Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2018 / Article
Special Issue

Mathematical Theories and Applications for Nonlinear Control Systems

View this Special Issue

Research Article | Open Access

Volume 2018 |Article ID 9642123 | https://doi.org/10.1155/2018/9642123

Yuliang Sun, Shaomin Yan, Bin Cai, Yuqiang Wu, "Maximum Power Point Tracking of DFIG with DC-Based Converter System Using Coordinated Feedback Linearization Control", Mathematical Problems in Engineering, vol. 2018, Article ID 9642123, 12 pages, 2018. https://doi.org/10.1155/2018/9642123

Maximum Power Point Tracking of DFIG with DC-Based Converter System Using Coordinated Feedback Linearization Control

Guest Editor: Weihai Zhang
Received28 Aug 2018
Accepted22 Oct 2018
Published11 Nov 2018

Abstract

This paper presents a coordinated feedback linearization strategy (CFLS) for DC-based doubly-fed induction generator (DFIG) system to track the maximum power point. The stator and rotor of DFIG are connected to DC grid directly by two voltage source converters. Compared with a traditional DFIG system, the DC-based DFIG system has more system inputs and coupling, which increases the difficulty of vector control strategy. Accordingly, CFLS is proposed to make DFIG operate at the maximum power point (MPP), and two aspects are improved: first a single-loop control is adopted to make DFIG operate steady and accurate under coordinated the control of RSC and SSC. Second system control laws are obtained by the feedback linearization strategy that achieves DC-based DFIG system decoupling fully during the MPPT and system control. Simulations are carried out the comparison between CFLS and conventional vector control (VC), and it shows that the control performance of CFLS is superior.

1. Introduction

In recent years, the technology of wind power generation has developed rapidly, due to its freely available and renewable resource. Variable speed constant frequency doubly fed induction generator (DFIG) is often selected in wind power generation [1]. Compared with the traditional AC transmission, the DC transmission is more economical and stable for the long distance high voltage transmission [2]. The traditional DFIG stator windings are connected to an AC grid by transformer, and the rotor windings is connected to the AC grid by back to back converters. Therefore, the traditional DFIG is used into the DC grid and additional converter will be needed, which is bound to increase the cost [3].

Accordingly, the new converter system is adopted for DFIG in DC grid. A diode-based stator converter interfaces a DFIG with a DC grid, which has the advantages of low cost and simple structure [47]. But harmonic may reach from 5.97% to 11.66% [8]. An IGBT-based converters system consisting of a rotor side converter (RSC) and a stator side converter (SSC) connects rotor and stator with DC grid, respectively [913]. This structure has the advantages that it can reduce the current harmonic effectively and regulate the stator flux and current flexibly according to the needs of the system, not limited to AC grid.

However, the above improved structure results in that the state variables and the system outputs of DFIG with DC-based converters system are twice that of the traditional system, which greatly increases the complexity and coupling of the system and also increases the control difficulty of the system. Therefore, the traditional vector control (VC), which is based on approximate linear model, is difficult to achieve the global optimal control requirements of the new system [14]. Reference [9] adopts indirect air gap flux linkage orientation strategy to control DFIG in a DC grid, but air gap flux linkage is not suitable for measurement. Model predictive control for DFIG in a DC grid is adopted by [13], which requires high precision of system model.

Nowadays, Feedback linearization control (FLC) has been widely used in power electronics and power systems [1521]. It adopts a dual-loop control strategy of FLC and PI to control DFIG in an AC grid in [22]. The advantage of this method is simple to calculate, but the disadvantage is that could not achieve full system decoupling. Article [23] adopts rotor speed and stator reactive power of DFIG in an AC grid as state variables for FLC, which computationally complex and does not select direct system parameters to controller which affects control accuracy [24].

This paper designs a CFLS for DC-based DFIG to achieve maximum power point tracking (MPPT). This control strategy employs stator flux, rotor speed, and rotor current as system inputs, applies FLC to make up a single-loop control, and achieves complete decoupling of the system, which attains coordinated optimal control performance between the SSC and the RSC. This proposed control strategy has better control accuracy and tracking speed than the traditional VC strategy.

The paper is organized as follows. In Section 2, the model of DFIG based on DC grid is introduced. In Section 3, the maximum wind power is achieved by designing CFLS. In Section 4, simulation studies are evaluate the control performance of the proposed control strategy on a DC-based DFIG system. Finally there is conclusion of this paper.

