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

Stanisław Duer, Jan Valicek, Jacek Paś, Marek Stawowy, Dariusz Bernatowicz, Radosław Duer and Marcin Walczak
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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

Energies, 2021, vol. 14, issue 9, 1-18

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.

Keywords: neural networks; intelligent systems; servicing process; diagnostic process; expert system; knowledge base; low-power solar plant devices; diagnostic information (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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