IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i5p1251-d329906.html
   My bibliography  Save this article

Disturbance Observer-Based Prescribed Performance Fault-Tolerant Control for a Multi-Area Interconnected Power System with a Hybrid Energy Storage System

Author

Listed:
  • Dong Yu

    (School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)

  • Weiming Zhang

    (School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)

  • Jianlin Li

    (School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China)

  • Weilin Yang

    (School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)

  • Dezhi Xu

    (School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)

Abstract

The goal of this study is to develop a disturbance observer-based prescribed performance fault-tolerant controller (PPFTC) for a multi-area interconnected power system (MIPS) with a hybrid energy storage system (HESS). The mathematical model of the MIPS with HESS is introduced first. Then the load disturbance estimation is obtained using a disturbance observer (DO) approach. By introducing two additional functions, the tracking error is bounded to achieve the desired response. The PPFTC is further developed based on the DO ensuring that the area control error (ACE) of the MIPS asymptotically converges to zero. Finally, the effectiveness of the given PPFTC and DO are inspected through the use of the Lyapunov theory and simulated results.

Suggested Citation

  • Dong Yu & Weiming Zhang & Jianlin Li & Weilin Yang & Dezhi Xu, 2020. "Disturbance Observer-Based Prescribed Performance Fault-Tolerant Control for a Multi-Area Interconnected Power System with a Hybrid Energy Storage System," Energies, MDPI, vol. 13(5), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1251-:d:329906
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/5/1251/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/5/1251/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fathima, A. Hina & Palanisamy, K., 2015. "Optimization in microgrids with hybrid energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 431-446.
    2. Weilin Yang & Dong Yu & Dezhi Xu & Yiwei Zhang, 2019. "Observer-Based Sliding Mode FTC for Multi-Area Interconnected Power Systems against Hybrid Energy Storage Faults," Energies, MDPI, vol. 12(14), pages 1-15, July.
    3. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    4. Zhang, Shuo & Xiong, Rui & Sun, Fengchun, 2017. "Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system," Applied Energy, Elsevier, vol. 185(P2), pages 1654-1662.
    5. Yan-Hui Jing & Guang-Hong Yang, 2017. "Adaptive quantised fault-tolerant tracking control of uncertain nonlinear systems with unknown control direction and the prescribed accuracy," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(13), pages 2826-2837, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nahid-Al Masood & Md. Nahid Haque Shazon & Hasin Mussayab Ahmed & Shohana Rahman Deeba, 2020. "Mitigation of Over-Frequency through Optimal Allocation of BESS in a Low-Inertia Power System," Energies, MDPI, vol. 13(17), pages 1-23, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    2. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    3. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    4. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    6. Hong Zhang & Zhuang Xing & Jiajian Song & Qiangqiang Yang, 2018. "Development and Test Application of an Auxiliary Power-Integrated System," Energies, MDPI, vol. 11(1), pages 1-18, January.
    7. Zhuang, Weichao & Ye, Jianwei & Song, Ziyou & Yin, Guodong & Li, Guangmin, 2020. "Comparison of semi-active hybrid battery system configurations for electric taxis application," Applied Energy, Elsevier, vol. 259(C).
    8. Ashleigh Townsend & Rupert Gouws, 2022. "A Comparative Review of Lead-Acid, Lithium-Ion and Ultra-Capacitor Technologies and Their Degradation Mechanisms," Energies, MDPI, vol. 15(13), pages 1-29, July.
    9. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Tang, Xiaolin & Lang, Kun & Xin, Zongke & Brighton, James, 2019. "Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge," Energy, Elsevier, vol. 173(C), pages 667-678.
    10. Ashleigh Townsend & Rupert Gouws, 2023. "A Comparative Review of Capacity Measurement in Energy Storage Devices," Energies, MDPI, vol. 16(10), pages 1-26, May.
    11. Shabbir, Wassif & Evangelou, Simos A., 2019. "Threshold-changing control strategy for series hybrid electric vehicles," Applied Energy, Elsevier, vol. 235(C), pages 761-775.
    12. Kim, Sunwoo & Choi, Yechan & Park, Joungho & Adams, Derrick & Heo, Seongmin & Lee, Jay H., 2024. "Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    13. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    14. Yu, Zhefeng & Zhao, Feng & Ding, Shihong & Chen, Xiangyong, 2022. "Adaptive pre-assigned finite-time control of uncertain nonlinear systems with unknown control gains," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    15. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    16. Yang, Ningkang & Han, Lijin & Xiang, Changle & Liu, Hui & Li, Xunmin, 2021. "An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle," Energy, Elsevier, vol. 236(C).
    17. Xiaohan Fang & Jinkuan Wang & Guanru Song & Yinghua Han & Qiang Zhao & Zhiao Cao, 2019. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling," Energies, MDPI, vol. 13(1), pages 1-26, December.
    18. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    19. Zhou, P. & Jin, R.Y. & Fan, L.W., 2016. "Reliability and economic evaluation of power system with renewables: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 537-547.
    20. Jaszczur, Marek & Hassan, Qusay & Palej, Patryk & Abdulateef, Jasim, 2020. "Multi-Objective optimisation of a micro-grid hybrid power system for household application," Energy, Elsevier, vol. 202(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1251-:d:329906. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.