2. Modeling of DFIG System

2.1. System Configuration

DFIG with its DC-based converters system in a DC grid is shown in Figure 1 [12]. The stator and the rotor of DFIG are connected to a DC grid through SSC and RSC. The stator voltage and frequency are completely unrestricted by the power grid. SSC can adjust stator voltage and frequency to control stator current and compensate stator reactive power. RSC can adjust rotor voltage to control rotor flux. The du/dt filter inductors are connected to stator and rotor, respectively, to prevent sharp voltage caused by converters and smooth currents.

2.2. DFIG Model Description

In this paper, DFIG model is adopted according to the motor direction in a d-q synchronous frame that can be expressed as [24, 25]where , , , , , and are the d-q components of stator voltage, current, and flux, respectively; , , , , , and are the d-q components of rotor voltage, current, and flux, respectively; , , , and are synchronous, slip, rotor, and mechanical angular frequency, respectively; , , and are the stator, rotor, and mutual inductance respectively; and are the rotor and stator resistance, respectively; is electromagnetic torque; is generator rotational inertia; and is the number of pairs of poles.

3. Control Strategy Description

3.1. MPPT Control Strategy

Usually the maximum kinetic power captured from the wind by a wind turbine is expressed as follows [26]:where is air density, is radius of wind turbine, is wind speed, and is maximum power coefficient.

To capture the maximum wind power, the power coefficient should maintain maximum at any wind speed within the operating range. Maximum is achieved by maintaining the tip speed ratio equal to optimal value and the pitch angle at a fixed value.The tip speed ratio indicates the state of the wind wheel under different wind speeds, asWhen is equal to , the optimal reference isIn this paper, the pitch angle is , the optimal tip speed ratio is , and the maximum power coefficient is . is shown in the Figure 2.

3.2. CFLS of DFIG

From (1)-(3), the DFIG model can be derived in the formwhere This is a multi-input multioutput (MIMO) system. An approach to obtain the inputCoutput linearization of the MIMO system is to differentiate the output of the system until the inputs appear, assuming that the corresponding relation degree is the smallest integer such that at least one of the inputs explicitly appears in [27]where denotes the th-order derivative of .

In (8), is 5 dimensional state phase quantity, is smooth vector field, and is smooth vector field. Each output has a , and by calculating that is 1, 1, 1 and 2. The system relation degree is ; therefore, the system has no nontrivial zero dynamics, which can be linearized by feedback linearization. According to (10) and of each output , the Lie Derivative of with along and the Lie Derivative of with along are obtained, and they are According to (8) and (11), the system can be described in the following matrix form:where There is , so the inverse matrix exists. A new input variable is defined for input-output feedback linearization, as . The conversion relation between the original input variable and the new input variable is as follows:where . Thus, the input-output mapping of (8) can be simplified as (14), which realizes the linear decoupling between the system output and input variables. According to the MTTP control strategy, and the output is stabilized at , where are the MPP reference value of the system. Through variable substitution the equilibrium point is moved to the origin, and the input variable can be redesigned into (14)In the controller design we can choice the parameters and in (15) and (16) to ensure the convergence and stability of the . Now we have derived that the partial states track the setting reference points.

Therefore, from the above analysis, the whole control scheme is shown in Figure 3.

3.3. Reference Points of Controller

The rotor flux and stator current are the control targets; the method determined in this paper is that, when a stator-oriented flux frame is adopted with its vector direction aligned with the q-axis, from (2), the stator flux reference value () and its d-q components are given bywhere is generator rated voltage amplitude, is synchronous speed. In this paper, pu, pu. Substituting (17) into (2), the rotor current are obtained:

3.4. Stability Analysis of State Variable

From the error equation (16), we obtain that the partial states are bounded are converge to the reference point. Next we will analyze the boundedness of the state . From (8), the equation about is extractedSubstituting (14) into (19), that can be derived asFrom (15), the states are convergent to , and further from (17), (18), and (7) know that their values are , so that dynamics (20) can be approximated asIn this paper, the rated wind speed of wind turbine is m/s, and the variable wind speed range is focus on 6-14 m/s. And the high frequency components in the wind speed measurement may cause undesired noise; therefore the measured wind speed is passed through low-pass filter to attenuate its effect [28]. It is assumed that the magnitude of its derivative is bounded as ; it becomesThe above can be known, the state variable is bounded, when .

