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The Decoupled Active/Reactive Power Predictive Control of Quasi-Z-source Inverter for Distributed Generations

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  • Control Theory and Applications
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International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

For the quasi-Z-source inverter (qZSI), capacitor voltage stability control, high performance of the inductor current reference tracking and fast response of the active/reactive power are key issues. Thus, a decoupled active/reactive power model predictive control (MPC) of the qZSI for distributed generations (DGs) is proposed to fulfill these requirements without additional control loops. Firstly, the digital observer is constructed to remove the utilization of the front voltage sensor and reduce the number of hardware equipment. Moreover, based on the advance determination of the system operation mode and the simplified cost function, the calculation complexity of the proposed MPC algorithm is simplified. Further, the proposed improved MPC method with the digital observer is proved to achieve the high accuracy and the zero prediction error, of which stability is demonstrated through Lyapunov stability criteria. Eventually, the proposed controller is compared with conventional MPC and PI controller in detail and its effectiveness is verified by both simulation and experimental results from a grid-connected qZSI.

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References

  1. R. Wang, Q. Sun, P. Zhang, Y. Gui, D. Qin and P. Wang, “Reduced-order transfer function model of the droop-controlled inverter via Jordan continued-fraction expansion,” IEEE Trans. on Energy Conversion, vol. 35, no. 3, pp. 1585–1595, Sept. 2020.

    Article  Google Scholar 

  2. M. Chen, A. Zhang, and K. T. Chong, “A novel controller design for three-phase voltage source inverter,” International Journal of Control, Automation and Systems, vol. 16, no. 5, pp. 2136–2145, 2018.

    Article  Google Scholar 

  3. R. Wang, Q. Sun, Y. Gui, and D. Ma, “Exponential-function-based droop control for islanded microgrids,” Journal of Modern Power Systems and Clean Energy, vol. 7, no. 4, pp. 899–912, July 2019.

    Article  Google Scholar 

  4. R. Wang, Q. Sun, D. Ma, and Z. Liu, “The small-signal stability analysis of the droop-controlled converter in electromagnetic timescale,” IEEE Trans. on Sustainable Energy, vol. 10, no. 3, pp. 1459–1469, 2019.

    Article  Google Scholar 

  5. W. Liang, Y. Liu, B. Ge, and X. Wang, “DC-link voltage balance control strategy based on multidimensional modulation technique for quasi-Z-source cascaded multilevel inverter photovoltaic power system,” IEEE Trans. on Industrial Informatics, vol. 14, no. 11, pp. 4905–4915, 2018.

    Article  Google Scholar 

  6. X. Cao, J. Wang, and B. Zeng, “Distributed generation planning guidance through feasibility and profit analysis,” IEEE Trans. on Smart Grid, vol. 9, no. 50, pp. 5473–5475, 2018.

    Article  Google Scholar 

  7. S. Dong, Q. Zhang, and S. Cheng, “Analysis of critical inductance and capacitor voltage ripple for a bidirectional Z-source inverter,” IEEE Trans. on Power Electronics, vol. 30, no. 7, pp. 4009–4015, 2015.

    Article  Google Scholar 

  8. Y. Liu, B. Ge, H. Abu-Rub, F. Z. Peng, “Overview of space vector modulations for three-phase z-source/quasi-z-source inverters,” IEEE Trans. on Power Electronics, vol. 29, no. 4, pp. 2098–2108, 2014.

    Article  Google Scholar 

  9. D. Cao, S. Jiang, X. Yu, and F. Z. Peng, “Low-cost semi-Z-source inverter for single-phase photovoltaic systems,” IEEE Trans. on Power Electronics, vol. 26, no. 12, pp. 3514–3523, 2011.

    Article  Google Scholar 

  10. A. Ayad, P. Karamanakos, and R. Kennel, “Direct model predictive current control strategy of quasi-Z-source inverters,” IEEE Trans. on Power Electronics, vol. 32, no. 7, pp. 5786–5801, 2017.

    Article  Google Scholar 

  11. J. Anderson and F. Peng, “A class of quasi-Z-source inverters,” Proc. of IEEE Industry Applications Society Annual Meeting, pp. 1–7, 2008.

  12. N. Noroozi and M. R. Zolghadri, “Three-phase quasi-Z-source inverter with constant common-mode voltage for photovoltaic application,” IEEE Trans. on Industrial Electronics, vol. 65, no. 6, pp. 4790–4798, 2018.

