Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Electricity Markets in the United States: A Brief History, Current Operations, and Trends Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Signal Processing in Smart Grids: From Data to Reliable Information

verfasst von : Meng Wang

Erschienen in: Smart Grid Control

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

The recent proliferation of data is revolutionizing the practice of power system monitoring and control. With the Smart Grid initiative, more than two thousand multi-channel phasor measurement units (PMUs) [37] have now been installed in North America [35]. PMUs can directly measure GPS-synchronized bus voltage phasors, line current phasors, and the frequency, at a rate of 30 or 60 samples per second per channel. Compared to the conventional Supervisory Control and Data Acquisition (SCADA) systems that only provide measurements every 2–5 s, which are not accurately synchronized in time, PMUs can drastically improve the system visibility and enhance the situational awareness.

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
Vorheriges Kapitel Research Challenges for Design and Implementation of Wide-Area Control
Nächstes Kapitel WAMS-Based Controlled System Separation to Mitigate Cascading Failures in Smart Grid
Fußnoten
1
The location of line trip events are considered as successful if one of the two related buses are correctly identified.
 
Literatur
1.
F. Aminifar, M. Fotuhi-Firuzabad, A. Safdarian, A. Davoudi, M. Shahidehpour, Synchrophasor measurement technology in power systems: Panorama and state-of-the-art. IEEE Access 2, 1607–1628 (2014)CrossRef
2.
R.B. Bobba, K.M. Rogers, Q. Wang, H. Khurana, K. Nahrstedt, T.J. Overbye, Detecting false data injection attacks on DC state estimation, in Proceedings of the First Workshop on Secure Control Systems (SCS) (2010)
3.
A. Bykhovsky, J.H. Chow, Power system disturbance identification from recorded dynamic data at the northfield substation. Int. J. Electr. Power Energy Syst. 25(10), 787–795 (2003)CrossRef
4.
5.
E.J. Candès, B. Recht, Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717–772 (2009)MathSciNetCrossRef
6.
O.P. Dahal, S.M. Brahma, H. Cao, Comprehensive clustering of disturbance events recorded by phasor measurement units. IEEE Trans. Power Del. 29(3), 1390–1397 (2014)CrossRef
7.
J. De La Ree, V. Centeno, J.S. Thorp, A.G. Phadke, Synchronized phasor measurement applications in power systems. IEEE Trans. Smart Grid 1(1), 20–27 (2010)CrossRef
8.
R. Diao, K. Sun, V. Vittal, R.J. O’Keefe, M.R. Richardson, N. Bhatt, D. Stradford, S.K. Sarawgi, Decision tree-based online voltage security assessment using PMU measurements. IEEE Trans. Power Syst. 24(2), 832–839 (2009)CrossRef
9.
P. Gao, M. Wang, J.H. Chow, M. Berger, L.M. Seversky, Matrix completion with columns in union and sums of subspaces, in 2015 Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2015), pp. 785–789
10.
P. Gao, M. Wang, J.H. Chow, S.G. Ghiocel, B. Fardanesh, G. Stefopoulos, M.P. Razanousky, Identification of successive “unobservable” cyber data attacks in power systems. IEEE Trans. Signal Process. 64(21), 5557–5570 (2016)MathSciNetCrossRef
11.
P. Gao, M. Wang, S.G. Ghiocel, J.H. Chow, Modeless reconstruction of missing synchrophasor measurements, in Proceedings of the IEEE PES General Meeting (Selected in Best Papers Sessions) (2014), pp. 1–5
12.
P. Gao, M. Wang, S.G. Ghiocel, J.H. Chow, B. Fardanesh, G. Stefopoulos, Missing data recovery by exploiting low-dimensionality in power system synchrophasor measurements. IEEE Trans. Power Syst. 31(2), 1006–1013 (2016)CrossRef
13.
