Buy (120 RUB)
|Generate QR code
Open Access
Subscription or Fee Access
APPLICATION OF THE INVERSE PROBLEM SOLUTIONS OF LINEAR AUTOREGRESSIVE PROCESSES FOR SIMULATION OF VIBRATION SIGNALS OF ROTOR PATS WIND POWER GENERATORS
https://doi.org/10.15518/isjaee.2018.25-30.034-043
Abstract
Peculiar properties of vibration signals simulation of wind generators rotation nodes are considered. The application of vibration diagnosis as one of the most perspective diagnostic method of wind generators is justified.Advisability of statistical method application for the wind generators vibration diagnostics is proved. Mentioned, thateffective vibration expert system designing is composed from several stages. First – mathematical model of nodevibration with consideration of operation features should be elaborated. Second – it is necessary to develop hardwareand software. Third – it is necessary to develop methods of decisive rules for wind generator nodes diagnosis. Fourth– it is necessary to carry out experimental verification of developed vibration diagnostic methods. Solution of inverseproblem for autoregressive processes for the first time to apply for the problem solution. Generative processcharacteristic function finding method for linear autoregressive processes second-order AR(2) is used for the problemsolution. Properties of characteristic functions of stationary linear autoregressive processes (AR) are discussed. Thecharacteristic function may be represented as in canonical Kolmogorof form and in linear random process withdiscrete time form. Properties of transformation kernel and kernel of linear random process for the processes are alsoconsidered. Examples of Gamma second-order autoregressive generating process characteristic functiondetermination are represented. Poisson jump spectra's properties of characteristic function of the linear autoregressiveprocesses are also used for the solution of the problem. An example of application of vibration signal simulation ofwind power generator is considered. Application of the results for characteristic function vibration signals of windgenerator for modeling of the signals is shown. Features of application of the approach for modeling of rollingbearing vibration of wind generator USW 56-100 in frame of basic shift in radial direction near bearing at the side ofwheel hub are considered.Rotation speed of wind wheel hub was equal 72 rpm. The experimental research was shown that vibration signalsof the node may be simulated of second-order autoregressive process with Gamma distribution of generating process.Characteristic function of generating process for the autoregressive process was calculated. The article consideresexamples of kernel of wing generation vibration signals when determination of inverse problem takes place and kernel of wing generation vibration signals when solution of inverse problem is impossible.
About the Author
V. N. Zvaritch
Institute of Renewable Energy, National Academy of Sciences of Ukraine;
Institute of Electrodynamics, National Academy of Science of Ukraine
Ukraine
Ukraine
Valerij Zvaritch - D.Sc. in Engineering, Senior Researcher at the Institute of Renewable Energy at the Na tional Academy of Sciences of Ukraine, Leading Researcher at the Institute of Elec trodynamics at the National Academy of Sciences of Ukraine
20А G. Hotkevych Str., Kyiv-94, 02094,
56 Perempgy Av., Kyiv-57, 03680
References
1. Manning L. Bearing up to turbine testing. Power Engineering International (PEI), 2014;(2):32–34 (in Eng.).
2. Babak V., Filonenko S., Kornienko-Miftakhova I., Ponomarenko A. Optimization of Signal Features under Object's Dynamic Test, Aviation, 2008;12(1):10– 17 (in Eng.).
3. Gyzhko Y.I., Myslovych M.V., Sysak R.M. To use of spectral windows in analysis of vibration signals. Przeglad Electrotechnichny, 2013;89(2A):294–296 (in Eng.).
4. Bayar T. Putting Wind to the Test. Power Engineering International, 2015;(12):16–18 (in Eng.).
5. Zvarich V.N., Marchenko B.G. Linear processes of autoregression in vibrodiagnostics problems (Lineinye protsessy avtoregressii v zadachakh vibrodiagnostiki). Problemy Prochnosti i Nadezhnosti Mashin, 1994;(3):96–106 (in Russ.).
6. Zvaritch V.N., Marchenko B.G. Linear processes of autoregression in problems of vibrodiagnostics of sections of electric drivers. Technical Diagnostics and Nondestructive Testing, 1996;8(1):45–54 (in Eng.).
7. Marchenko B.G., Myslovich M.V. Vibration Diagnosis of rolling bearings of electric driver parts (Vibrodiagnostika podshipnikovykh uzlov elektricheskikh mashin.). Moscow – Kiev: Naukova dumka Publ., 1992; 196 p. (in Russ.).
8. Zvarich V.N. Autoregression methods application for development of wind generators diagnostic systems (Primenenie metodov avtoregressii dlya postroeniya sistem vibrodiagnostiki vetroagregatov). Renewable Power Engineering (Vozobnovlyaemaya energetika), 2005;(1):49–54 (in Russ.).
