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Electronic modeling

Vol 40, No 2 (2018)

https://doi.org/10.15407/emodel.40.02

CONTENTS

Mathematical Modeling and Computation Methods

  VYNNYCHUK S.D.
Definition of Flow Distribution in Networks with the Predominant Tree Structure of the Graph on the Basis of Potential Values at the Midpoint of Branch-Chords
3-16
  ZVARITCH V.N., MYSLOVYCH M.V.
White Noise in Some Simulation Problems of Information Signals
17-26

Informational Technologies

  ABRAMOVYCH R.P., BALVA A.A., SAMOILOV V.D.
Integrated Technology for Designing Computer-Aided Scenario Type Software Used for Training Personnel of Energy Industry
27-42

Computational Processes and Systems

  SAPOZHNIKOV V.V., SAPOZHNIKOV Vl.V., EFANOV D.V.
Weighted Codes with Permutations – a New Class of Codes with Summation for Technical Diagnostics of Discrete Systems
43-70

Application of Modeling Methods and Facilities

  MAKHNENKO O.V., SAPRYKINA G.Yu.
Modeling of Residual Stresses in the Area of Technological Welds of PWR-1000 Reactor Shells
71-94
  PODGURENKO V.S., TEREKHOV V.E.
Mathematical Modeling of the Pitch-Controlled Wind Turbine Work
95-104
  POPOV V.A., YARMOLIUK O.S., TKACHENKO F.V., YATSENKO D.V.
Features of Multicriteria Evaluation of Alternative Options of Distributed Generation Applications Taking in Conditions of Initial Information Uncertainty
105-118
Chronicle and information 119-123

DEFINITION OF FLOW DISTRIBUTION IN NETWORKS WITH THE PREDOMINANT TREE STRUCTURE OF THE GRAPH ON THE BASIS OF POTENTIAL VALUES AT THE MIDPOINT OF BRANCH-CHORDS

S.D. Vynnychuk, Dr Sc. (Eng.),
Pukhov Institute for Problems of Modelling in Energy Engineering, NAS of Ukraine,
15 General Naumov St, Kyiv, 03164, Ukraine, e-mail:  This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2018, 40(2):03-16
https://doi.org/10.15407/emodel.40.02.003

ABSTRACT

An algorithm RP is proposed for calculating the flow distribution in distributive networks with graph G of the dominant tree structure, in which the number of cycles h does not exceed the root from the number of its nodes V, with linear dependences of the potential change on the current. The algorithm is based on reducing the graph to a tree by breaking the branches-chords and determining the value of the potential at their midpoint. The algorithm provides for the calculation of two currents for fixed potentials, the computational complexity of which is T (E) =O (E), where E is the number of branches of the graph. To determine the unknown potentials at the midpoints h of the branches-chords, a system of linear equations of the order h is formed, the coefficients and right-hand parts of which are formed from the results of h additional calculations of currents for
different variants of fixed values of the potentials. The computational complexity of determining the unknown potentials and currents is of the order O (hE* + E + h3), where E* is the number of branches of the equivalent graph G*, i.e., the subgraph G obtained on the basis of the folding of the hanging nodes. For h of magnitude no higher than O (V3/2), and the amount of necessary memory is proportional to the number of nodes in the graph. For h will be the order of magnitude no higher than O (V3/2), and the amount of necessary memory is proportional to the number of nodes in the graph. A method for analyzing the structure of the graph of the distribution system is proposed, which allows identifying the branches of the graph, the removal of which leads to the decomposition of the graph G* into components whereby the system of linear equations of order h can be divided into subsystems.

KEYWORDS

distribution network, flow distribution, equivalence, convolution, brancheschords.

