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A Review of State Estimation Techniques for Grid-Connected PMSG-Based Wind Turbine Systems

Author

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  • Ganesh Mayilsamy

    (School of IT Information and Control Engineering, Kunsan National University, 588 Daehak-ro, Gunsan-si 54150, Republic of Korea)

  • Kumarasamy Palanimuthu

    (School of IT Information and Control Engineering, Kunsan National University, 588 Daehak-ro, Gunsan-si 54150, Republic of Korea)

  • Raghul Venkateswaran

    (School of IT Information and Control Engineering, Kunsan National University, 588 Daehak-ro, Gunsan-si 54150, Republic of Korea)

  • Ruban Periyanayagam Antonysamy

    (School of IT Information and Control Engineering, Kunsan National University, 588 Daehak-ro, Gunsan-si 54150, Republic of Korea)

  • Seong Ryong Lee

    (School of IT Information and Control Engineering, Kunsan National University, 588 Daehak-ro, Gunsan-si 54150, Republic of Korea)

  • Dongran Song

    (School of Automation, Central South University, Changsha 410083, China)

  • Young Hoon Joo

    (School of IT Information and Control Engineering, Kunsan National University, 588 Daehak-ro, Gunsan-si 54150, Republic of Korea)

Abstract

The power system network grows yearly with a large number of nonlinear power generation systems. In this scenario, accurate modeling, control, and monitoring of interface systems and energy conversion systems are critical to the reliability and performance of the overall power system. In this trend, the permanent magnet synchronous generator (PMSG)-based wind turbine systems (WTS) equipped with a full-rated converter significantly contribute to the development of new and renewable energy generation. The various components and control systems involved in operating these systems introduce higher complexity, uncertainty, and highly nonlinear control challenges. To deal with this, state estimation remains an ideal and reliable procedure in the relevant control of the entire WTS. In essence, state estimation can be useful in control procedures, such as low-voltage ride-through operation, active power regulation, stator fault diagnosis, maximum power point tracking, and sensor faults, as it reduces the effects of noise and reveals all hidden variables. However, many advanced studies on state estimation of PMSG-based WTS deal with real-time information of operating variables through filters and observers, analysis, and summary of these strategies are still lacking. Therefore, this article aims to present a review of state-of-the-art estimation methods that facilitate advances in wind energy technology, recent power generation trends, and challenges in nonlinear modeling. This review article enables readers to understand the current trends in state estimation methods and related issues of designing control, filtering, and state observers. Finally, the conclusion of the review demonstrates the direction of future research.

Suggested Citation

  • Ganesh Mayilsamy & Kumarasamy Palanimuthu & Raghul Venkateswaran & Ruban Periyanayagam Antonysamy & Seong Ryong Lee & Dongran Song & Young Hoon Joo, 2023. "A Review of State Estimation Techniques for Grid-Connected PMSG-Based Wind Turbine Systems," Energies, MDPI, vol. 16(2), pages 1-27, January.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:634-:d:1025713
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    References listed on IDEAS

    as
    1. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    2. Zhang, Shuzhi & Zhang, Chen & Jiang, Shiyong & Zhang, Xiongwen, 2022. "A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation," Energy, Elsevier, vol. 246(C).
    3. Shu, Tong & Song, Dongran & Joo, Young Hoon, 2022. "Non-centralised coordinated optimisation for maximising offshore wind farm power via a sparse communication architecture," Applied Energy, Elsevier, vol. 324(C).
    4. Nasiri, M. & Milimonfared, J. & Fathi, S.H., 2015. "A review of low-voltage ride-through enhancement methods for permanent magnet synchronous generator based wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 399-415.
    5. Yin, Xiuxing & Jiang, Zhansi & Pan, Li, 2020. "Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems," Renewable Energy, Elsevier, vol. 145(C), pages 1149-1157.
    6. Song, Dongran & Yang, Jian & Cai, Zili & Dong, Mi & Su, Mei & Wang, Yinghua, 2017. "Wind estimation with a non-standard extended Kalman filter and its application on maximum power extraction for variable speed wind turbines," Applied Energy, Elsevier, vol. 190(C), pages 670-685.
    7. Waseem El Sayed & Mostafa Abd El Geliel & Ahmed Lotfy, 2020. "Fault Diagnosis of PMSG Stator Inter-Turn Fault Using Extended Kalman Filter and Unscented Kalman Filter," Energies, MDPI, vol. 13(11), pages 1-24, June.
    8. Kumarasamy Palanimuthu & Ganesh Mayilsamy & Ameerkhan Abdul Basheer & Seong-Ryong Lee & Dongran Song & Young Hoon Joo, 2022. "A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems," Energies, MDPI, vol. 15(21), pages 1-27, November.
    9. Ghaheri, Aghil & Afjei, Ebrahim & Torkaman, Hossein, 2022. "Design optimization of a novel linear transverse flux switching permanent magnet generator for direct drive wave energy conversion," Renewable Energy, Elsevier, vol. 198(C), pages 851-860.
    10. Song, Dongran & Li, Ziqun & Wang, Lei & Jin, Fangjun & Huang, Chaoneng & Xia, E. & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Joo, Young Hoon, 2022. "Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation," Applied Energy, Elsevier, vol. 312(C).
    11. Fantino, Roberto & Solsona, Jorge & Busada, Claudio, 2016. "Nonlinear observer-based control for PMSG wind turbine," Energy, Elsevier, vol. 113(C), pages 248-257.
    12. Gao, Richie & Gao, Zhiwei, 2016. "Pitch control for wind turbine systems using optimization, estimation and compensation," Renewable Energy, Elsevier, vol. 91(C), pages 501-515.
    13. Zheng, Changwen & Chen, Ziqiang & Huang, Deyang, 2020. "Fault diagnosis of voltage sensor and current sensor for lithium-ion battery pack using hybrid system modeling and unscented particle filter," Energy, Elsevier, vol. 191(C).
    14. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
    15. Miranda-Blanco, Blanca Nieves & Díaz-Dorado, Eloy & Carrillo, Camilo & Cidrás, J., 2014. "State estimation for wind farms including the wind turbine generator models," Renewable Energy, Elsevier, vol. 71(C), pages 453-465.
    16. Soares-Ramos, Emanuel P.P. & de Oliveira-Assis, Lais & Sarrias-Mena, Raúl & Fernández-Ramírez, Luis M., 2020. "Current status and future trends of offshore wind power in Europe," Energy, Elsevier, vol. 202(C).
    17. Carranza, O. & Figueres, E. & Garcerá, G. & Gonzalez, L.G., 2011. "Comparative study of speed estimators with highly noisy measurement signals for Wind Energy Generation Systems," Applied Energy, Elsevier, vol. 88(3), pages 805-813, March.
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