4. Simulation Results

In this section, simulation results are carried out in MATLAB/SIMULINK, to verify the coordinated optimal control performance in a wide range operating conditions. There are two tests performed: case one shows the accuracy of CFLS compare with the VC under the ramp-change wind and case two is the system that operates at random-change wind to show the tracking speed of controller. The DFIG system parameters in the following simulation are listed in Appendix A.

4.1. Ramp-Change Wind

Figures 47 shows the performance of a DFIG in DC-grid controlled by the VC and CFLS under the ramp-change wind. The wind speed condition is depicted in Figure 4(a). The wind speed rises from 7 m/s to 14 m/s and then remains 14 m/s. It can be seen from Figure 4(b) that the optimal reference rotor speed is well tracked by the CFLS, while the response of the VC with larger overshoots is slower. Figures 4(c), 4(e), and 4(f) show that the tracking references performance of VC and CFLS and the tracking accuracy of CFLS are better than that of VC. The state variable is shown in Figure 4(d), as it keep stable in both VC and CFLS under ramp-change wind.

As shown in Figure 5, the CFLS can well capture the maximum power coefficient value, but the VC needs long time to catch . It means that the wind turbine maintains the maximum output power under the proposed control strategy.

Figure 6 shows the system output DC-voltage that connected to a stable DC-grid. It can be seen that the DC voltage remains stable under the control of CFLS and VC, when the wind ramp changes.

The rotor currents and voltages , and the stator currents and voltages can be seen that the DFIG is operated at the rated value while under the CFLS in Figure 7. and are sine wave with frequency of 60 Hz, and their amplitudes are 1.0 pu and 0.8 pu. is sine wave with amplitude of 0.85 pu. is the RSC output pulse wave, and its amplitude is 0.7 pu.

4.2. Random-Change Wind

The performance of a DC-based DFIG system controlled by the VC and CFLS under random-change wind is shown in Figures 811. The wind speed condition is depicted in Figure 8(a), which fluctuates between 6.8 m/s and 13.4 m/s. As shown in Figures 8(b), 8(c), 8(d), and 8(e), the tracking speed and accuracy of CFLS are obviously better than VC when the wind speed is time-varying. The state variable in the CFLS is shown in Figure 8(f), as it can stay mostly stable under the influence of random-change wind.

When the wind speed changes at any time, the power coefficient should be kept at the maximum value in order to extract the maximum wind power. As shown in Figure 9, the maximum power coefficient can be well captured by the CFLS. However, the change of the wind speed will affect the maximum power coefficient obtained by the VC strategy. This means that the CFLS maximum power tracking performance is much better than VC in a wide range operating conditions.

System output DC-voltage connected to a stable DC-grid in the random-change wind speed is shown in Figure 10; it remains stable under the control of CFLS and VC.

The output control signals of controllers are listed in Figure 11. Under the CFLS, because of the rotor speed tracking the optimal reference rotor speed that fluctuates follow the random-change wind, so the rotor side output signals are fluctuated near the working point. But the stator side output signals less fluctuant and operate steadily at working point. This shows that the proposed control strategy achieves the complete decoupling of DC-based DFIG system and that the stator side and rotor side have each performs its own functions, and coordinate the work.

5. Conclusion

From the simulation results and analysis, the following conclusions may be drawn out:

(1) A coordinated feedback linearization strategy is applied to achieve the complete decoupling and linearization for the DFIG with DC-based converter system.

(2) This paper gives up the traditional indirect dual-loop control method of power and current and selects stator flux (), rotor speed (), and rotor current () as state variables. A direct single-loop coordinated control method is implemented, which achieves the division cooperation between stator side and rotor side, and enhances the robustness of the system.

(3) The maximum power point tracking performance of the proposed control strategy is better than traditional VC. The CFLS kept power coefficient () at its maximum, to make DFIG operate at the maximum power point (MPP).

In further study, this experiment will be extended to verify the effectiveness of the system in case of failure.

Appendix

A.

A.1. DFIG Parameters [29]

, , , , ,

, , , .

A.2. Wind Turbine Parameters

, , , , .

