nach oben

Journal of Computer and Systems Sciences International

Erschienen in:

01.11.2020 | DATA PROCESSING AND IDENTIFICATION

Algebraic Method of Nonparametric Identification of Abnormal Modes of the Power System as a Dynamic MIMO System

verfasst von: I. M. Galiaskarov, N. E. Zubov, E. Yu. Zybin, V. N. Ryabchenko

Erschienen in: Journal of Computer and Systems Sciences International | Ausgabe 6/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

An approach is proposed to identify abnormal modes of a power system in real time, using only the data of synchronized vector measurements and that does not require a priori information about the parameters of its mathematical model. The algorithmic base of the method is the algebraic criterion of consistency of the linear matrix equation for the identification of the mathematical model of the power system; it does not imply the solution of parametric identification or prediction problems, does not use statistical calculations, and does not require preliminary training or long-term tuning. The effectiveness of the method is demonstrated by the example of detecting the moment an emergency situation arises in the Omsk power system.

Vorheriger Artikel Stability of an Interval Family of Differential-Algebraic Equations

Nächster Artikel An Optimal Recurrent Logical-Dynamical Filter of a High Order and Its Covariance Approximations

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

R. E. Kalman, P. L. Falb and M. A. Arbib, Topics in Mathematical System Theory (McGraw-Hill, New York, 1969).MATH

T. Kailath, Linear Systems (Prentice Hall, NJ, 1990).MATH

H. A. Nour-Eldin, “Linear multivariable systems controllability and observability: Numerical aspects,” in Systems and Control Encyclopedia, Ed. by M. G. Singh (Pergamon, Oxford, UK, 1987), pp. 2816–2827.

A. A. Golovan and N. A. Parusnikov, “On the connection between the concept of stochastic estimability and singular value decompositions of matrices,” Avtom. Telemekh., No. 2, 45–50 (1998).

M. G. Gadzhiev, E. A. Gulevich, V. N. Ryabchenko, and Yu. V. Sharov, “About PMU placement for the identification of the mathematical model of the power system mode,” Elektrichestvo, No. 5, 4–10 (2018).CrossRef

M. M. Eissa, M. E. Masoud, and M. M. M. Elanwar, “A novel back up wide area protection technique for power transmission grids using phasor measurement unit,” IEEE Trans. Power Deliv. 25, 270–278 (2010).CrossRef

M. K. Neyestanaki and A. M. Ranjbar, “An adaptive PMU-based wide area backup protection scheme for power transmission lines,” IEEE Trans. Smart Grid 6, 1550–1559 (2015).CrossRef

Y. Ge, A. J. Flueck, D. K. Kim, et al., “Power system real-time event detection and associated data archival reduction based on synchrophasors,” IEEE Trans. Smart Grid 6, 2088–2097 (2015).CrossRef

M. Biswal, S. M. Brahma, and H. Cao, “Supervisory protection and automated event diagnosis using PMU data,” IEEE Trans. Power Deliv. 31, 1855–1863 (2016).CrossRef

10.

T. Overbye, P. Sauer, C. DeMarco, et al., “Using PMU data to increase situational awareness,” Power Syst. Eng. Res. Center Publ. 21, 10–16 (2010).

11.

K. B. Swain and C. C. Mahato, “A brief review on fault detection, classification and location on transmission lines using PMUs,” Int. J. Manage. Technol. Eng. 8, 2608–2618 (2018).

12.

F. Aminifar, M. Fotuhi-Firuzabad, A. Safdarian, A. Davoudi, and M. Shahidehpour, “Synchrophasor measurement technology in power systems: Panorama and state-of-the-art,” IEEE Access 2, 1607–1628 (2014).CrossRef

13.

E. Yu. Zybin and V. V. Kos’yanchuk, “An algebraic criterion for detecting the fact and time a fault occurs in control systems of dynamic plants,” J. Comput. Syst. Sci. Int. 55, 546 (2016).MathSciNetCrossRef

14.

L. A. Mironovskii, Functional Diagnostics of Dynamic Systems (MGU-GRIF, Moscow, St. Petersburg, 1998) [in Russian].

15.

L. P. Kolodezhnyi and A. V. Chernodarov, Reliability and Technical Diagnostics (VVA im. N. E. Zhukovskogo and Yu. A. Gagarina, Moscow, 2010) [in Russian].

16.

M. Kh. Khusniyarov, M. F. Sunagatov, and D. S. Matveev, Fundamentals of Reliability and Diagnostics of Technical Systems (UGNTU, Ufa, 2011) [in Russian].

17.

A. N. Zhirabok, “Diagnostic observers and parity relation: Comparative analysis,” Autom. Remote Control 73, 873 (2012).MathSciNetCrossRef

18.

K. Chen, C. Huang, and J. He, “Fault detection, classification and location for transmission lines and distribution systems: A review on the methods,” High Voltage 1, 25–33 (2016).CrossRef

19.

F. Mohammadi, C. Zheng, and R. Su, “Fault diagnosis in smart grid based on data-driven computational methods,” in Proceedings of the 5th International Conference on Applied Research in Electrical, Mechanical, and Mechatronics Engineering, Tehran, Iran, 2019, Vol. 24.

20.

Z. Yang, Yu Chen, N. Zhou, et al., “Data-driven online distributed disturbance location for large-scale power grids,” IEEE Trans. Smart Grid 2, 381–390 (2019).CrossRef

21.