REFERENCES

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2. Maksimovich, N.G. (1961), Lineinye elektricheskie tsepi i ikh preobrazovaniya [Linear circuits and their conversion], Gosenergoizdat, Moscow-Leningrad, Russia.
3. Pukhov, G.Ye. (1967), Metody analiza i sinteza kvazi analogovykh elektronnykh tsepey [Methods of analysis and synthesis of the quasi analog electronic circuits], Naukova Dumka, Kiev, Ukraine.
4. Seshu, S. and Rid, M.B. (1971), Lineinye grafy i elektricheskie tsepi, Per. s angl., pod red. P.A. Ionkina, Uchebnoe posobie dlya vuzov spetsialnostey radiotekhnika, elektronnaya tekhnika, elektropriborostroenie i avtomatika [Line graphs and circuits, Transl. from English., Ed. Ionkin, P.A., Textbook for Universities radio engineering specialties, electronic engineering, electrical instrumentation and automation], Vysshaya shkola, Moscow, Russia.
5. Sigorskiy, V.P. and Petrenko, A.I. (1970), Algoritmy analiza elektronnykh skhem [Algorithms analysis of electronic circuits], Tekhnika, Kiev, Ukraine.
6. Shakirov, I.A. (1980), “Universal conversion and diakoptics circuits”, Abstract of Dr. Sci. (Tech.) dissertation, Leningrad, Russia.
7. Vynnychuk, S.D. (2016), “Definition of stream distribution in networks with a tree-like graph”, Elektronnoe modelirovanie, Vol. 38, no. 4, pp. 65-80.
8. Merenkov, A.P. and Khasilev, V.Ya. (1985), Teoriya gidravlicheskikh tsepey [Theory of hydraulic circuits], Nauka, Moscow, Russia.
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10. Shargin, Yu.M. and Merkuriev, A.G. (2003), “Calculation of electrical modes of the method of equivalent transformation”, Elektrichestvo, no. 4, pp. 53-55.
11. Vynnychuk, S.D. and Samoylov, V.D. (2015), “Determination of the currents in the switching structures of the electrical energy networks with tree graph’s structure”, Elektronnoe modelirovanie, Vol. 37, no. 5, pp. 89—104.
12. Bessonov, L.A. (1978), Teoreticheskie osnovy elektrotekhniki: Elektricheskie tsepi. Uchebnik dlya studentov elektrotekhnicheskikh, energeticheskikh i priborostroitelnykh spetsialnostey vuzov, 7-ye izd., pererabotannoye i dop. [Theoretical fundamentals of electrical engineering: Electric circuits. Textbook for students of electrical, power and instrument engineering specialties of universities, 7-th ed., revised and additional], Vysshaya shkola, Moscow, Russia.

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White Noise in Some Simulation Problems of Information Signals

V.N. Zvaritch, Dr Sc. (Eng.), M.V. Myslovych, Dr Sc. (Eng.)
Institute of Electrodynamics of the National Academy of Sciences of Ukraine
56, Pobeda Av. Kyiv, 03680, Ukraine,
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2018, 40(2):17-26
https://doi.org/10.15407/emodel.40.02.017

ABSTRACT

A constructive method of information signal mathematical models characterization on the white noise basis is developed. Linear random processes, linear random processes with periodic structures, linear autoregressive processes, linear autoregressive processes with periodic structures are represented as examples of the method application.

KEYWORDS

white noise, linear random process, linear random process with periodic structures, linear autoregressive processes.

REFERENCES

1. Ito, K. (1954), Stationary random distribution, Mem. Coll. Univ., Vol. 28, pp.209-223.
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3. Khinchin, A. (1938), Predelnye zakony dlya sum nezavisimykh stokhasticheskikh velichin [Limit laws for sums of independent stochastic values], ONTI, Moscow-Leningrad, USSR.
4. Martchenko, B. (1973), Metod stokhasticheskikh integralnykh predstavleniyi i yego primeneniye v radiotekhnike [Method of stochastic integral representations and its applications in radio-engineering], Naukova dumka, Kiev, USSR.
5. Brockwell, P.J. and Davis, R.A. (2002), Introduction to time series and forecasting, 2nd edition, Springer, New York, USA.
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6. Iwueze, I.S., Arimie, C.O., Iwu, H.C. and Onyemachi, E. (2017), Some applications of the linear Gaussian white noise process, Applied Mathematics, Vol. 8, pp. 1918-1938.
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7. Zvaritch, V., Myslovitch, M. and Martchenko, B. (1994), White noise in information signals models, Appl. Math. Lett., Vol. 7, no. 3, pp. 93-95.
https://doi.org/10.1016/0893-9659(94)90120-1
8. Ogura, H. (1971), Spectral representation of a periodic nonstationary random process, IEEE Transaction of Information Theory, Vol. IT-17, no. 2, pp. 143-149.
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9. Skhorokhod, A. (1964), Sluchainye protsessy c nezavisimymi prirashcheniyami [Random processes with independent increments], Nauka, Moscow, USSR.
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11. Zvaritch, V., Myslovitch, M. and Martchenko, B. (1995), The models of random periodic information signals on the white noise bases, Appl. Math. Lett., Vol. 8, no. 3, pp. 87-89.
https://doi.org/10.1016/0893-9659(95)00035-O
12. Zvaritch, V., Myslovitch, M. and Martchenko, B. (1995), Stochastically periodical random processes used as models of information signals, Radioelectronics and Communication Systems, Vol. 38, no. 1, pp. 129-132.
13. Zvarich, V.N. and Marchenko, B.G. (1999), “The method of determining characteristic functions of the generating processes for linear autoregression processes”, Izvestiya Vysshikh Uchebnykh Zavedeniy Radioelektroniki, Vol. 42, no. 7, pp. 64-71.
14. Zvarich, V.N. and Marchenko, B.G. (2002), “Characteristic function of the generating process in the model of stationary linear AR-gamma process”, Izvestiya Vysshikh Uchebnykh Zavedeniy Radioelektroniki, Vol. 45, no. 8, pp. 12-18.
15. Zvarich, V.N. (2016), 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, Vol. 59, no. 12, pp. 567-573.
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16. Zvarich, V.N. and Marchenko, B.G. (2011), Linear autoregressive processes with periodic structures as models of information signals, Radioelectronics and Communication Systems, Vol. 54, no. 7, pp. 367-372.
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17. Shao, X. (2011), Testing for white noise under unknown dependence and its applications to diagnostic checking for time seriesmodels, Econometric Theory,Vol. 27, no. 2, pp. 312-343.
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18. Zvaritch, V. and Glazkova, E. (2015), Some singularities of kernels of linear AR and ARMA processes and their applications to simulation of information signals, Computational Problems of Electrical Engineering, Vol. 5, no. 1, pp. 71-74.