A.3. VC Parameters [11]

RSC:Outer-loop: , ;Inner-loop: , .

SSC:Outer-loop:, ;Inner-loop: , .

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by National Natural Science Foundation of China (61473170).

References

  1. F. Akel, T. Ghennam, E. M. Berkouk, and M. Laour, “An improved sensorless decoupled power control scheme of grid connected variable speed wind turbine generator,” Energy Conversion and Management, vol. 78, pp. 584–594, 2014. View at: Publisher Site | Google Scholar
  2. X. She, A. Q. Huang, S. Lukic, and M. E. Baran, “On integration of solid-state transformer with zonal DC microgrid,” IEEE Transactions on Smart Grid, vol. 3, no. 2, pp. 975–985, 2012. View at: Publisher Site | Google Scholar
  3. W. Chen, A. Q. Huang, C. Li, G. Wang, and W. Gu, “Analysis and comparison of medium voltage high power DC/DC converters for offshore wind energy systems,” IEEE Transactions on Power Electronics, vol. 28, no. 4, pp. 2014–2023, 2013. View at: Publisher Site | Google Scholar
  4. M. F. Iacchetti, G. D. Marques, and R. Perini, “A scheme for the power control in a DFIG connected to a DC bus via a diode rectifier,” IEEE Transactions on Power Electronics, vol. 30, no. 3, pp. 1286–1296, 2015. View at: Publisher Site | Google Scholar
  5. G. D. Marques and M. F. Iacchetti, “Inner control method and frequency regulation of a DFIG connected to a dc link,” IEEE Transactions on Energy Conversion, vol. 29, no. 2, pp. 435–444, 2014. View at: Google Scholar
  6. M. F. Iacchetti, G. D. Marques, and R. Perini, “Torque Ripple Reduction in a DFIG-DC System by Resonant Current Controllers,” IEEE Transactions on Power Electronics, vol. 30, no. 8, pp. 4244–4254, 2015. View at: Publisher Site | Google Scholar
  7. G. D. Marques and M. F. Iacchetti, “A Self-Sensing Stator-Current-Based Control System of a DFIG Connected to a DC-Link,” IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6140–6150, 2015. View at: Publisher Site | Google Scholar
  8. R. Zhu, Z. Chen, and X. Wu, “Diode rectifier bridge-based structure for DFIG-based wind turbine,” in Proceedings of the IEEE Applied Power Electronics Conference and Exposition (APEC '15), pp. 1290–1295, Charlotte, NC, USA, March 2015. View at: Publisher Site | Google Scholar
  9. H. Nian and X. Yi, “Coordinated control strategy for doubly-fed induction generator with DC connection topology,” IET Renewable Power Generation, vol. 9, no. 7, pp. 747–756, 2015. View at: Publisher Site | Google Scholar
  10. S. Yan, A. Zhang, H. Zhang, J. Wang, and B. Cai, “An Optimum Design for a DC-Based DFIG System by Regulating Gearbox Ratio,” IEEE Transactions on Energy Conversion, vol. 33, no. 1, pp. 223–231, 2018. View at: Publisher Site | Google Scholar
  11. S. Yan, A. Zhang, H. Zhang, and J. Wang, “Control scheme for DFIG converter system based on DC-transmission,” IET Electric Power Applications, vol. 11, no. 8, pp. 1441–1448, 2017. View at: Publisher Site | Google Scholar
  12. S. Yan, A. Zhang, H. Zhang, J. Wang, and B. Cai, “Transient stability enhancement of DC-connected DFIG and its converter system using fault protective device,” Journal of Modern Power Systems and Clean Energy, vol. 5, no. 6, pp. 887–896, 2017. View at: Publisher Site | Google Scholar
  13. S. Yan, A. Zhang, H. Zhang, J. Wang, and B. Cai, “Optimized and coordinated model predictive control scheme for DFIGs with DC-based converter system,” Journal of Modern Power Systems and Clean Energy, vol. 5, no. 4, pp. 620–630, 2017. View at: Publisher Site | Google Scholar
  14. W. Qiao, L. Qu, and R. G. Harley, “Control of IPM synchronous generator for maximum wind power generation considering magnetic saturation,” IEEE Transactions on Industry Applications, vol. 45, no. 3, pp. 1095–1105, 2009. View at: Publisher Site | Google Scholar
