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Distribution network topology identification based on synchrophasor

Dipartimento di Ingegneria Elettrica Elettronica e Informatica (DIEEI), University of Catania, viale A. Doria, 6-95125 Catania, Italy

Topical Section: Smart Grids and Networks

A distribution system upgrade moving towards Smart Grid implementation is necessary to face the proliferation of distributed generators and electric vehicles, in order to satisfy the increasing demand for high quality, efficient, secure, reliable energy supply. This perspective requires taking into account system vulnerability to cyber attacks. An effective attack could destroy stored information about network structure, historical data and so on. Countermeasures and network applications could be made impracticable since most of them are based on the knowledge of network topology. Usually, the location of each link between nodes in a network is known. Therefore, the methods used for topology identification determine if a link is open or closed. When no information on the location of the network links is available, these methods become totally unfeasible. This paper presents a method to identify the network topology using only nodal measures obtained by means of phasor measurement units.
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Keywords accuracy; distribution network; measurement errors; numerical methods; phasor measurement unit; power system state estimation; system of equations; topology identification

Citation: Stefania Conti, Santi A. Rizzo, Nunzio Salerno, Giuseppe M. Tina. Distribution network topology identification based on synchrophasor. AIMS Energy, 2018, 6(2): 245-260. doi: 10.3934/energy.2018.2.245


  • 1. Monticelli A (2004) Electric power system state estimation. P IEEE 88: 262–282.
  • 2. Kumar DMV, Srivastava SC, Shah S, et al. (1996) Topology processing and static state estimation using artificial neural networks. IEE P-Gener Transm D 143: 99–105.    
  • 3. Clements KA, Costa AS (2002) Topology error identification using normalized Lagrange multipliers. IEEE T Power Syst 13: 347–353.
  • 4. Singh N, Glavitsch H (1991) Detection and identification of topological errors in on line power system analysis. IEEE T Power Syst 6: 324–331.
  • 5. Costa IS, Leao JA (1993) Identification of topology errors in power system state estimation. IEEE T Power Syst 8: 1531–1538.    
  • 6. Silva APAD, Quintana VH, Silva APAD, et al. (1995) Pattern analysis in power system state estimation. Int J Elec Power 17: 51–60.    
  • 7. Singh D, Pandey JP, Chauhan DS (2005) Topology identification, bad data processing, and state estimation using fuzzy pattern matching. IEEE T Power Syst 20: 1570–1579.    
  • 8. Deka D, Backhaus S, Chertkov M (2015) Structure learning and statistical estimation in distribution networks-Part II. Eprint Arxiv, 1–1.
  • 9. Bolognani S, Bof N, Michelotti D, et al. (2013) Identification of power distribution network topology via voltage correlation analysis. IEEE Conference on Decision and Control. IEEE, 1659–1664.
  • 10. Moslehi K, Kumar R (2010) A reliability perspective of the smart grid. IEEE T Smart Grid 1: 57–64.    
  • 11. Conti S, Rizzo SA, El-Saadany EF, et al. (2014) Reliability assessment of distribution systems considering telecontrolled switches and microgrids. IEEE T Power Syst 29: 598–607.
  • 12. Rueda JL, Guaman WH, Cepeda JC, et al. (2013) Hybrid approach for power system operational planning with smart grid and small-signal stability enhancement considerations. IEEE T Smart Grid 4: 530–539.    
  • 13. Moeini-Aghtaie M, Farzin H, Fotuhi-Firuzabad M, et al. (2016) Generalized analytical approach to assess reliability of renewable-based energy hubs. IEEE T Power Syst 32: 368–377.
  • 14. Minciardi R, Sacile R (2012) Optimal control in a cooperative network of smart power grids. IEEE Syst J 6: 126–133.    
  • 15. Zubo RHA, Mokryani G, Rajamani HS, et al. (2016) Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: A review. Renew Sust Energ Rev 72: 1177–1198.
  • 16. Soltani NY, Kim SJ, Giannakis GB (2015) Real-time load elasticity tracking and pricing for electric vehicle charging. IEEE T Smart Grid 6: 1303–1313.    
  • 17. Gungor VC, Sahin D, Kocak T, et al. (2011) Smart grid technologies: communication technologies and standards. IEEE T Ind Inform 7: 529–539.    
  • 18. Hamilton B, Summy M (2011) Benefits of the smart grid [In My View]. IEEE Power Energy M 9: 104–112.    
  • 19. Ansari N (2013) CONSUMER: A novel hybrid intrusion detection system for distribution networks in smart grid. IEEE T Emerg Top Com 1: 33–44.    
  • 20. Conti S, Corte AL, Nicolosi R, et al. (2016) Impact of cyber-physical system vulnerability, telecontrol system availability and islanding on distribution network reliability. Sust Energ Grid Netw 6: 143–151.    
  • 21. Davis KR, Davis CM, Zonouz SA, et al. (2015) A cyber-physical modeling and assessment framework for power grid infrastructures. IEEE T Smart Grid 6: 2464–2475.    
  • 22. Hug G, Giampapa JA (2012) Vulnerability assessment of ac state estimation with respect to false data injection cyber-attacks. IEEE T Smart Grid 3: 1362–1370.    
  • 23. Li X, Poor HV, Scaglione A (2016) Blind topology identification for power systems. IEEE International Conference on Smart Grid Communications. IEEE, 91–96.
  • 24. Erseghe T, Tomasin S, Vigato A (2013) Topology estimation for smart micro grids via powerline communications. IEEE T Signal Proces 61: 3368–3377.    
  • 25. Zhao L, Song WZ, Tong L, et al. (2014) Topology identification in smart grid with limited measurements via convex optimization. Innovative Smart Grid Technologies-Asia. IEEE, 803–808.
  • 26. Singh R, Manitsas E, Pal BC, et al. (2010) A recursive bayesian approach for identification of network configuration changes in distribution system state estimation. IEEE T Power Syst 25: 1329–1336.    
  • 27. Luan W, Peng J, Maras M, et al. (2015) Smart meter data analytics for distribution network connectivity verification. IEEE T Smart Grid 6: 1964–1971.    
  • 28. Chatzarakis GE (2009) Nodal analysis optimization based on the use of virtual current sources: A powerful new pedagogical method. IEEE T Educ 52: 144–150.    
  • 29. Phasor Advanced FAQ-Phasor-RTDMS: Available from: http://www.phasor-rtdms.com/phaserconcepts/phasor_adv_faq.html.
  • 30. Vetter WJ (1975) Vector structures and solutions of linear matrix equations. Linear Algebra & Its Applications 10: 181–188.
  • 31. Depablos J, Centeno V, Phadke AG, et al. (2004) Comparative testing of synchronized phasor measurement units. Power Engineering Society General Meeting. IEEE 1: 948–954.
  • 32. Baran ME, Wu FF (1989) Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE T Power Deliver 4: 1401–1407.    


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