless Vector Control of Induction Generators for Variable-Speed Wind Turbines Using Micro-2407," Energy and Power Engineering, Vol. 4 No. 4, 2012, pp. 248-254. doi: 10.4236/epe.2012.44034.

1. Introduction

The advantages of cage induction machines are well known. These machines are relatively inexpensive, robust, and require low maintenance. When induction machines are operated using vector-control techniques, fast dynamic response and accurate torque control are obtained [1]. All of these characteristics are advantageous in variable-speed wind energy conversion systems (WECS). The control systems for the operation of indirect rotorflux-oriented (IRFO) vector-controlled induction machines for variable-speed wind energy applications have already been discussed in [1-3], cage induction machines are considered and a fuzzy control system is used to drive the WECS to the point of maximum energy capture for a given wind velocity. The induction machine is connected to the utility using back to-back converters. In [1-3], speed encoders are used to implement the vector control strategies. The use of this encoder implies additional wiring, extra cost, extra space, and careful mounting which detracts from the inherent robustness of cage induction machines [4-6].

In this paper, a sensor less control structure based on a direct rotor flux-oriented (DRFO) vector-control system, for variable speed wind energy applications, is discussed. A speed estimation, obtained from a model reference adaptive system (MRAS) [4], is used to control the electrical torque of the induction machine. A V/F control strategy is used in the low-speed region for starting and driving the WECS set into the speed operating range. In order to tune the MRAS system and compensate for the variation of the machine parameters, an estimation of the rotational speed is obtained from the rotor slot harmonics (RSH) [7,8]. The spectral analysis method used in this publication can track the rotational speed not only in steady state but also when the WECS is subjected to fast dynamic changes.

The system proposed in this paper is shown in Figure 1. An induction generator is driven from an emulated variable-speed wind turbine. An IPM, Micro-2407 based system is used to implement the DRFO algorithms, the V/F control strategy, the MRAS rotational speed observer, the spectral estimation algorithm, the control of the front-end converter, and the emulation of the wind turbine. The front-end converter supplies the electrical energy into the grid. This converter controls the dc link voltage of the back-to-back configuration using a fuzzy PI controller. The dq currents and voltages of the induction machine are referred to a reference frame aligned to the rotor flux. These currents take dc values in steady

Figure 1. Control system proposed.

state. The rotor flux is calculated from the machine voltages and currents (“Voltage Model” in Figure 1). The α-β components of the flux are used to calculate the electrical angle θe for the vector rotators [9-22].

The output is Δτr which is added to drive the estimation of the rotor time constant to the correct value. The tuning algorithm is switched off for fast speed changes to avoid the relatively large speed errors produced at the output of the RML-ATF filter (in practice, this does not take place very often because of the large inertia of WECS). Due to the reduced slip at light load, small errors in the estimated speed may produce a large variation in the estimated rotor time constant. To avoid this, the tuning algorithm is also switched off when the current is small.

2. Experimental Results

A 2.5-kW, 380-V, 50-Hz, four pole cage induction machine is utilized in the experimental prototype. The machine parameters are given in the Appendix. Two 5-kW commercial inverters with a 1-kHz switching frequency are used. The supply-side converter is connected to the grid via three 12-mH single-phase inductors. The dc link voltage is regulated to 550 V. A speed encoder of 10,000 ppr is used to calculate the system speed. This speed is not used in the control algorithms and it is only used for comparison purposes and the emulation of the wind turbine. In the machine side, two line currents and two line voltages are measured together with the dc link voltage and the current iG. The experimental fig is shown in Figure 2.

The generator is driven by a speed-controlled dc motor drive that emulates a wind turbine. The ωG (k + 1) speed is calculated in each sampling time and sent to the dc machine control system which regulates the shaft speed. A lookup table is used to store the Ct- characteristic in the micro-2407 board. Additional lookup tables are used to implement the abc to dq transformations, the calculation of the electrical angle θe, etc.

The control strategies proposed in this paper have been tested with several wind profiles (obtained from [23]) and similar results have been achieved. Figure 3 shows a typical wind profile with a 0.1-s sampling time for the wind velocity. The results in this section have been obtained using this profile. The performance of the RMLATF algorithm is the emulation of wind turbines with stiff shaft because in this case the shaft is not absorbing part of the speed fluctuations. For this reason, only the emulation of wind turbines with stiff shafts is considered in this paper. The torque current iq is controlled.

The response of the MRAS observer and RML-ATF is

Figure 2. Experimental system.

Figure 3. Wind profile in the experimental fig.

shown in Figure 4 for a wind step between 6 to 9 m/s when a wind turbine of j = 1.75 kg·m2 (JT + JG) is emulated. A wind step is not very realistic but it is the most drastic change from the control system point of view. In Figure 4, the rotor time constant is correctly estimated and the estimated speeds from the MRAS observer and RML-ATF algorithm are very good with a negligible tracking e.

Figure 5 shows the performance of the MRAS and RMLATF algorithm when a wind turbine of J = 3 kg·m2 is emulated in this test, the rotor time constant is underestimated by 50% and the tuning algorithm is off. The top graphic in Figure 5 shows the speed obtained from the encoder, MRAS and RML-ATF with a negligible error during the whole wind profile. The bottom graphic

Conflicts of Interest

The authors declare no conflicts of interest.


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