    Article  Google Scholar 

  13. S. Zhou, L. Kang, J. Sun, G. Guo, B. Cheng, B. Cao, and Y. Tang, “A novel maximum power point tracking algorithms for stand-alone photovoltaic system,” International Journal of Control, Automation and Systems, vol. 8, no. 6, pp. 1364–1371, 2010.

    Article  Google Scholar 

  14. R. A. Guisso, A. M. S. S. Andrade, H. L. Hey, and M. L. da. S. Martins, “Grid-tied single source quasi-Z-source cascaded multilevel inverter for PV applications,” Electronics Letters, vol. 55, no. 6, pp. 342–343, 2019.

    Article  Google Scholar 

  15. O. M. Kwon, M. J. Park, J. H. Park, and S. M. Lee, “Enhancement on stability criteria for linear systems with interval time-varying delays,” International Journal of Control, Automation and Systems, vol. 14, no. 1, pp. 12–20, 2016.

    Article  Google Scholar 

  16. O. Ellabban, V. Mierlo, and P. Lataire, “A DSP-based dualloop peak DC-link voltage control strategy of the Z-source inverter,” IEEE Trans. on Power Electronics, vol. 27, no. 9, pp. 4088–4097, 2012.

    Article  Google Scholar 

  17. P. Iniyaval and R. Karthikeyan, “Fuzzy logic based quasi Z-source cascaded multilevel inverter with energy storage for photovoltaic power generation system,” Proc. of International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), pp. 1–5, 2008.

  18. H. Rostami and A. Khaburi, “Neural networks controlling for both the DC boost and AC output voltage of Z-source inverter,” Proc. of 1st Power Electronic & Drive Systems & Technologies Conference (PEDSTC), pp. 135–140, 2010.

  19. K. Shinde, G. Kadwane, P. Gawande, M. J. B. Reddy, and D. K. Mohanta, “Sliding mode control of single-phase grid-connected quasi-Z-source inverter,” IEEE Access, vol. 5, no. 10, pp. 10232–10240, 2017.

    Article  Google Scholar 

  20. J. Rodriguez, P. Kazmierkowski, R. Espinoza, J. R. Espinoza, P. Zanchetta, H. Abu-Rub, H. A. Young, and C. A. Rojas, “State of the art of finite control set model predictive control in power electronics,” IEEE Trans. on Industrial Informatics, vol. 9, no. 2, pp. 1003–1016, 2013.

    Article  Google Scholar 

  21. P. Karamanakos, A. Ayad, and R. Kennel, “A variable switching point predictive current control strategy for quasi-Z-source inverters,” IEEE Trans. on Industry Applications, vol. 54, no. 2, pp. 1469–1480, 2018.

    Article  Google Scholar 

  22. S. Vazquez, J. Rodriguez, M. Rivera, L. G. Franquelo, and M. Norambuena, “Model predictive control for power converters and drives: Advances and trends,” IEEE Trans. on Industrial Electronics, vol. 64, no. 2, pp. 935–947, 2017.

    Article  Google Scholar 

  23. R. Wang, Q. Sun, D. Ma, and X. Hu, “Line impedance cooperative stability region identification method for grid-tied inverters under weak grids,” IEEE Trans. on Smart Grid, vol. 11, no. 4, pp. 2856–2866, 2020.

    Article  Google Scholar 

  24. L. Tarisciotti, A. Formentini, A. Gaets, M. Degano, P. Zanchetta, R. Rabbeni, and M. Pucci, “Model predictive control for shunt active filters with fixed switching frequency,” IEEE Trans. on Industry Applications, vol. 53, no. 1, pp. 296–304, 2017.

    Article  Google Scholar 

  25. Y. Liu, H. Abu-Rub, Y. Xue, and F. Tao, “A discrete-time average model-based predictive control for a quasi-Z-source inverter,” IEEE Trans. on Industrial Electronics, vol. 65, no. 8, pp. 6044–6054, 2018.

    Article  Google Scholar 

  26. M. Mosa, O. Ellabban, H. Abu-Rub, and J. Rodriguez, “Model predictive control applied for quasi-Z-source inverter,” Proc. of Twenty-Eighth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 165–169, 2013.

  27. M. Mosa, R. Balog, and H. Abu-Rub, “High-performance predictive control of quasi-impedance source inverter,” IEEE Trans. on Power Electronics, vol. 32, no. 4, pp. 3251–3262, 2017.

    Article  Google Scholar 

  28. S. Jain, M. B. Shadmand, and R. S. Balog, “Decoupled active and reactive power predictive control for PV applications using a grid-tied quasi-Z-source inverter,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 6, no. 4, pp. 1769–1782, 2018.

