IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i7p1989-d529684.html
   My bibliography  Save this article

Regional Pole Placers of Power Systems under Random Failures/Repair Markov Jumps

Author

Listed:
  • Farag Ali El-Sheikhi

    (Department of Electrical and Electronics Engineering, Istanbul Esenyurt University, 34517 Esenyurt, Istanbul, Turkey)

  • Hisham M. Soliman

    (Department of Electrical & Computer Engineering, College of Engineering, Sultan Qaboos University, Al-Khoud, Muscat 123, Oman)

  • Razzaqul Ahshan

    (Department of Electrical & Computer Engineering, College of Engineering, Sultan Qaboos University, Al-Khoud, Muscat 123, Oman)

  • Eklas Hossain

    (Department of Electrical Engineering and Renewable Energy, Oregon Institute of Technology, Klamath Falls, OR 97601, USA)

Abstract

This paper deals with a discrete-time stochastic control model design for random failure prone and maintenance in a single machine infinite bus (SMIB) system. This model includes the practical values of failure/repair rate of transmission lines and transformers. The probability matrix is, therefore, calculated accordingly. The model considers two extreme modes of operations: the most reliable mode and the least reliable contingency case. This allows the control design which stochastically stabilizes the system under jump Markov disturbances. For adequate transient response, the proposed state feedback power system stabilizer (PSS) achieves a desired settling time and damping ratio by placing the closed-loop poles in a desired region. The control target should also be satisfied for load variations in either mode of operation. A sufficient condition is developed to achieve the control objectives via solving a set of linear matrix inequalities (LMI). Using simulation, the performance of the designed controller is tested for the system that prone to random failure/maintenance under various loading conditions. Simulation results reveal that the closed-loop poles reside within the desired region satisfying the required settling time and damping ratio under the aforementioned disturbances. The contributions of the paper are summarized as follows: (1) modeling of transition probability matrix under Markov Jumps using practical data, (2) designing a controller by compelling the closed poles into the desired region to achieve adequate dynamic performance under different load varying conditions.

Suggested Citation

  • Farag Ali El-Sheikhi & Hisham M. Soliman & Razzaqul Ahshan & Eklas Hossain, 2021. "Regional Pole Placers of Power Systems under Random Failures/Repair Markov Jumps," Energies, MDPI, vol. 14(7), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1989-:d:529684
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/7/1989/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/7/1989/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lisnianski, Anatoly & Elmakias, David & Laredo, David & Ben Haim, Hanoch, 2012. "A multi-state Markov model for a short-term reliability analysis of a power generating unit," Reliability Engineering and System Safety, Elsevier, vol. 98(1), pages 1-6.
    2. Wenping Hu & Jifeng Liang & Yitao Jin & Fuzhang Wu, 2018. "Model of Power System Stabilizer Adapting to Multi-Operating Conditions of Local Power Grid and Parameter Tuning," Sustainability, MDPI, vol. 10(6), pages 1-18, June.
    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. Mourad Kchaou & Houssem Jerbi & Dan Stefanoiu & Dumitru Popescu, 2022. "Quantized Fault-Tolerant Control for Descriptor Systems with Intermittent Actuator Faults, Randomly Occurring Sensor Non-Linearity, and Missing Data," Mathematics, MDPI, vol. 10(11), pages 1-20, May.
    2. Hisham M. Soliman & Farag A. El-Sheikhi & Ehab H. E. Bayoumi & Michele De Santis, 2022. "Ellipsoidal Design of Robust Stabilization for Markov Jump Power Systems under Normal and Contingency Conditions," Energies, MDPI, vol. 15(19), pages 1-16, October.
    3. Alexander Poznyak & Hussain Alazki & Hisham M. Soliman & Razzaqul Ahshan, 2022. "Ellipsoidal Design of Robust Stabilization of Power Systems Exposed to a Cycle of Lightning Surges Modeled by Continuous-Time Markov Jumps," Energies, MDPI, vol. 16(1), pages 1-16, December.