P. Gao, R. Wang, M. Wang, J.H. Chow, Low-rank matrix recovery from quantized and erroneous measurements: accuracy-preserved data privatization in power grids, in Proceedings of the Asilomar Conference on Signals, Systems, and Computers (2016)
14.
E. Ghahremani, I. Kamwa, Dynamic state estimation in power system by applying the extended kalman filter with unknown inputs to phasor measurements. IEEE Trans. Power Syst. 26(4), 2556–2566 (2011)CrossRef
15.
A.K. Ghosh, D.L. Lubkeman, The classification of power system disturbance waveforms using a neural network approach. IEEE Trans. Power Del. 10(1), 109–115 (1995)CrossRef
16.
J. Gomez-Vilardebo, D. Gunduz, Smart meter privacy for multiple users in the presence of an alternative energy source. IEEE Trans. Inf. Forensics Secur. 10(1), 132–141 (2015)CrossRef
17.
A. Hauer, D. Trudnowski, J.G. DeSteese, A perspective on wams analysis tools for tracking of oscillatory dynamics, in Proceedings of the IEEE Power Engineering Society General Meeting (2007), pp. 1–10
18.
R.A. Horn, C.R. Johnson, Matrix Analysis (Cambridge university press, 2012)
19.
H. Jiang, J.J. Zhang, D,W. Gao, Fault localization in smart grid using wavelet analysis and unsupervised learning, in Proceedings of the Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012, pp. 386–390
20.
H. Jiang, J.J. Zhang, W. Gao, Z. Wu, Fault detection, identification, and location in smart grid based on data-driven computational methods. IEEE Trans. Smart Grid 5(6), 2947–2956 (2014)CrossRef
21.
A. Kaci, I. Kamwa, L.-A. Dessaint, S. Guillon, Synchrophasor data baselining and mining for online monitoring of dynamic security limits. IEEE Trans. Power Syst. 29(6), 2681–2695 (2014)CrossRef
22.
H. Khurana, R. Bobba, T. Yardley, P. Agarwal, E. Heine, Design principles for power grid cyber-infrastructure authentication protocols, in Proceedings of the Hawaii International Conference on System Sciences (HICSS) (2010), pp. 1–10
23.
H. Khurana, M. Hadley, N. Lu, D.A. Frincke, Smart-grid security issues. IEEE Secur. Priv. 8(1), 81–85 (2010)CrossRef
24.
T. Kim, H. Poor, Strategic protection against data injection attacks on power grids. IEEE Trans. Smart Grid 2(2), 326–333 (2011)CrossRef
25.
O. Kosut, L. Jia, R. Thomas, L. Tong, Malicious data attacks on smart grid state estimation: attack strategies and countermeasures, in Proceedings of the IEEE International Conference on Smart Grid Communications (SmartGridComm) (2010), pp. 220–225
26.
W. Li, M. Wang, J.H. Chow, Fast event identification through subspace characterization of PMU data in power systems, in Proceedings of the IEEE Power and Energy Society (PES) General Meeting (2017), pp. 1-5
27.
W. Li, M. Wang, J.H. Chow, Real-time event identification through low-dimensional subspace characterization of high-dimensional synchrophasor data. IEEE Trans. Power Syst. (Early access) (2018)
28.
Y.-H. Lin, C.-W. Liu, C.-S. Chen, A new PMU-based fault detection/location technique for transmission lines with consideration of arcing fault discrimination-Part I: Theory and algorithms. IEEE Trans. Power Del. 19(4), 1587–1593 (2004)CrossRef
29.
G. Liu, J. Quintero, V.M. Venkatasubramanian, Oscillation monitoring system based on wide area synchrophasors in power systems, in Proceedings of the iREP Symposium-Bulk Power System Dynamics and Control-VII. Revitalizing Operational Reliability (IEEE, 2007), pp. 1–13
30.
L. Liu, M. Esmalifalak, Q. Ding, V.A. Emesih, Z. Han, Detecting false data injection attacks on power grid by sparse optimization. IEEE Trans. Smart Grid 5(2), 612–621 (2014)CrossRef
31.
Y. Liu, P. Ning, M.K. Reiter, False data injection attacks against state estimation in electric power grids, in Proceedings of the 16th ACM Conference on Computer and Communications Security, 2009, pp. 21–32
32.
