Intelligent frequency control in AC microgrids using fuzzy logic
Keywords:
Microgrid, Controller, Fuzzy Logic, Frequency Control, Optimization.
Abstract
Dispersed generation resources in a microgrid are equipped with power electronics technologies including high-frequency AC systems (e.g., microturbines) and DC (e.g., solar panels and fuel cells). By enhancing the frequency controller and implementing a novel method, this paper seeks to reduce the microgrid frequency deviation in the dynamic mode. The proposed controller is intended for use in a renewable-energy microgrid. According to the strategy, fuzzy logic is used to adjust the proposed control coefficients at any time, and the group search optimizer (GSO) algorithm is employed to optimize them. The controller parameters are optimized to account for the uncertainty of some microgrid component parameters to improve the robust controller's performance. The key idea behind this proposed control strategy is to combine the capabilities of fuzzy logic with the proposed method to reduce control operations and achieve optimal fuzzy control in the transient mode to ensure resilient microgrid frequency control performance. The Fuzzy-PI-GSO frequency controller outperforms the standard Fuzzy-PI and PI frequency controllers in simulations with various disturbances.
References
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[2] Hosseinzadeh, Salmasi, F.R., “Power management of an isolated hybrid AC/DC micro-grid with fuzzy control of battery banks”, IEEE Transactions On Power Systems, Vol.21, No.1, 2013, pp.419-427.
[3] Baoquan Liu, Fang Zhuo ; Yixin Zhu ; Hao Yi., “System Operation and Energy Management of a Renewable Energy-Based DC Micro-Grid for High Penetration Depth Application”, IEEE Transactions On Power Systems, Vol.21, No.1, 2015, pp.589-597.
[4] Mirsaeidi, S Mat Said, D. ; Mustafa, M.W. ; Hafiz Habibuddin, M., “A protection strategy for micro-grids based on positive-sequence component”, IEEE Transactions On Power Systems, Vol.21, No.1, 2014, pp.151-162.
[5] H. Bevrani, M. Watanabe and Y. Mitani, “Microgrid Contorols, to be published as invited chapter in: standard handbook for electrical engineers”, 16th edition, H. wayne Beaty (ED), McGraw-Hill; Expected 2012.
[6] T. Rosmarino, “A Self-Funding Enterprise Solution to Reduce Power Consumption and Carbon Emissions”, The NYS Forum's May Executive Committee Meeting: Building an Energy Smart IT Environment, May 2014.
[7] M. Mahmoodi, M. McDonough, P. Shamsi, and B. Fahimi, “Stability Assessment of a DC Distribution Network in a Hybrid Micro-Grid Application”, IEEE Transportation Electrification Conference and Expo, 2012.
[8] K. Clement-Nyns, E. Haesen, and J. Driesen, “Analysis and control of capacitor-excited induction generators connected to a micro-grid through power electronic converters”, IEEE Trans. Power Systems, vol. 25, no. 1, pp. 371-380, Feb. 2010.
[9] A. S. Masoum, S. Deilami, P. S. Moses, M. A. S. Masoum, and A. Abu-Siada, “Smart load management of plug-in electric vehicles in distribution and residential networks with charging stations for peak shaving and loss minimization considering voltage regulation”, IET Generation, Transmission & Distribution, vol. 5, no. 8, pp. 877-888, August 2011.
[10] Y. Ota, H. Taniguchi, T. Nakajima, K. M. Liyanage, J. Baba, and A. Yokoyama, “Autonomous distributed V2G (Vehicle-to-Grid) satisfying scheduled charging”, IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 559-564, March 2012.
[11] McGowan J.G., Manwell J.F., “Hybrid wind/PV/diesel system experiences”, Renewable Energy, Vol.16, No. 1-4, pp. 928-933. 1999
[12] Pragya Nema R.K., Rangnekar S., “A current and future state of art development of hybrid energy system using wind and PV-solar: A review”, Renewable and Sustainable Energy Reviews, Vol.13, No. 8, pp.2096-2103.
[13] Yaniv O., “Quantitative feedback design of linear and nonlinear controlsystems”, Kluwer academic, 1999.
[14] Rerkpreedapong D. and Feliachi A., “Decentralized H∞ load frequency control using LMI control toolbox”, Proc. of the Inter. Symp. on Circuits andSystems, IEEE, vol.3 pp. III-411 - III-414, May 2003.
[15] Bevrani H. and Mitani Y. and Tsuji K., “Robust decentralized load frequency control using an iterative linear matrix inequalities algorithm”, IEEProc.Gener Transm. Distib, Vol. 151, No. 3, May 2004.
[16] Christie, R.D. and Bose, A., “Load Frequency Control Issues In Power System Operations after Deregulation”, IEEE Trans. Power System, Vol. 11, No. 3, pp. 1191–1200, 1996.
