Keywords
electrical equipment
monitoring and diagnostics sestem
How to Cite
Abstract
Features of the construction and practical application of multi-level information and measurement systems for monitoring and diagnostics of electrical equipment are considered. The results of the construction and research of mathematical models of some diagnostic signals (acoustic emission, vibrations) accompanying the operation of electrical equipment units (including powerful generators) are given. Improved mathematical models of vibrational information-diagnostic signals are proposed, taking into account both the properties of diagnostic objects and the modes in which such equipment works. According to the results of the analysis of mathematical models of the considered information signals, a number of diagnostic signs are theoretically justified, which allows monitoring and establishing the technical condition of individual generator nodes. As illustrative examples, the use of acoustoemission and vibration diagnostic signals for the formation of training sets, which are part of a multi-level information and measurement system for monitoring and diagnostics of electrical equipment, is considered. The information base obtained at the experimental test stands of the IED of the National Academy of Sciences of Ukraine was used to form such educational groups. It is pointed out the need to take into account the influence of temperature and humidity on the assessment of diagnostic signs during monitoring and diagnostics of electrical equipment. Bible 26, fig. 8.
References
Myslovych M.V., Sysak R.M. About some features of the construction of intelligent multi-level systems of technical diagnostics of electric power facilities. Tekhnichna elektrodynamika. 2015. No. 1. Pp. 78–85. (Ukr)
Sysak R.M. Optimization of algorithmic software of autonomous measuring modules of distributed diagnostic systems. Tekhnichna elektrodynamika. 2018. No 3. Pp. 90–96. (Ukr) DOI: https://doi.org/10.15407/techned2018.03.090
Myslovych M.V. Models of forms of representation of learning populations for multi-level systems of diagnosing electrical equipment nodes. Tekhnichna elektrodynamika. 2021. No 3. Pp. 65–73. (Ukr) DOI: https://doi.org/10.15407/techned2021.03.065
Stogniy B.S., Kirylenko O.V., Butkevich O.F., Sopel M.F. Information provision of power system management tasks. Energy: economy, technologies, ecology. 2012. No 1. Pp. 13–22. (Ukr)
Stogniy B.S., Kirylenko O.V., Prakhovnyk A.V., Denisyuk S.P. Evolution of intelligent electrical networks and their prospects in Ukraine. Tekhnichna elektrodynamika. 2012. No 5. Pp. 52–67. (Ukr)
Ilyin V.V. Introduction to Smart Grid. AVOK. 2012. No 7. Pp. 76–86. (Rus)
S. García, J. Luengo, F. Herrera Data Preprocessing in Data Mining. Part of the Intelligent Systems Reference Library book series (ISRL, Vol. 72). Springer International Publishing Switzerland, 2015. 327 р. DOI: https://doi.org/10.1007/978-3-319-10247-4
V.C. Gungor, Bin Lu, G.P. Hancke Opportunities and Challenges of Wireless Sensor Networks in Smart Grid. IEEE Transactions on industrial electronics. 2010. Vol. 57. No 10. Рp. 3557–3564. DOI: https://doi.org/10.1109/TIE.2009.2039455
A. Secic et al.: Vibro-Acoustic Methods in the Condition Assessment of Power Transformers Received May 2019, accepted June 10, 2019, date of publication June 19, 2019, date of current version July 12, 2019. Рp. 83915–83931.
DOI: https://doi.org/10.1109/ACCESS.2019.2923809
Gui-Ping Zhou, Huan-Huan Luo, Wei-Chun Ge, Yi-Ling Ma, Shi Qiu, Li-Na Fu Design and application of condition monitoring for power transmission and transformation equipment based on smart grid dispatching control system The Journal of Engineering J. Eng., 2019, Vol. 2019 Iss. 16, Pp. 2817–2821. The 14th IET International Conference on AC and DC Power Transmission (ACDC 2018). Рp. 2817–2821. DOI: https://doi.org/10.1049/joe.2018.8456
Yiging Zhou, Jian Wang, Zeru Wang. Multisensor-Based Heavy Machine Faulty Identification Using Sparse Autoencoder-Based Feature Fusion and Deep Belief Network-Based Ensemble Learning. Hindawi: Journal of Sensor. Volume, 2022. Article ID 5796505. P. 1–26. DOI: https://doi.org/10.1155/2022/5796505
Myslovych M., Sysak R. Design peculiarities of multi-level systems for technical diagnostics of electrical machines. Computational Problems of Electrical Engineering. 2014. Vol. 4. No. 1. Pp. 47–50.