J. Ma, Y. Makarov, C. Miller, T. Nguyen, Use multi-dimensional ellipsoid to monitor dynamic behavior of power systems based on PMU measurement, in Proceedings of the IEEE Power and Energy Society General Meeting (2008), pp. 1–8
33.
P. McDaniel, S. McLaughlin, Security and privacy challenges in the smart grid. IEEE Secur. Priv. 7(3), 75–77 (2009)CrossRef
34.
Y. Mo, T.H.-J. Kim, K. Brancik, D. Dickinson, H. Lee, A. Perrig, B. Sinopoli, Cyber-physical security of a smart grid infrastructure. Proc. IEEE 100(1), 195–209 (2012)CrossRef
35.
36.
37.
A. Phadke, J. Thorp, Synchronized Phasor Measurements and Their Applications (Springer, 2008)
38.
A. Reinhardt, F. Englert, D. Christin, Enhancing user privacy by preprocessing distributed smart meter data, in Proceedings of the Sustainable Internet and ICT for Sustainability (SustainIT) (2013), pp. 1–7
39.
H. Sandberg, A. Teixeira, K.H. Johansson, On security indices for state estimators in power networks, in Proceedings of the the First Workshop on Secure Control Systems (SCS) (2010)
40.
H. Sedghi, E. Jonckheere, Statistical structure learning of smart grid for detection of false data injection, in Proceedings of the IEEE Power and Energy Society General Meeting (PES) (2013), pp. 1–5
41.
A. Silverstein, J.E. Dagle, Successes and challenges for synchrophasor technology: an update from the north american synchrophasor initiative, in Proceedings of the Hawaii International Conference on System Science (HICSS) (2012), pp. 2091–2095
42.
J.E. Tate, T.J. Overbye, Line outage detection using phasor angle measurements. IEEE Trans. Power Syst. 23(4), 1644–1652 (2008)CrossRef
43.
G. Valverde, V. Terzija, Unscented Kalman filter for power system dynamic state estimation. IET Gener. Trans. Distrib. 5(1), 29–37 (2011)CrossRef
44.
M. Wang, J.H. Chow, P. Gao, X. T. Jiang, Y. Xia, S.G. Ghiocel, B. Fardanesh, G. Stefopolous, Y. Kokai, N. Saito, A low-rank matrix approach for the analysis of large amounts of power system synchrophasor data, in Proceedings of the IEEE Hawaii International Conference on System Sciences (HICSS) (2015), pp. 2637–2644
45.
M. Wang, G. de Mijolla, P. Gao, J. H. Chow, B. Fardanesh, G. Stefopoulos, S. Babaei, A. Ettlinger, D. Iles, D. Tran, Missing data recovery by exploiting low-dimensionality in synchrophasor measurements, in North American Synchrophasor Initiative meetings, March 2016
46.
S. Wang, L. Cui, J. Que, D.-H. Choi, X. Jiang, S. Cheng, L. Xie, A randomized response model for privacy preserving smart metering. IEEE Trans. Smart Grid 3(3), 1317–1324 (2012)CrossRef
47.
W. Wang, L. He, P. Markham, H. Qi, Y. Liu, Q.C. Cao, L.M. Tolbert, Multiple event detection and recognition through sparse unmixing for high-resolution situational awareness in power grid. IEEE Trans. Smart Grid 5(4), 1654–1664 (2014)CrossRef
48.
M. Wu, L. Xie, Online detection of low-quality synchrophasor measurements: a data-driven approach. IEEE Trans. Power Syst. 32(4), 2817–2827 (2017)CrossRef
49.
L. Xie, Y. Mo, B. Sinopoli, Integrity data attacks in power market operations. IEEE Trans. Smart Grid 2(4), 659–666 (2011)CrossRef
50.
M. Zhou, V.A. Centeno, J.S. Thorp, A.G. Phadke, An alternative for including phasor measurements in state estimators. IEEE Trans. Power Syst. 21(4), 1930–1937 (2006)CrossRef
51.
H. Zhu, G.B. Giannakis, Sparse overcomplete representations for efficient identification of power line outages. IEEE Trans. Power Syst. 27(4), 2215–2224 (2012)CrossRef
Metadaten
Titel
Signal Processing in Smart Grids: From Data to Reliable Information
verfasst von
Meng Wang
Copyright-Jahr
2019
Buch
Smart Grid Control

Print ISBN: 978-3-319-98309-7

Electronic ISBN: 978-3-319-98310-3

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-98310-3_11