    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. Zhang, Cai Wen & Zhang, Tieling & Chen, Nan & Jin, Tongdan, 2013. "Reliability modeling and analysis for a novel design of modular converter system of wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 86-94.
    2. Song, Haifeng & Liu, Jieyu & Schnieder, Eckehard, 2017. "Validation, verification and evaluation of a Train to Train Distance Measurement System by means of Colored Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 10-23.
    3. FANG Yongfeng & TAO Wenliang & TEE Kong Fah, 2016. "Reliability Analysis of Multi-State Engine Units Utilizing Time-Domain Response Data," Journal of Systems Science and Information, De Gruyter, vol. 4(4), pages 354-364, August.
    4. Fazlollahtabar, Hamed & Saidi-Mehrabad, Mohammad & Balakrishnan, Jaydeep, 2015. "Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 34-44.
    5. Aliyu Sabo & Noor Izzri Abdul Wahab & Mohammad Lutfi Othman & Mai Zurwatul Ahlam Mohd Jaffar & Hakan Acikgoz & Hamzeh Beiranvand, 2020. "Application of Neuro-Fuzzy Controller to Replace SMIB and Interconnected Multi-Machine Power System Stabilizers," Sustainability, MDPI, vol. 12(22), pages 1-42, November.
    6. Postnikov, Ivan & Stennikov, Valery & Mednikova, Ekaterina & Penkovskii, Andrey, 2018. "Methodology for optimization of component reliability of heat supply systems," Applied Energy, Elsevier, vol. 227(C), pages 365-374.
    7. Jiang, Tao & Liu, Yu, 2017. "Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 3-15.
    8. Mi, Jinhua & Li, Yan-Feng & Peng, Weiwen & Huang, Hong-Zhong, 2018. "Reliability analysis of complex multi-state system with common cause failure based on evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 71-81.
    9. Zhou, Taotao & Zhang, Xiaoge & Droguett, Enrique Lopez & Mosleh, Ali, 2023. "A generic physics-informed neural network-based framework for reliability assessment of multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    10. Eldosouky, AbdelRahman & Saad, Walid & Mandayam, Narayan, 2021. "Resilient critical infrastructure: Bayesian network analysis and contract-Based optimization," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    11. Panagiotis K. Marhavilas & Michael G. Tegas & Georgios K. Koulinas & Dimitrios E. Koulouriotis, 2020. "A Joint Stochastic/Deterministic Process with Multi-Objective Decision Making Risk-Assessment Framework for Sustainable Constructions Engineering Projects—A Case Study," Sustainability, MDPI, vol. 12(10), pages 1-21, May.
    12. Chen, Qian & Zuo, Lili & Wu, Changchun & Li, Yun & Hua, Kaixun & Mehrtash, Mahdi & Cao, Yankai, 2022. "Optimization of compressor standby schemes for gas transmission pipeline systems based on gas delivery reliability," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    13. Zhao, Yunfei & Gao, Wei & Smidts, Carol, 2021. "Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    14. Song, Xiaogang & Zhai, Zhengjun & Liu, Yidong & Han, Jie, 2018. "A stochastic approach for the reliability evaluation of multi-state systems with dependent components," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 257-266.
    15. Kong, Yaonan & Ye, Zhisheng, 2017. "Goodness-of-fit tests in the multi-state Markov model," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 16-24.
    16. Tawfik Guesmi & Badr M. Alshammari & Yasser Almalaq & Ayoob Alateeq & Khalid Alqunun, 2021. "New Coordinated Tuning of SVC and PSSs in Multimachine Power System Using Coyote Optimization Algorithm," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    17. Ivan Postnikov & Ekaterina Samarkina & Andrey Penkovskii & Vladimir Kornev & Denis Sidorov, 2023. "Modeling Unpredictable Behavior of Energy Facilities to Ensure Reliable Operation in a Cyber-Physical System," Energies, MDPI, vol. 16(19), pages 1-11, October.
    18. Zhang, Aibo & Wu, Shengnan & Fan, Dongming & Xie, Min & Cai, Baoping & Liu, Yiliu, 2022. "Adaptive testing policy for multi-state systems with application to the degrading final elements in safety-instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    19. Chun-Ho Wang & Chao-Hui Huang & Deng-Guei You, 2022. "Condition-Based Multi-State-System Maintenance Models for Smart Grid System with Stochastic Power Supply and Demand," Sustainability, MDPI, vol. 14(13), pages 1-29, June.
    20. Dhople, S.V. & DeVille, L. & Domínguez-García, A.D., 2014. "A Stochastic Hybrid Systems framework for analysis of Markov reward models," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 158-170.

    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:14:y:2021:i:7:p:1989-:d:529684. 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.