M. Mousa, S. Abdelwahed, J. Kluss, et al., “Review of fault types, impacts, and management solutions in smart grid systems,” Smart Grid Renewable Energy 10 (04), 98 (2019).CrossRef

22.

M. Glavic and T. van Cutsem, “A short survey of methods for voltage instability detection,” in Proceedings of the IEEE Power and Energy Society General Meeting, Detroit (IEEE, 2011), pp. 1–8.

23.

Y. Jiang, S. Yin, and O. Kaynak, “Data-driven monitoring and safety control of industrial cyber-physical systems: Basics and beyond,” IEEE Access 6, 47374–47384 (2018).CrossRef

24.

Y. Xiong, W. Yao, W. Chen, et al., “A data-driven approach for fault time determination and fault area location using random matrix theory,” Int. J. Electr. Power Energy Syst. 116, 105566 (2020).CrossRef

25.

D. Huang, Q. Chen, S. Ma, et al., “Wide-area measurement-based model-free approach for online power system transient stability assessment,” Energies 11, 958 (2018).CrossRef

26.

S. Xu, “A survey of knowledge-based intelligent fault diagnosis techniques,” J. Phys.: Conf. Ser. 1187, 032006 (2019).

27.

H. A. Tokel, R. Al Halaseh, G. Alirezaei, et al., “A new approach for machine learning-based fault detection and classification in power systems,” in Proceedings of the IEEE Power Energy Society Innovative Smart Grid Technologies Conference ISGT, Sarajevo (IEEE, 2018), pp. 1–5.

28.

M. Kesici, K. B. Saner, V. M. I. Genc, et al., “Wide area measurement based online monitoring and event detection using convolutional neural networks,” in Proceedings of the 7th International Istanbul Smart Grids and Cities Congress and Fair ICSG, Istanbul (IEEE, 2019), pp. 223–227.

29.

W. Li, D. Deka, M. Chertkov, et al., “Real-time faulted line localization and PMU placement in power systems through convolutional neural networks,” IEEE Trans. Power Syst. 34, 4640–465 (2019).CrossRef

30.

A. Karić, T. Konjić, and A. Jahić, “Power system fault detection, classification and location using artificial neural networks,” in Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (Springer, Cham, 2017), pp. 89–101.

31.

M. G. Gadzhiev, M. Sh. Misrikhanov, V. N. Ryabchenko, and Yu. V. Sharov, Matrix Methods for Analysis and Management of Transient Processes in Electric Power Systems (MEI, Moscow, 2019) [in Russian].

32.

N. E. Zubov, E. A. Mikrin, and V. N. Ryabchenko, Matrix Methods in the Theory and Practice of Automatic Control Systems of Aircraft (MGTU im. N. E. Baumana, Moscow, 2016) [in Russian].

33.

B. T. Polyak and P. S. Shcherbakov, Robust Stability and Control (Nauka, Moscow, 2002) [in Russian].

34.

K. Zhou, Essentials of Robust Control (Prentice Hall, Upper Saddle River, NJ, 1998).

35.

D. S. Watkins, Fundamentals of Matrix Computations (Wiley, Hoboken, 2010).MATH

36.

D. S. Bernstein, Matrix Mathematics (Princeton Univ. Press, Princeton, 2005).

37.

C. B. Chang and M. Athans, “State estimation for discrete systems with switching parameters,” IEEE Trans. Aerospace Electron. Syst., No. 3, 418–425 (1978).

38.

J. D. Hamilton, “Analysis of time series subject to changes in regime,” J. Econometr., No. 45 (1), 39–70 (1990).

39.

Z. Ghahramani and G. E. Hinton, “Switching state-space models,” Tech. Report CRG-TR-96-2 (Univ. of Toronto, Toronto, 1996).

40.

E. Fox, E. B. Sudderth, M. I. Jordan, and A. S. Willsky, “Nonparametric bayesian learning of switching linear dynamical systems,” Adv. Neural Inform. Process. Syst., No. 1, 457–464 (2009).

41.

E. Yu. Zybin, M. Sh. Misrikhanov, and V. N. Ryabchenko, “Minimal parametrization of solutions of linear matrix equations,” Vestn. IGEU, No. 6, 127–131 (2004).

42.

E. Yu. Zybin, “On identifiability of closed-loop linear dynamical systems under normal operating conditions,” Izv. YuFU, Tekh. Nauki, No. 4 (166), 160–170 (2015).

Titel: Algebraic Method of Nonparametric Identification of Abnormal Modes of the Power System as a Dynamic MIMO System
verfasst von: I. M. Galiaskarov
N. E. Zubov
E. Yu. Zybin
V. N. Ryabchenko
Publikationsdatum: 01.11.2020
Verlag: Pleiades Publishing
Erschienen in: Journal of Computer and Systems Sciences International / Ausgabe 6/2020
Print ISSN: 1064-2307
Elektronische ISSN: 1555-6530
DOI: https://doi.org/10.1134/S1064230720060039

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 6/2020

The Concept of Constructing an Artificial Dispatcher Intelligent System Based on Deep Reinforcement Learning for the Automatic Control System of Electric Networks

Recursive Definitions of Tabular Transformations

Search-Detection-Recognition: Simulation via Thermal Images with Varying Quality

An Optimal Recurrent Logical-Dynamical Filter of a High Order and Its Covariance Approximations

Synthesis-Analysis of Derivation Sequences of Tables’ Functional Dependences

Equilibria and Compromises in Two-Person Zero-Sum Multicriteria Games

Premium Partner