Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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 64:148–155
Bevrani H (2014) Robust power system frequency control, 2nd edn. Springer, Berlin
Bevrani H, Hiyama T (2011) Intelligent automatic generation control. CRC Press, Boca Raton
Bevrani H, Shokoohi S (2013) An intelligent droop control for simultaneous voltage and frequency regulation in islanded microgrids. IEEE Trans Smart Grid 4(3):1505–1513
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 York
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):1935–1944
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 Global
Bevrani H, Watanabe M, Mitani Y (2014) Power system monitoring and control. Wiley, Hoboken
Bevrani H, Feizi MR, Ataee S (2015) Robust frequency control in an islanded microgrid: H∞ and μ-synthesis approaches. IEEE Trans Smart Grids, pp 1–12
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):1107–1115
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):1843–1853
Fathi M, Bevrani H (2013) Statistical cooperative power dispatching in interconnected microgrids. IEEE Trans Sustain Energy 4(3):586–593
Fogel DB, Fogel LJ (1994) Evolutionary computation. IEEE Trans Neural Netw 5(1):1–2
Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99
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 58:158–172
Gupta MM, Jin L, Homma N (2004) Static and dynamic neural networks: from fundamentals to advanced theory. Wiley, New York
Hagan MT, Demuth HB, Beale MH (1996) Neural network design. Pws Pub, Boston
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 1–140
Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
IEEE standard for interconnecting distributed resources with electric power systems. IEEE Std 1547-2003, pp 1–28 (2003)
Khezri R, Shokoohi S, Golshannavaz S, Bevrani H (2015) Intelligent over-current protection scheme in inverter-based microgrids. In: Smart grid conference (SGC), pp 53–59
Khezri R, Golshannavaz S, Shokoohi S, Bevrani H (2017a) Toward intelligent transient stability enhancement in inverter-based microgrids. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2859-1
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 132:96–105
Marwali MN, Keyhani A (2004) Control of distributed generation systems. Part I. Voltages and currents control. IEEE Trans Power Electron 19(6):1541–1550
Mishra SK (2009) Design-oriented analysis of modern active droop-controlled power supplies. IEEE Trans Ind Electron 56:3704–3708
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)
Sarangapani J (2006) Neural network control of nonlinear discrete-time systems. CRC Press, Boca Raton
Schwefel H-P (1977) Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie: mit einer vergleichenden Einführung in die Hill-Climbing-und Zufallsstrategie. Birkhäuser, Basel
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 1–6
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):3357–3384
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Shokoohi, S., Golshannavaz, S., Khezri, R. et al. Intelligent secondary control in smart microgrids: an on-line approach for islanded operations. Optim Eng 19, 917–936 (2018). https://doi.org/10.1007/s11081-018-9382-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11081-018-9382-9