IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p7374-d672864.html
   My bibliography  Save this article

Formation of Characteristic Polynomials on the Basis of Fractional Powers j of Dynamic Systems and Stability Problems of Such Systems

Author

Listed:
  • Orest Lozynskyy

    (Institute of Power Engineering and Control Systems, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine)

  • Damian Mazur

    (Department of Electrical and Computer Engineering Fundamentals, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

  • Yaroslav Marushchak

    (Institute of Power Engineering and Control Systems, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine)

  • Bogdan Kwiatkowski

    (Department of Electrical and Computer Engineering Fundamentals, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

  • Andriy Lozynskyy

    (Institute of Power Engineering and Control Systems, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine)

  • Tadeusz Kwater

    (Institute of Technical Engineering, State University of Technology and Economics in Jaroslaw, Czarnieckiego Str. 16, 37-500 Jaroslaw, Poland)

  • Bohdan Kopchak

    (Institute of Power Engineering and Control Systems, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine)

  • Przemysław Hawro

    (Institute of Technical Engineering, State University of Technology and Economics in Jaroslaw, Czarnieckiego Str. 16, 37-500 Jaroslaw, Poland)

  • Lidiia Kasha

    (Institute of Power Engineering and Control Systems, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine)

  • Robert Pękala

    (Institute of Technical Engineering, State University of Technology and Economics in Jaroslaw, Czarnieckiego Str. 16, 37-500 Jaroslaw, Poland)

  • Robert Ziemba

    (Department of Electrical and Computer Engineering Fundamentals, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

  • Bogusław Twaróg

    (Department of Computer Engineering, Faculty of Mathematics and Natural Sciences, University of Rzeszow, Pigonia Str. 1, 35-959 Rzeszow, Poland)

Abstract

The article presents the creation of characteristic polynomials on the basis of fractional powers j of dynamic systems and problems related to the determination of the stability intervals of such systems.

Suggested Citation

  • Orest Lozynskyy & Damian Mazur & Yaroslav Marushchak & Bogdan Kwiatkowski & Andriy Lozynskyy & Tadeusz Kwater & Bohdan Kopchak & Przemysław Hawro & Lidiia Kasha & Robert Pękala & Robert Ziemba & Bogus, 2021. "Formation of Characteristic Polynomials on the Basis of Fractional Powers j of Dynamic Systems and Stability Problems of Such Systems," Energies, MDPI, vol. 14(21), pages 1-35, November.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7374-:d:672864
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/7374/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/21/7374/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alfredo Nespoli & Emanuele Ogliari & Sonia Leva & Alessandro Massi Pavan & Adel Mellit & Vanni Lughi & Alberto Dolara, 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques," Energies, MDPI, vol. 12(9), pages 1-15, April.
    2. Emanuele Ogliari & Francesco Grimaccia & Sonia Leva & Marco Mussetta, 2013. "Hybrid Predictive Models for Accurate Forecasting in PV Systems," Energies, MDPI, vol. 6(4), pages 1-12, April.
    3. Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
    4. Fei Mei & Yi Pan & Kedong Zhu & Jianyong Zheng, 2018. "A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    5. Tavazoei, Mohammad Saleh & Haeri, Mohammad, 2009. "A note on the stability of fractional order systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1566-1576.
    6. Alberto Dolara & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output," Energies, MDPI, vol. 8(2), pages 1-16, February.
    7. Margarita Rivero & Sergei V. Rogosin & José A. Tenreiro Machado & Juan J. Trujillo, 2013. "Stability of Fractional Order Systems," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-14, May.
    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. Yaroslav Marushchak & Damian Mazur & Bogdan Kwiatkowski & Bohdan Kopchak & Tadeusz Kwater & Maciej Koryl, 2022. "Approximation of Fractional Order PI λ D μ -Controller Transfer Function Using Chain Fractions," Energies, MDPI, vol. 15(13), pages 1-12, July.

    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. Grzegorz Dec & Grzegorz Drałus & Damian Mazur & Bogdan Kwiatkowski, 2021. "Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic," Energies, MDPI, vol. 14(6), pages 1-16, March.
    2. Athanasios I. Salamanis & Georgia Xanthopoulou & Napoleon Bezas & Christos Timplalexis & Angelina D. Bintoudi & Lampros Zyglakis & Apostolos C. Tsolakis & Dimosthenis Ioannidis & Dionysios Kehagias & , 2020. "Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting," Energies, MDPI, vol. 13(22), pages 1-31, November.
    3. Wei Li & Hui Ren & Ping Chen & Yanyang Wang & Hailong Qi, 2020. "Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review," Energies, MDPI, vol. 13(22), pages 1-25, November.
    4. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    5. Roberto S. Faranda & Hossein Hafezi & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "The Optimum PV Plant for a Given Solar DC/AC Converter," Energies, MDPI, vol. 8(6), pages 1-18, May.
    6. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    7. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    8. Happy Aprillia & Hong-Tzer Yang & Chao-Ming Huang, 2020. "Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm," Energies, MDPI, vol. 13(8), pages 1-20, April.
    9. Ogliari, Emanuele & Dolara, Alberto & Manzolini, Giampaolo & Leva, Sonia, 2017. "Physical and hybrid methods comparison for the day ahead PV output power forecast," Renewable Energy, Elsevier, vol. 113(C), pages 11-21.
    10. Alberto Dolara & Francesco Grimaccia & Giulia Magistrati & Gabriele Marchegiani, 2017. "Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming," Energies, MDPI, vol. 10(2), pages 1-20, February.
    11. Ümmühan Başaran Filik & Tansu Filik & Ömer Nezih Gerek, 2018. "A Hysteresis Model for Fixed and Sun Tracking Solar PV Power Generation Systems," Energies, MDPI, vol. 11(3), pages 1-15, March.
    12. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    13. Brester, Christina & Kallio-Myers, Viivi & Lindfors, Anders V. & Kolehmainen, Mikko & Niska, Harri, 2023. "Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations," Renewable Energy, Elsevier, vol. 207(C), pages 266-274.
    14. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    15. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
    16. Evgeny Solomin & Shanmuga Priya Selvanathan & Sudhakar Kumarasamy & Anton Kovalyov & Ramyashree Maddappa Srinivasa, 2021. "The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations," Energies, MDPI, vol. 14(9), pages 1-18, May.
    17. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
    18. Federica Cucchiella & Idiano D’Adamo & Paolo Rosa, 2015. "Industrial Photovoltaic Systems: An Economic Analysis in Non-Subsidized Electricity Markets," Energies, MDPI, vol. 8(11), pages 1-16, November.
    19. Javier Borquez & Hector Chavez & Karina A. Barbosa & Marcela Jamett & Rodrigo Acuna, 2020. "A Simple Distribution Energy Tariff under the Penetration of DG," Energies, MDPI, vol. 13(8), pages 1-17, April.
    20. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(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:14:y:2021:i:21:p:7374-:d:672864. 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.