  15. N. Reddy Naguru, A. Karthikeyan, C. Nagamani, and V. Sravan Kumar, “Comparative study of power control of DFIG using PI control and feedback linearization control,” in Proceedings of the International Conference on Advances in Power Conversion and Energy Technologies (APCET '12), pp. 1–6, August 2012. View at: Google Scholar
  16. L. Jiang and Q. Wu, “Nonlinear adaptive control via sliding-mode state and perturbation observer,” IEEE Proceedings - Control Theory and Applications, vol. 149, no. 4, pp. 269–277, 2002. View at: Publisher Site | Google Scholar
  17. Z. Zhang, Y. Wu, and J. Huang, “Differential-flatness-based finite-time anti-swing control of underactuated crane systems,” Nonlinear Dynamics, vol. 87, no. 3, pp. 1749–1761, 2017. View at: Publisher Site | Google Scholar
  18. L. Gao and D. Wang, “Input-to-state stability and integral input-to-state stability for impulsive switched systems with time-delay under asynchronous switching,” Nonlinear Analysis: Hybrid Systems, vol. 20, pp. 55–71, 2016. View at: Publisher Site | Google Scholar | MathSciNet
  19. X. Yan, X. Song, and X. Wang, “Global output-feedback stabilization for nonlinear time-delay systems with unknown control coefficients,” International Journal of Control, Automation, and Systems, vol. 16, no. 4, pp. 1550–1557, 2018. View at: Publisher Site | Google Scholar
  20. X. Xie, N. Duan, and C. Zhao, “A combined homogeneous domination and sign function approach to output-feedback stabilization of stochastic high-order nonlinear systems,” IEEE Transactions on Automatic Control, vol. 59, no. 5, pp. 1303–1309, 2014. View at: Publisher Site | Google Scholar | MathSciNet
  21. Y. Wu, J. Yu, and Y. Zhao, “Output feedback regulation control for a class of cascade nonlinear systems and its application to fan speed control,” Nonlinear Analysis: Real World Applications, vol. 13, no. 3, pp. 1278–1291, 2012. View at: Publisher Site | Google Scholar | MathSciNet
  22. X. Lin, K. S. Xiahou, Y. Liu, Y. B. Zhang, and Q. H. Wu, “Maximum power point tracking of DFIG-WT using feedback linearization control based current regulators,” in Proceedings of the IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia '16), pp. 718–723, Melbourne, Australia, November 2016. View at: Publisher Site | Google Scholar
  23. B. Yang, L. Jiang, L. Wang, W. Yao, and Q. Wu, “Nonlinear maximum power point tracking control and modal analysis of DFIG based wind turbine,” International Journal of Electrical Power & Energy Systems, vol. 74, pp. 429–436, 2016. View at: Publisher Site | Google Scholar
  24. R. Cardenas, R. Pena, S. Alepuz, and G. Asher, “Overview of control systems for the operation of DFIGs in wind energy applications,” IEEE Transactions on Industrial Electronics, vol. 60, no. 7, pp. 2776–2798, 2013. View at: Publisher Site | Google Scholar
  25. M. Tazil, V. Kumar, R. C. Bansal et al., “Three-phase doubly fed induction generators: an overview,” IET Electric Power Applications, vol. 4, no. 2, pp. 75–89, 2010. View at: Publisher Site | Google Scholar
  26. W. Qiao, “Dynamic modeling and control of doubly fed induction generators driven by wind turbines,” in Proceedings of the IEEE/PES Power Systems Conference and Exposition (PSCE '09), pp. 1–8, March 2009. View at: Google Scholar
  27. H. K. Khalil, Nonlinear Systems, 3rd edition, 2002.
  28. W. Qiao, W. Zhou, J. M. Aller, and R. G. Harley, “Wind speed estimation based sensorless output maximization control for a wind turbine driving a DFIG,” IEEE Transactions on Power Electronics, vol. 23, no. 3, pp. 1156–1169, 2008. View at: Publisher Site | Google Scholar
  29. N. W. Miller et al., “Dynamic modeling of GE 1.5 and 3.6 MW wind turbine-generators for stability simulations,” Power Engineering Society General Meeting IEEE, vol. 3, pp. 1977–1983, 2004. View at: Google Scholar

Copyright © 2018 Yuliang Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views654
Downloads421
Citations

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.