A Low Voltage Ride Through Strategy of DFIG based on Explicit Model Predictive Control

https://doi.org/10.1016/j.ijepes.2019.105783Get rights and content

Highlights

Abstract

In this paper, an improved demagnetization control, based on Explicit Model Predictive Control (E-MPC) is proposed to improve Low Voltage Ride Through (LVRT) capability of Doubly Fed Induction Generators (DFIGs). By injecting an additional rotor current component, the demagnetization control can efficiently eliminate the free and negative flux to avoid saturation of the rotor converter. The conventional demagnetization control is based on fixed scaling factors, whose control performance can’t be guaranteed for different fault conditions. The proposed E-MPC approach can fully explore the potential of rotor side converter. Besides, the control parameters of E-MPC are derived offline and very efficient for online control. In addition, the proposed E-MPC structure is simple and easy to be implemented. The mechanism of proposed E-MPC is presented in detail and verified in Matlab/Simulink. The results show that the proposed control scheme has a good performance and can improve the LVRT capability of DFIGs under various fault conditions, especially unbalanced faults.

Introduction

WITH the increasing penetration of wind power, the integration of wind farms to the grid and their dynamic behavior under grid faults have become an important issue in recent years [1], [2]. According to the grid codes, to keep the system stability, the wind turbine should always be connected to the grid during and after faults, especially Low Voltage Ride Through (LVRT).

The Doubly Fed Induction Generator (DFIG) is widely used in wind power generation systems due to its high reliability and low cost [3], [4]. However, it is excessive sensitivity to grid disturbances since the stator is directly connected to the grid. The sudden voltage fault may cause problems of DFIG such as rotor side over-current and over-voltage at DC bus [5]. The situation gets even worse for the unbalanced cases, due to the rotor voltage induced by free and negative sequence flux, which will cause activation of protection devices and even disconnection of DFIG from the grid [6]. It is of highly importance to study the improvement of LVRT capability of DFIG [7].

To protect the DFIG against voltage dips, the primary solution is using hardware protection devices, such as crowbar and chopper [8]. However, during the activation of crowbar, the DFIG absorbs a large amount of reactive power from the power grid, which can’t help and even deteriorate the voltage recovery [9]. Crowbar should be timely removed to support reactive power to the grid [10]. Dynamic voltage restorer is installed between the stator and the grid to compensate the drop in the grid voltage by providing reactive power during the fault [11], [12]. However, the application of hardware in these methods increases the system cost and control complexity [13].

Many studies have focused on the improvement of converter control methods to reduce the usage or shorten the activation time of hardware protection devices. Part of the methods improve the dynamic performance of DFIG by changing the control parameters. Obviously, the rational design of the control parameters is crucial for the control of the DFIG [14]. Appropriate tuning the proportional integral (PI) controllers is also shown to affect the DFIG LVRT [15].

In addition, it is also a mainstream LVRT solution to change the control structure of the rotor side controller. Ref. [16] proposed the demagnetization control to eliminate the induced voltage by injecting a rotor current opposite to the free and negative flux. In [17], the measurement of negative sequence and free component flux was improved to avoid the delay caused by low pass filter. Ref. [18] proposed a scaled current tracking control for rotor-side converter to enhance its LVRT capacity without flux observation. In [19], virtual resistance is introduced to enlarge the control range, but still can’t adjust the demagnetizing current flexibly. An improved demagnetization control, immune to system parameter variation, is proposed to shorten the dynamic process [20], but only applied to balanced faults. In [21], the rotor flux linkage is controlled to track a reduced fraction of the changing stator flux linkage. However, the reactive power required by grid code is not involved. The cooperation of the crowbar and the demagnetization are studied in the article [22]. However, the control parameters of the aforementioned control strategies are fixed, which can’t be adjusted flexibly for different grid conditions. Thus, their control performance can’t be guaranteed.

To overcome this problem, a demagnetization control based on Explicit Model Predictive Control (E-MPC) is proposed. Model Predictive Control (MPC) can predict future output over a specific prediction horizon based on the system model [23], [24]. The main advantage of using MPC is computing the optimal control action while considering system constraints [25]. However, during LVRT, the fault time is very short and MPC is difficult to handle the fast online calculation [26]. In this study, E-MPC is applied to calculate the optimization problem offline and suitable for online calculation [27].