[17] Mojtaba Shivaie, Mohammad G. Kazemi, Mohammad T. Ameli., "A modified harmony search algorithm for solving load-frequency control of non-linear interconnected hydrothermal power systems", Sustainable Energy Technologies and Assessments, Vol. 10, No. 4, pp. 53-62, 2015.
[18] Wen Tan, Hongxia Zhang, Mei Yu., "Decentralized load frequency control in deregulated environments", International Journal of Electrical Power & Energy Systems, Vol. 41, No. 1, pp. 16-26, 2012.
[19] Banaja Mohanty, Sidhartha Panda, P.K. Hota., "Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system", International Journal of Electrical Power & Energy Systems, Vol. 54, No. 2, pp. 77-85, 2014.
[20] Hamed Shabani, Behrooz Vahidi, Majid Ebrahimpour., "A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems", ISA Transactions, Vol. 52, No. 1, pp. 88-95, 2013.
[21] Sopian K., Zamri Ibrahim M., Wan Daud W.R., Othman M.Y., Yatim B., Amin N., “Performance of a PV–wind hybrid system for hydrogen production”, Renewable Energy, Vol.34, No. 8, pp. 1973-1978. 2009.
[22] Michael Z. Bernard, Tarek Hassan Mohamed, Yaser Soliman Qudaih, Y. Mitani., "Decentralized load frequency control in an interconnected power system using Coefficient Diagram Method", International Journal of Electrical Power & Energy Systems, Vol. 63, No. 10, pp. 165-172, 2014.
[23] Yanmei Tang, Yan Bai, Congzhi Huang, Bin Du., "Linear active disturbance rejection-based load frequency control concerning high penetration of wind energy", Energy Conversion and Management, Vol. 95, No. 12, pp. 259-271, 2015.
[24] Khatib T., Mohamed A., Sopian K., “Optimization of a PV/wind micro-grid for rural housing electrification using a hybrid iterative/genetic algorithm: Case study of Kuala Terengganu, Malaysia”, Energy and Buildings, Vol.47, pp.321-331. 2012.
[25] Caballero F., Sauma E., Yanine F. “Business optimal design of a grid-connected hybrid PV (photovoltaic)-wind energy system without energy storage for an Easter Island's block”, Energy, Vol. 61, No.1, pp.248-261. 2013.
[26] Hocaoglu F.O., Gerek O.N., Kurban M., “The effect of model generated solar radiation data usage in hybrid (wind–PV) sizing studies”, Energy Conversion and Management, Vol.50, No. 12, pp. 2956-2963. 2009.
[27] Ekren B.Y., Ekren O. “Simulation based size optimization of a PV/wind hybrid energy conversion system with battery storage under various load and auxiliary energy conditions”, Applied Energy, Vol.86, No. 9, pp.1387-1394. 2009.
[28] Dehghan S., Saboori H., Parizad A., Kiani B., “Optimal sizing of a hydrogen-based wind/pv plant considering reliability indices”, IEEE Trans. Energy Conversion, Vol. 20, pp. 654-660. 2009.
[29] Kennedy, J., Eberhart, R., “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks IV. Perth, WA, pp. 1942-1948. 1995.
[30] Karaboga D., Basturk B., “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm”, Journal of Global Optimization, November 2007, Vol.39, No.3, pp. 459-471. 2007.
[31] Dorigo M., Di Caro, G. “Ant colony optimization: A new meta-heuristic”, Proceedings of the 1999 Congress on Evolutionary Computation (CEC’99), Piscataway, NJ, pp. 1470-1477.
[32] Neshat M., Sepidnam G., Sargolzaei M., Najaran Toosi A. “Artificial fish swarm algorithm: a survey of the stateof-the-art, hybridization, combinatorial and indicative applications”, Artificial Intelligence Review, Vol. 11, pp. 123-165. 2012
[33] Bozorg Hadad O., Afshar A., Marino M. A. “Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization”, Water Resources Management, Vol.20, pp.661-680. 2006.
[34] Yan X., Yang W., Shi H., “A group search optimization based on improved small world and its application on neural network training in ammonia synthesis”, Neurocomputing, Vol.97, pp.94-107. 2012.
[35] He S., Wu Q. H., Saunders J. R. “Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior”, IEEE Transactions on Evolutionary Computation, Vol.13, No. 5, pp. 973-990. 2009.
Published
2023-07-12
How to Cite
Nabizadeh, F., & Bakhshinejad, A. (2023). Intelligent frequency control in AC microgrids using fuzzy logic. Majlesi Journal of Mechatronic Systems, 11(4), 7-16. Retrieved from https://ms.majlesi.info/index.php/ms/article/view/518
Section
Articles