    Article  Google Scholar 

  29. Public Utilities Commision — Recommendations for Advanced Functions for Distributed Energy Resources (DER) Systems’, http://www.energy.ca.gov/electricity_analysis/rule21/documents/phase3/SIWG_Phase_3_Advanced_DER_Functions_2015-03-12.pdf

  30. S. Bayhan, M. Trabelsi, H. Abu-Rub, and M. Malinowski, “Finite control set model predictive control for a quasi-Z-source four-leg inverter under unbalanced load condition,” IEEE Trans. on Industrial Electronics, vol. 64, no. 4, pp. 2560–2569, 2017.

    Article  Google Scholar 

  31. S. Sajadian and R. Ahmadi, “Model predictive-based maximum power point tracking for grid-tied photovoltaic applications using a Z-source inverter,” IEEE Trans. on Power Electronics, vol. 31, no. 11, pp. 7611–7620, 2016.

    Article  Google Scholar 

  32. P. Cortes, S. Kouro, L. Rocca, R. Vargas, J. Rodriguez, J. I. Leon, and S. Vazquez, “Guidelines for weighting factors design in model predictive control of power converters and drives,” Proc. of IEEE International Conference on Industrial Technology, pp. 1–7, 2009.

  33. C. Gajanayake, D. Vilathgamuwa, and P. Loh, “Development of a comprehensive model and a multiloop controller for Z-source inverter DG Systems,” IEEE Trans. on Industrial Electronics, vol. 54, no. 4, pp. 2352–2359, 2007.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, 11 Wenhua Road, Heping District, Shenyang, 110004, China

    Dazhong Ma, Rui Wang & Sen Lin

  2. State Grid Jingzhou Electric Power Supply Company, 15 Gongqing Road, Shashi District, Jingzhou, 434000, China

    Ke Cheng

  3. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, 66 Fanma Road, Xinmo, Gulou District, Nanjing, 210003, China

    Xiangpeng Xie

Authors
  1. Dazhong Ma
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  2. Ke Cheng
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Correspondence to Xiangpeng Xie.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Yonghao Gui under the direction of Editor Young IL Lee. This paper is supported by the National Key Research and Development Program of China(2018YFA0702200), National Natural Science Foundation of China (61773109, 62022044, 62073064), Jiangsu Natural Science Foundation for Distinguished Young Scholars under Grant (BK20190039), and Liaoning Revitalization Talents Program, China(XLYC1807009).

Dazhong Ma received his B.S. degree in automation in 2004 and a Ph.D. degree in control theory and control engineering in 2011, from Northeastern University, Shenyang, China, where he is currently an Associate Professor. His current research interests include fault diagnosis, fault-tolerant control, energy management systems, control and optimization of distributed generation systems, microgrids, and energy Internet.

Ke Cheng received his B.S. degree in electrical engineering and automation from Liaoning Technical University in 2017, and received his M.S. degree in power electronics and power drives from Northeastern University in 2020. His research interests include model predictive control, grid connection of distributed generation, and impedance source inverter.

Rui Wang received his B.S. degree in electrical engineering and automation in 2016 from Northeastern University, Shenyang, China, where he is currently working toward a Ph.D. degree in power electronics and power drive. His research interest focuses on collaborative optimization of distributed generation and its stability analysis of electromagnetic timescale in energy Internet.

Sen Lin received his B.S. degree in electrical engineering and automation from Northeastern University in 2015, and received his M.S. degree in electrical engineering from Northeastern University in 2020. His research interests include model predictive control, harmonic suppression, and Active front-end converter.

Xiangpeng Xie received his B.S. and Ph.D. degrees in engineering from Northeastern University, Shenyang, China, in 2004 and 2010, respectively. From 2010 to 2014, he was a Senior Engineer with the Metallurgical Corporation of China LTD. He is currently a Professor with the Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China. His research interests include fuzzy modeling and control synthesis, state estimations, optimization in process industries and intelligent optimization algorithms. Prof. Xie serves as Associate Editors of International Journal of Control, Automation, and Systems and International Journal of Fuzzy Systems.

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Ma, D., Cheng, K., Wang, R. et al. The Decoupled Active/Reactive Power Predictive Control of Quasi-Z-source Inverter for Distributed Generations. Int. J. Control Autom. Syst. 19, 810–822 (2021). https://doi.org/10.1007/s12555-019-0698-9

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  • DOI: https://doi.org/10.1007/s12555-019-0698-9

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