The main contributions of this paper are twofold. Firstly, the demagnetizing current can be optimally derived for various fault conditions to fully explore the capacity of the rotor side converter. Secondly, a simple E-MPC control structure without additional measurement requirements is designed and described in detail.

This paper is organized as follows. Section 2 describes the dynamic model of DFIG. Section 3 analyzes the dynamical performance of DFIG under unbalanced conditions. In Section 4, the working principle of conventional demagnetization control is elaborated. The proposed demagnetization control based on E-MPC is designed and presented in detail in Section 5. To verify the proposed strategy, the simulation are carried out in Section 6. The conclusion is drawn in Section 7.

Section snippets

Dynamical model of DFIG

The basic structure of DFIG with crowbar and chopper is shown in Fig. 1.

The mathematical modelling of DFIG under synchronous rotating reference frame (dq) can be expressed as follows. The equivalent circuits are shown in Fig. 2.usd=Rsisd+dψsddt-ωsψsqusq=Rsisq+dψsqdt+ωsψsdurd=Rrird+dψrddt-ωrψrqurq=Rrirq+dψrqdt+ωrψrd,where the flux linkages for stator and rotor are derived by,ψsd=Lsisd+Lmirdψsq=Lsisq+Lmirqψrd=Lmisd+Lrirdψrq=Lmisq+Lrirq,where usd,usq,isd,isq,ψsd,ψsq are stator voltage, current and

Dynamical performance of DFIG under unbalanced conditions

Due to the direct connection to the grid, it can be considered that the stator voltage Vss is determined by the grid [28]. By ignoring the stator resistance Rs,Vss can be written as the sum of the positive sequence and the negative sequence in stator reference frame without considering the zero sequence component,Vss=V1ejωst+V2e-jωst,where V1 and V2 denote the amplitudes of the positive and negative sequence of the stator voltage. To be noticed, the superscript “s” in variables represents

Conventional demagnetization control

To counteract the induced voltage vr2r and vrfr, the demagnetization control which injecting a rotor current opposite to the free and negative flux is employed. This method avoids rotor converter saturation and reduces DC bus over-voltage and rotor over-current.

Since vr2r and vrfr are caused by ψs2s and ψsfs , the current irde opposite to ψs2s and ψsfs can be used as the injected demagnetizing current,irder=-kψsfr-kψs2r.

The demagnetizing current irde produces a magnetic flux ψdef

Demagnetization control based on E-MPC

The control structure of proposed E-MPC based demagnetization control is shown in the Fig. 8. To be noticed, the following variables are all in the rotor reference frame. Therefore the superscript “r” is ignored for simplicity.

Cases study

A 1.5-MW DFIG-based system is built in MATLAB/ Simulink to verify the proposed control strategy. The DFIG parameters are shown in Table 1. To show the control performance, simulation is carried out under both balanced and unbalanced faults with three controllers: without demagnetization (labelled by “Control 1”), conventional demagnetization control (labelled by “Control 2”) and E-MPC.

Conclusion

In this paper, the demagnetization control based on E-MPC is proposed to enhance the LVRT capability of DFIGs. The proposed E-MPC can flexibly adjust the demagnetizing current under different fault conditions and fully explore the capability of rotor side converter. Based on the offline derived calculation parameters, the designed E-MPC can speed up solving the optimization problem significantly, which is very suitable for online control application. The control structure of proposed E-MPC is

Declaration of Competing Interest

None.

References (29)

  • P. Dash et al.

    Adaptive fractional integral terminal sliding mode power control of UPFC in DFIG wind farm penetrated multimachine power system

    Prot Control Mod Power Syst

    (2018)
  • M. Kenan et al.

    Transient modeling and analysis of a DFIG based wind farm with supercapacitor energy storage

    Int J Electr Power Energy Syst

    (2016)
  • O. Noureldeen et al.

    A novel controllable crowbar based on fault type protection technique for DFIG wind energy conversion system using adaptive neuro-fuzzy inference system

    Prot Control Mod Power Syst

    (2018)
  • K. Shahbabaei et al.

    LVRT capability enhancement of DFIG-based wind farms by using capacitive DC reactor-type fault current limiter

    Int J Electr Power Energy Syst

    (2018)
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    This work was supported by the National Key Research and Development Program of China (2018YFB0904004).

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