Hertsyk S.M. A computerized system of diagnostics of electrical equipment nodes, taking into account the modes of its operation.: Abstract dissertation for obtaining the scientific degree of Candidate of Technical Sci-ences in the specialty 05.13.05 computer systems and components. Kyiv: Institute of Electrodynamics of the Na-tional Academy of Sciences of Ukraine. 2019. 20 p. (Ukr).
Kensytsky O.G. Operational reliability of energy-generating equipment of power units of nuclear power plants of Ukraine. Pratsi Instytutu Elektrodynamiky NAN Ukrainy. 2021. Issue 58. Pp. 100–106. (Ukr) DOI: https://doi.org/10.15407/publishing2021.58.100
Kensytsky O.G., Klyuchnikov A.G., Fedorenko H.M. Safety, reliability and efficiency of operation of electrical and electrical equipment of NPP units. Institute of NPP Safety Problems, 2009. 240 p. (Rus)
Ulytko O.V. Acoustic-emission information and measurement system for statistical diagnostics of electrical equipment units.: Abstract of the dissertation for obtaining the scientific degree of Candidate of Technical Sciences in the specialty 05.13.05 computer systems and components. Kyiv: Institute of Electrodynamics of the National Academy of Sciences of Ukraine. 2011. 22 p. (Ukr)
Marchenko B.G. The method of stochastic integrals is presented and its applications in radio engineering. Kyiv: Nauk. dumka, 1973. 192 p. (Rus)
Marchenko B.G., Shcherbak L.N. Linear random processes and their applications. Kyiv: Nauk. dumka, 1975. 143 p. (Rus)
Andreykiv A.E., Lysak N.V. Method of acoustic emission in researches of destruction processes. Kyiv: Nauk. dumka, 1989. 176 p. (Rus)
Babak V.P., Filonenko S.F., Kalita V.M. Models of formation of acoustic emission signals during deformation and destruction of materials. Technological systems. 2002. No. 1(12). Pp. 26–34. (Rus)
Ulytko A.V. Peculiarities of constructing decisive rules for determining the degree of load in electrical equipment nodes in information and measurement systems of diagnostics. Pratsi Instytutu Elektrodynamiky NAN Ukrainy. 2007. No. 2 (17). Pp. 81–86. (Rus)
Babak S.V., Myslovich M.V., Sysak R.M. Statistical diagnostics of electrical equipment. Kyiv: Institute of Electrodynamics of the National Academy of Sciences of Ukraine, 2015. 456 p. (Rus)
Babak V.P., Babak S.V., Myslovych M.V., Zvaritch V.N., Zaporozhets A.O. Diagnostic Systems For Energy Equipments, 2020 Springer Nature, Switzerland AG. Studies in Systems, Decision and Control book series (SSDC, volume 281), 133 p.
DOI: https://doi.org/10.1007/978-3-030-44443-3
Zvaritch V., Myslovych M., Gyzhko Y. Application of Linear Random Processes to Construction of Diagnostic System for Power Engineering Equipment. In: Dolgui A., Bernard A., Lemoine D., von Cieminski G., Romero D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 630. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-85874-2_67
Kramer H. Mathematical methods of statistics. Moskva: Myr, 1976. 476 p. (Rus)
Hertsyk S.M. Formation of training groups for diagnostic systems of electric power equipment, taking into account its modes of operation. Pratsi Instytutu Elektrodynamiky NAN Ukrainy. 2019. Issue 52. Pp. 54–61. (Ukr) DOI: https://doi.org/10.15407/publishing2019.52.054
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