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INTEGRATED TECHNOLOGY FOR DESIGNING COMPUTER-AIDED SCENARIO TYPE SOFTWARE USED FOR TRAINING PERSONNEL OF ENERGY INDUSTRY

1 R.P. Abramovych, 2 A.A. Balva, Cand. Sc. (Eng.), 2 V.D. Samoilov, Dr Sc. (Eng.),
1 Scientific-Production Enterprise ASOT
33 Navrotsky St, Lviv, 79034, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
2 Pukhov Institute for Problems of Modelling in Energy Engineering, NAS of Ukraine,
15 General Naumov St, Kyiv, 03164, Ukraine, This email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2018, 40(2):27-42
https://doi.org/10.15407/emodel.40.02.027

ABSTRACT

An integrated technology for development of computer-aided software aimed at training energy industry personnel is presented. This technology, based on a scenario-pedagogical design method with the use of graphic specifications, is implemented in the adapted FLASH multimedia package as a construction set in order to involve industry specialists into the design process. Examples of technology components implementation in construction of training scenario type systems for personnel of distribution energy companies are presented.

KEYWORDS

computer-aided training software, scenario, situational training simulators, graphical specification, computer-integrated technology, energy company.

REFERENCES

1. Samoilov, V.D. (2007), Modelnoye konstruirovaniye kompyuternykh prilozheniy [Model design of computer applications], Naukova dumka, Kyiv, Ukraine. 
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7. Vynnychuk, S.D. and Samoilov, V.D. (2015), “Determination of currents in commutative structure of the electricity networks with a tree structure graph”, Elektronnoe modelirovanie, Vol. 37, no. 5, pp. 89-104.
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WEIGHTED CODES WITH PERMUTATIONS — A NEW CLASS OF CODES WITH SUMMATION FOR TECHNICAL DIAGNOSTICS OF DISCRETE SYSTEMS

V.V. Sapozhnikov,  Dr Sc. (Eng.), Vl.V. Sapozhnikov, Dr Sc. (Eng.),D. V. Efanov,  Cand. Sc. (Eng.),
Emperor Alexander State University of Communication Means of St.-Petersburg
9 Moskovsky Ave, St.-Petersburg, 190031, Russian Federation, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2018, 40(2):43-70
https://doi.org/10.15407/emodel.40.02.043

ABSTRACT

A method for formation of a code with summation that effectively detects errors of small multiplicity is described. The method is based on weighting the transitions between the bits occupying neighboring positions in data vectors, the weight coefficients from the natural number of numbers, the subsequent calculation of the modified weight of the data vector, and also by applying a series of special permutations of the resulting weights between the data vectors. The check functions of the resulting «permutation» code are linear and are described only using the addition operation modulo two. The properties of error detection of various types andmultiplicities by new codes are analyzed, and a comparison with known codes revealing double errors is made. It is established that the new code detects any twofold errors in data vectors with data vector lengths m <10. It is shown that the permutation code has the advantage over any known codes detecting double errors,with data vectors lengthsm=8 andm= 9. Experimentswith benchmarks have allowed to confirm the theoretical results, aswell as to evaluate the effectiveness of the application of the new code in the concurrent error detection systems by the indicators of structural redundancy.

KEYWORDS

technical diagnostics of discrete systems, summation code, Berger code, Hamming code, modified weighted code, detection of double errors, structural redundancy.

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