Abstract
The wind energy characteristics at the site not only indicate the amount of energy obtained from wind energy, but it also depends on the control strategy implemented on the wind energy conversion system. This study proposes an application of Ant Lion Optimizer (ALO) for tuning the classical (PI) controller parameters for the permanent magnet synchronous generator driven by wind energy system, to such an extent that the most maximum power point tracking can be acknowledged along with an improved fault ride-through capability. The optimized PI-controller parameters based ALO improving the low voltage ride through (LVRT) performance and the maximum power point tracking. ALO is implemented to the classical PI-controller on the machine side converter system, and grid side converter, for maximum power extraction from PMSG and enhancing LVRT. Four cases were considered in this paper, to examine the feasibility of the proposed ALO-PI controller. The suggested cases are system running at normal condition, a step-change in wind speed, wind speed variation at normal grid condition, and three-phase fault. A significant improvement in the system dynamic performance when applying the ALO-PI controller is compared with classical PI-controller.
Similar content being viewed by others
References
Liao S, Yao W, Han X, Wen J, Cheng S (2017) Chronological operation simulation framework for regional power system under high penetration of renewable energy using meteorological data. Appl Energy 203:816–828
Maleki A, Pourfayaz F (2015) Optimal sizing of autonomous hybrid photovoltaic/wind/battery power system with LPSP technology by using evolutionary algorithms. Sol Energy 115:471–483
Xie D, Lu Y, Sun J, Gu C (2017) Small signal stability analysis for different types of PMSGs connected to the grid. Renew Energy 106:149–164
Li S, Zhang K, Li J, Liu C (2016) On the rejection of internal and external disturbances in a wind energy conversion system with direct-driven PMSG. ISA Trans 61:95–103
Marei MI, Mohy A, El-Sattar AA (2015) An integrated control system for sparse matrix converter interfacing PMSG with the grid. Int J Electr Power Energy Syst 73:340–349
Shehata EG (2017) A comparative study of current control schemes for a direct-driven PMSG wind energy generation system. Electr Power Syst Res 143:197–205
Hong C-M, Chen C-H, Tu C-S (2013) Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors. Energy Convers Manag 69:58–67
Sanchez AG, Molina MG, Lede AMR (2012) Dynamic model of wind energy conversion systems with PMSG-based variable-speed wind turbines for power system studies. Int J Hydrog Energy 37:10064–10069
Carranza O, Figueres E, Garcerá G, Gonzalez-Medina R (2013) Analysis of the control structure of wind energy generation systems based on a permanent magnet synchronous generator. Appl Energy 103:522–538
Beddar A, Bouzekri H, Babes B, Afghoul H (2016) Experimental enhancement of fuzzy fractional order PI + I controller of grid connected variable speed wind energy conversion system. Energy Convers Manag 123:569–580
Errami Y, Ouassaid M, Maaroufi M (2015) A performance comparison of a nonlinear and a linear control for grid connected PMSG wind energy conversion system. Int J Electr Power Energy Syst 68:180–194
Li S, Haskew TA, Xu L (2010) Conventional and novel control designs for direct driven PMSG wind turbines. Electr Power Syst Res 80:328–338
Zhang Z, Zhao Y, Qiao W, Qu L (2014) A space-vector-modulated sensorless direct-torque control for direct-drive PMSG wind turbines. IEEE Trans Ind Appl 50:2331–2341
Yang B, Yu T, Shu H, Zhang X, Qu K, Jiang L (2018) Democratic joint operations algorithm for optimal power extraction of PMSG based wind energy conversion system. Energy Convers Manag 159:312–326
Errami Y, Ouassaid M, Maaroufi M (2013) Control of a PMSG based wind energy generation system for power maximization and grid fault conditions. Energy Procedia 42:220–229
Pardalos PM, Rebennack S, Pereira MV, Iliadis NA, Pappu V (2013) Handbook of wind power systems. Springer, Berlin
Zhu M, Liu J, Lin Z, Meng H (2016) Mixed H2/H∞ pitch control of wind turbine generator system based on global exact linearization and regional pole placement. Int J Mach Learn Cybern 7:921–930
Kumar D, Chatterjee K (2016) A review of conventional and advanced MPPT algorithms for wind energy systems. Renew Sustain Energy Rev 55:957–970
Sagiraju DKV, Obulesu YP, Choppavarapu SB (2017) Dynamic performance improvement of standalone battery integrated PMSG wind energy system using proportional resonant controller. Eng Sci Technol Int J 20:1353–1365
Badr MA, Atallah AM, Bayoumi MA (2015) Comparison between aggregation techniques for PMSG wind farm. Energy Procedia 74:1162–1173
Xing B, Gao W-J (2014) Innovative computational intelligence: a rough guide to 134 clever algorithms. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=B.+Xing+and+W.-J.+Gao%2C+%22Innovative+computational+intelligence%3A+a+rough+guide+to+134+clever+algorithms%2C%22+2014.+%5B22%5D+H.+M.+Hasanien+and+S.+Muyeen%2C+%22Design+optimization+of+controller+parameters+used+in+variable+speed+wind+energy+conversion+system+by+1*
Hasanien HM, Muyeen S (2012) Design optimization of controller parameters used in variable speed wind energy conversion system by genetic algorithms. IEEE Trans Sustain Energy 3:200–208
Hong C-M, Cheng F-S, Chen C-H (2014) Optimal control for variable-speed wind generation systems using general regression neural network. Int J Electr Power Energy Syst 60:14–23
Kim Y-S, Chung I-Y, Moon S-I (2015) Tuning of the PI controller parameters of a PMSG wind turbine to improve control performance under various wind speeds. Energies 8:1406–1425
Haddin M, Suprajitno A (2016) Pitch angle controller design on the wind turbine with permanent magnet synchronous generator (PMSG) base on Firefly Algorithms (FA). In: 2016 international seminar on application for technology of information and communication (ISemantic), pp 13–17
Mohamed A-AA, Haridy AL, Hemeida A (2019) The Whale Optimization Algorithm based controller for PMSG wind energy generation system. In: 2019 international conference on innovative trends in computer engineering (ITCE), pp 438–443
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lotfy Haridy, A., Ali Mohamed Abdelbasset, AA., Mohamed Hemeida, A. et al. Optimum Controller Design Using the Ant Lion Optimizer for PMSG Driven by Wind Energy. J. Electr. Eng. Technol. 16, 367–380 (2021). https://doi.org/10.1007/s42835-020-00585-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42835-020-00585-5