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Neural Networks in the Diagnostics Process of Low-Power Solar Plant Devices

Author

Listed:
  • Stanisław Duer

    (Department of Energy, Faculty of Mechanical Engineering, Technical University of Koszalin, 15–17 Raclawicka St., 75-620 Koszalin, Poland)

  • Jan Valicek

    (Department of Electrical Engineering, Automation and Informatics, Faculty of Engineering, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 94976 Nitra, Slovakia)

  • Jacek Paś

    (Faculty of Electronic, Military University of Technology of Warsaw, 2 Urbanowicza St., 00-908 Warsaw, Poland)

  • Marek Stawowy

    (Department of Transport Telecommunication, Faculty of Transport, Warsaw University of Technology, Koszykowa St. 75, 00-662 Warsaw, Poland)

  • Dariusz Bernatowicz

    (Faculty of Electronics and Computer Science, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland)

  • Radosław Duer

    (Faculty of Electronics and Computer Science, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland)

  • Marcin Walczak

    (Faculty of Electronics and Computer Science, Technical University of Koszalin, 2 Sniadeckich St., 75-620 Koszalin, Poland)

Abstract

The article presents the problems of diagnostics of low-power solar power plants with the use of the three-valued (3VL) state assessment {2, 1, 0}. The 3VL diagnostics is developed on the basis of two-valued diagnostics (2VL), and it is elaborated on. In the (3VL) diagnostics, the range of changes in the values of the signals from the 2VL logic was accepted for the serviceability condition: state {1 2VL }. This range of signal value changes for logic (3VL) was divided into two signal value change sub-ranges, which were assigned two status values in the logic (3VL): {2 3VL }—serviceability condition and {1 3VL }—incomplete serviceability condition. The state of failure for both logics applied of the valence of states is interpreted equally for the same changes in the values of diagnostic signals, the possible changes of which exceed the ranges of their permissible changes. The DIAG 2 intelligent system based on an artificial neural network was used in diagnostic tests. For this purpose, the article presents the structure, algorithm and rules of inference used in the DIAG intelligent diagnostic system. The diagnostic method used in the DIAG 2 system utilizes the method known from the literature to compare diagnostic signal vectors with the reference signal vectors assigned. The result of this vector analysis is the metric developed of the difference vector. The problem of signal analysis and comparison is carried out in the input cells of the neural network. In the output cells of the neural network, in turn, the classification of the states of the object’s elements is realized. Depending on the condition of the individual elements that make up the object, the method is able to indicate whether the elements are in working order, out of order or require quick repair/replacement.

Suggested Citation

  • Stanisław Duer & Jan Valicek & Jacek Paś & Marek Stawowy & Dariusz Bernatowicz & Radosław Duer & Marcin Walczak, 2021. "Neural Networks in the Diagnostics Process of Low-Power Solar Plant Devices," Energies, MDPI, vol. 14(9), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2719-:d:551380
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    References listed on IDEAS

    as
    1. WenBo Xiao & Gina Nazario & HuaMing Wu & HuaMing Zhang & Feng Cheng, 2017. "A neural network based computational model to predict the output power of different types of photovoltaic cells," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-8, September.
    2. Marek Stawowy & Adam Rosiński & Jacek Paś & Tomasz Klimczak, 2021. "Method of Estimating Uncertainty as a Way to Evaluate Continuity Quality of Power Supply in Hospital Devices," Energies, MDPI, vol. 14(2), pages 1-16, January.
    3. Stanisław Duer, 2020. "Assessment of the Operation Process of Wind Power Plant’s Equipment with the Use of an Artificial Neural Network," Energies, MDPI, vol. 13(10), pages 1-17, May.
    4. Toshio Nakagawa, 2005. "Maintenance Theory of Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-221-8, December.
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    Citations

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    Cited by:

    1. Zheng Xu, 2022. "Three Technical Challenges Faced by Power Systems in Transition," Energies, MDPI, vol. 15(12), pages 1-21, June.
    2. Marcin Kaminski & Tomasz Tarczewski, 2023. "Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction," Energies, MDPI, vol. 16(11), pages 1-25, May.
    3. Jacek Paś, 2023. "Issues Related to Power Supply Reliability in Integrated Electronic Security Systems Operated in Buildings and Vast Areas," Energies, MDPI, vol. 16(8), pages 1-22, April.
    4. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Ghulam Moeen Uddin & Waqar Muhammad Ashraf & Karolina Grabowska & Anna Zylka & Anna Kulakowska & Wojciech Nowak, 2023. "Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives," Energies, MDPI, vol. 16(8), pages 1-12, April.

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