A Low Voltage Ride Through Strategy of DFIG based on Explicit Model Predictive Control
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.where the flux linkages for stator and rotor are derived by,where 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 is determined by the grid [28]. By ignoring the stator resistance can be written as the sum of the positive sequence and the negative sequence in stator reference frame without considering the zero sequence component,where and 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 and , 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 and are caused by and , the current opposite to and can be used as the injected demagnetizing current,
The demagnetizing current produces a magnetic flux
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)
- et al.
A quantitative approach to wind farm diversification and reliability
Int J Electr Power Energy Syst
(2011) - et al.
A comprehensive review of low voltage ride through of doubly fed induction wind generators
Renew Sustain Energy Rev
(2016) - et al.
Analyzing effectiveness of LVRT techniques for DFIG wind turbine system and implementation of hybrid combination with control schemes
Renew Sustain Energy Rev
(2018) - et al.
A SCR crowbar commutated with power converter for DFIG-based wind turbines
Int J Electr Power Energy Syst
(2016) - et al.
Enhanced crowbarless FRT strategy for DFIG based wind turbines under three-phase voltage dip
Electr Power Syst Res
(2017) A new approach for low voltage ride through capability in DFIG based wind farm
Int J Electr Power Energy Syst
(2016)- et al.
Comparison of centralized, distributed and hierarchical model predictive control schemes for electromechanical oscillations damping in large-scale power systems
Int J Electr Power Energy Syst
(2014) - et al.
Distributed coordinated active and reactive power control of wind farms based on model predictive control
Int J Electr Power Energy Syst
(2019) - et al.
Continuous-time tube-based explicit model predictive control for collective pitching of wind turbines
Energy
(2017) - et al.
Mechanism analysis of DFIG-based wind turbine’s fault current during LVRT with equivalent inductances
IEEE J Emerg Sel Top Power Electron
(2019)
Adaptive fractional integral terminal sliding mode power control of UPFC in DFIG wind farm penetrated multimachine power system
Prot Control Mod Power Syst
Transient modeling and analysis of a DFIG based wind farm with supercapacitor energy storage
Int J Electr Power Energy Syst
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
LVRT capability enhancement of DFIG-based wind farms by using capacitive DC reactor-type fault current limiter
Int J Electr Power Energy Syst
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2022, International Journal of Electrical Power and Energy SystemsCitation Excerpt :Considering the symmetrical fault and asymmetrical fault scenarios in the power grid, the comparative simulations with the PI control, the stator current based control [40], the improved demagnetization control [23], and the proposed control method are designed to show the control effect of Q-RMPC. The main control without Q-learning is simulated to verify the function of the correction control, and the comparison between the Q-RMPC and the MPC in [33] is realized to prove the control characteristics of the proposed method under different operation parameters. The three-phase grounding short circuit fault occurs at the PCC at 3.5 s, and the duration of the fault is 0.2 s.
This work was supported by the National Key Research and Development Program of China (2018YFB0904004).