9. Zvaritch V., Glazkova E. Application of Linear AR and ARMA Processes for Simulation of Power Equipment Diagnostic System Information Signals. Proceedings 2015 16 th International Conference on Computational problems of Electrical Engineering (CPEE), Lviv, Ukraine, September 2–5, 2015; 259–261 (in Eng.).
10. Zvaritch V., Myslovitch M., Martchenko B. White Noise in Information Signals Models. Applied Mathematics Letters, 1994;7(3):93–95 (in Eng.).
11. Zvaritch V., Myslovitch M., Martchenko B. The Model of Random Periodic Information Signals on the White Noise Bases. Applied Mathematics Letters, 1995;8(3):87–89 (in Eng.).
12. Marchenko N., Myslovich M., Sysak R. Vibration diagnostics of wind-driven power units with usage of statistical expert systems. Przeglad Electrotechnichny, 2005;89(2A):294–296 (in Eng.).
13. Zvaritch V., Glazkova E. Some Singularities of Kernels of Linear AR and ARMA Processes and Their Applications to Simulation of Information Signals. Computational Problems of Electrical Engineering, 2015;5(1):71–74 (in Eng.).
14. Torres G.L., Garia A., Blas M.D., Francisco A.D. Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Solar energy, 2005;79(1):65– 77 (in Eng.).
15. Hoelf D. When Virtual meets Reality. Power Engineering International, 2016;(9):26–27 (in Eng.).
16. Krasilnikov A.I. Models of noise type signals at the diagnostic systems of heat power engineering equipment (Modeli shumovykh signalov v sistemakh diagnostiki teploenergeticheskogo oborudovaniya). Kiev: Polygraph-service, 2014; 112 p.(in Russ.).
17. Krasilnikov A.I. Class of non-Gaussian distributions with zero skewness and curtosis. Radioelectronics and Communication Systems, 2013;56(6):312–320 (in Eng.).
18. Lawrance A.J. The Innovation Distributions for Gamma Distributed Autoregressive Process, Scandinavian Journal of Statistics. Theory and Applications, 1982;9:234–236 (in Eng.).
19. McKenzie Ed. Innovation Distributions for Gamma and Negative Binomial Autoregressions, Scandinavian Journal of Statistics. Theory and Applications, 1987;14:79–85 (in Eng.).
20. Alliot P. Some theoretical results on Markovswitching autoregressive models with gamma innovations. C.R. Acad. Sci. Paris, Ser. I 343; pp. 271– 274 (in Eng.).
21. Zvarich V.N., Marchenko B.G. Method of finding of generating processes characteristic functions of autoregression linear processes. Radioelectronics and Communication Systems, 1999;42(7):64–71 (in Eng.).
22. Zvarich V.N Application of invers problem solutions of the linear autoregressive processes for power equipment vibromonitoring (Ispol'zovanie reshenii obratnoi zadachi lineinykh protsessov avtoregressii dlya modelirovaniya vibratsionnykh signalov uzlov elektrotekhnicheskogo oborudovaniya). Tekhnichna Elektrodynamika, 2016;(2):83–89 (in Russ.).
23. Zarich V.N., Marchenko B.G. Generating process characteristic function in the model of stationary linear AR-gamma process. Radioelectronics and Communication Systems, 2002;45(8):7–11 (in Eng.).
24. Zvarich V.N., Marchenko B.G., Bedny N.S. Linear random processes in the some problems of information signals simulation (Lineinye sluchainye protsessy v nekotorykh zadachakh modelirovaniya informatsionnykh signalov). Electronic modeling (Elektronnoe modelirovanie), 2001;23(1):62–69 (in Russ.).
25. Zvarich V.N. Peculiarities of finding characteristic functions of generating process in the model of stationary linear AR(2) process with negative binomial distribution. Radioelectronics and Communication Systems, 2016;59(12):567–573 (in Eng.).
26. Golovko V.M., Kohanevich V.P., Shihaylov M.O., Sandoval K., Donets A.M. Simulation model of autonomous electric wind units with asynchronous generator for analysis of parameters (Imitatsionnaya model' dlya analiza parametrov avtonomnykh vetroelektricheskikh ustanovok s asinkhronnym generatorom). International Scientific Journal for Alternative Energy and Ecology (ISJAEE), 2017;16(4):42–51 (in Russ.).
Review
For citations:
Zvaritch V.N. APPLICATION OF THE INVERSE PROBLEM SOLUTIONS OF LINEAR AUTOREGRESSIVE PROCESSES FOR SIMULATION OF VIBRATION SIGNALS OF ROTOR PATS WIND POWER GENERATORS. Alternative Energy and Ecology (ISJAEE). 2018;(25-30):34-43. (In Russ.) https://doi.org/10.15518/isjaee.2018.25-30.034-043
Views:
694
Email this article 