Intelligent secondary control in smart microgrids: an on-line approach for islanded operations

  • Shoresh Shokoohi
  • Sajjad Golshannavaz
  • Rahmat Khezri
  • Hassan Bevrani


Dealing with islanded microgrids (MGs), this paper aims at improving the secondary control process to restrict the fluctuations in both the voltage and frequency signals. With the aim of retrieving these parameters at the nominal values, an intelligent control scheme is devised to adjust the corresponding control parameters. To do so, an on-line self-optimizing control approach is embedded in the MG’s central controller. In the tuning process, evolutionary-based techniques such as genetic algorithms provide proper initial adjustment for the parameters. Subsequently, an artificial neural network (ANN) is triggered to provide accurate online modification of the control parameters. Specifically, the training capability of the ANN mechanism along with its extensibility feature avoids the dependency of the controller on the operating point conditions and accommodates different changes and uncertainty reflections. Detailed simulation studies are conducted to investigate the performance of the proposed approach, and the results are discussed in depth.


Microgrid (MG) Distributed generation (DG) Intelligent secondary voltage and frequency controller Artificial neural networks (ANNs) Self-optimizing on-line controller 


  1. Ahmadi S, Shokoohi S, Bevrani H (2015) A fuzzy logic-based droop control for simultaneous voltage and frequency regulation in an AC microgrid. Int J Electr Power Energy Syst 64:148–155CrossRefGoogle Scholar
  2. Bevrani H (2014) Robust power system frequency control, 2nd edn. Springer, BerlinzbMATHGoogle Scholar
  3. Bevrani H, Hiyama T (2011) Intelligent automatic generation control. CRC Press, Boca RatonGoogle Scholar
  4. Bevrani H, Shokoohi S (2013) An intelligent droop control for simultaneous voltage and frequency regulation in islanded microgrids. IEEE Trans Smart Grid 4(3):1505–1513CrossRefGoogle Scholar
  5. Bevrani H, Watanabe M, Mitani Y (2012a) Microgrid controls. In: Beaty HW (ed) Standard handbook for electrical engineers, Section 16, 16th edn. McGraw Hill, New YorkGoogle Scholar
  6. Bevrani H, Habibi F, Babahajyani P, Watanabe M, Mitani Y (2012b) Intelligent frequency control in an AC microgrid: online PSO-based fuzzy tuning approach. IEEE Trans Smart Grid 3(4):1935–1944CrossRefGoogle Scholar
  7. Bevrani H, Habibi F, Shokoohi S (2012c) ANN-based self-tuning frequency control design for an isolated microgrid. In: Meta-heuristics optimization algorithms in engineering, business, economics, and finance, IGI GlobalGoogle Scholar
  8. Bevrani H, Watanabe M, Mitani Y (2014) Power system monitoring and control. Wiley, HobokenCrossRefGoogle Scholar
  9. Bevrani H, Feizi MR, Ataee S (2015) Robust frequency control in an islanded microgrid: H∞ and μ-synthesis approaches. IEEE Trans Smart Grids, pp 1–12Google Scholar
  10. De Brabandere K, Bolsens B, Van den Keybus J, Woyte A, Driesen J, Belmans R (2007) A voltage and frequency droop control method for parallel inverters. IEEE Trans Power Electron 22(4):1107–1115CrossRefGoogle Scholar
  11. Etemadi AH, Davison EJ, Iravani R (2012) A decentralized robust control strategy for multi-DER microgrids. Part I. Fundamental concepts. IEEE Trans Power Deliv 27(4):1843–1853CrossRefGoogle Scholar
  12. Fathi M, Bevrani H (2013) Statistical cooperative power dispatching in interconnected microgrids. IEEE Trans Sustain Energy 4(3):586–593CrossRefGoogle Scholar
  13. Fogel DB, Fogel LJ (1994) Evolutionary computation. IEEE Trans Neural Netw 5(1):1–2CrossRefzbMATHGoogle Scholar
  14. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99CrossRefGoogle Scholar
  15. Guerrero JM, Vasquez JC, Matas J, de Vicuña LG, Castilla M (2011) Hierarchical control of droop-controlled AC and DC microgrids—a general approach toward standardization. IEEE Trans Ind Electron 58:158–172CrossRefGoogle Scholar
  16. Gupta MM, Jin L, Homma N (2004) Static and dynamic neural networks: from fundamentals to advanced theory. Wiley, New YorkGoogle Scholar
  17. Hagan MT, Demuth HB, Beale MH (1996) Neural network design. Pws Pub, BostonGoogle Scholar
  18. Hatziargyriou N, Donnelly M, Papathanassiou S, Lopes JP, Takasaki M, Chao H, Usaola J, Lasseter R, Efthymiadis A, Karoui K, Arabi S (2000) Modeling new forms of generation and storage. Cigre Technical Brochure, CIGRE TF38.01.10, pp 1–140Google Scholar
  19. Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  20. IEEE standard for interconnecting distributed resources with electric power systems. IEEE Std 1547-2003, pp 1–28 (2003)Google Scholar
  21. Khezri R, Shokoohi S, Golshannavaz S, Bevrani H (2015) Intelligent over-current protection scheme in inverter-based microgrids. In: Smart grid conference (SGC), pp 53–59Google Scholar
  22. Khezri R, Golshannavaz S, Shokoohi S, Bevrani H (2017a) Toward intelligent transient stability enhancement in inverter-based microgrids. Neural Comput Appl. Google Scholar
  23. Khezri R, Golshannavaz S, Vakili R, Memar-Esfahani B (2017b) Multi-layer fuzzy-based under-frequency load shedding in back-pressure smart industrial microgrids. Energy 132:96–105CrossRefGoogle Scholar
  24. Marwali MN, Keyhani A (2004) Control of distributed generation systems. Part I. Voltages and currents control. IEEE Trans Power Electron 19(6):1541–1550CrossRefGoogle Scholar
  25. Mishra SK (2009) Design-oriented analysis of modern active droop-controlled power supplies. IEEE Trans Ind Electron 56:3704–3708CrossRefGoogle Scholar
  26. Rechenberg I (1994) Evolutions strategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. frommann-holzbog, Stuttgart, 1973. Step-size adaptation based on non-local use of selection information. In: Parallel problem solving from nature (PPSN3)Google Scholar
  27. Sarangapani J (2006) Neural network control of nonlinear discrete-time systems. CRC Press, Boca RatonCrossRefGoogle Scholar
  28. Schwefel H-P (1977) Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie: mit einer vergleichenden Einführung in die Hill-Climbing-und Zufallsstrategie. Birkhäuser, BaselCrossRefzbMATHGoogle Scholar
  29. Shokoohi S, Sabori F, Bevrani H (2014) Secondary voltage and frequency control in islanded microgrids: online ANN tuning approach. In: Smart grid conference (SGC), Tehran, pp 1–6Google Scholar
  30. Tiwari MK, Vidyarthi NK (2000) Solving machine loading problems in a flexible manufacturing system using a genetic algorithm based heuristic approach. Int J Prod Res 38(14):3357–3384CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Shoresh Shokoohi
    • 1
  • Sajjad Golshannavaz
    • 2
  • Rahmat Khezri
    • 1
  • Hassan Bevrani
    • 1
  1. 1.Electrical Engineering DepartmentUniversity of KurdistanSanandajIran
  2. 2.Electrical Engineering DepartmentUrmia UniversityUrmiaIran

Personalised recommendations