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Optimal Control of Active and Reactive Powers in Wind Energy Conversion Systems Using Particle Swarm Optimization and Adaptive Sliding Mode Control


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DOI: https://doi.org/10.15866/ireaco.v11i5.15098

Abstract


The strong nonlinearities present in wind energy conversion systems (WECSs) increase the complexity of the control algorithms and they require parameters optimization to reach the desired performances. Since parameters optimization is difficult with exact methods, the success of these controllers highly depends on the designer skills and experience. This paper shows how to overcome limitations of exact methods and avoid empirical parameters tuning by using new paradigm inspired by nature. A new adaptive sliding mode controller (ASMC) is presented with variable sliding surface optimized by PSO which is a popular algorithm among nature-inspired approaches. This new controller is used for active and reactive powers flow control in the most popular WECS topology based on Doubly-Fed Induction Generator (DFIG). Chattering amplitude is reduced by more than half, response time is significantly improved and robustness against parameters variations is also enhanced by up to 50%, compared to unoptimized ASMC. The proposed control strategy is approved by simulation using Matlab/Simulink software.
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Keywords


Active and Reactive Power Control; Nature-Inspired Algorithms; PSO; Sliding Mode Control; WECS

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References


R. Ribeiro and F. Enembreck, A sociologically inspired heuristic for optimization algorithms: A case study on ant systems, Expert Systems with Applications, vol. 40, pp. 1814-1826, 2013.
http://dx.doi.org/10.1016/j.eswa.2012.09.020

R. A. Mahale and S. Chavan, A Survey: Evolutionary and Swarm Based Bio-Inspired Optimization Algorithms, International Journal of Scientific and Research Publications, vol. 2, no. 12, 2012.

S. Kirkpatrick, C. J. Gelatt and M. Vecchi, Optimization by Simulated Annealing, Science, vol. 220, no. 4598, pp. 671-680, 1983.
http://dx.doi.org/10.1126/science.220.4598.671

A. Alqudah, A. Malkawi and A. Alwadie, Adaptive Control of DC-DC Converter Using Simulated Annealing Optimization Method, Journal of Signal and Information Processing, 2014, 5, 198-207, vol. 5, pp. 198-207, 2014.
http://dx.doi.org/10.4236/jsip.2014.54021

G. Zhang, Quantum-inspired evolutionary algorithms: a survey and empirical study, Journal of Heuristics, Springer, vol. 17, pp. 303-351, 2011.
http://dx.doi.org/10.1007/s10732-010-9136-0

L. R. da Silveira, R. Tanscheit and M. M. Vellasco, Quantum inspired evolutionary algorithm for ordering problems, Expert Systems with Applications, vol. 67, pp. 71-83, January 2017.
http://dx.doi.org/10.1016/j.eswa.2016.08.067

E. Rashedi, H. Nezamabadi-pour and S. Saryazdi, GSA: A Gravitational Search Algorithm, Information Sciences, vol. 179, no. 13, pp. 2232-2248, June 2009.
http://dx.doi.org/10.1016/j.ins.2009.03.004

N. Siddique and H. Adeli, Nature-Inspired Chemical Reaction Optimisation Algorithms, Cognitive Computation, vol. 9, no. 4, pp. 411-422, 2017.
http://dx.doi.org/10.1007/s12559-017-9485-1

A. Y. S. Lam and V. O. K. Li, Chemical-Reaction-Inspired Metaheuristic for Optimization, IEEE Transactions On Evolutionary Computation, vol. 14, no. 3, pp. 381-399, 2010.
http://dx.doi.org/10.1109/tevc.2009.2033580

E. Bonabeau, M. Dorigo and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, New York: Oxford University Press, 1999.
http://dx.doi.org/10.1086/393972

X. Lei LI, Z. Jiang SHAO and J. Xin QIAN, An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm, Systems Engineering - Theory & Practice, vol. 22, no. 11, pp. 32-38, 2002.

J. Hu, X. Zeng and J. Xiao, Artificial Fish School Algorithm for Function Optimization, in 2010 2nd International Conference on Information Engineering and Computer Science, Wuhan, China , Dec. 2010.
http://dx.doi.org/10.1109/iciecs.2010.5678350

Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis, Grey Wolf Optimizer, Advances in Engineering Software, vol. 69, pp. 46-61, 2014.
http://dx.doi.org/10.1016/j.advengsoft.2013.12.007

H. Rezaei, O. Bozorg-Haddad and X. Chu, Grey Wolf Optimization (GWO) Algorithm, in Advanced Optimization by Nature-Inspired Algorithms, Springer, vol. 720, Singapore, Editors: Bozorg-Haddad, Omid (Ed.), July 2017, pp. 81-91.
http://dx.doi.org/10.1007/978-981-10-5221-7_9

X.-S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Berlin, Heidelberg, Springer, 2010, pp. 65-74.
http://dx.doi.org/10.1007/978-3-642-12538-6_6

I. Fister, X.-S. Yang, S. Fong et Y. Zhuang, «Bat algorithm: Recent advances, in 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, Hungary, Nov. 2014.
http://dx.doi.org/10.1109/cinti.2014.7028669

M. Dorigo and T. Stützle, Ant Colony Optimization, MIT Press, 2004.
http://dx.doi.org/10.1017/s0269888905220386

T. Stützle, M. López-Ibáñez and M. Dorigo, A Concise Overview of Applications of Ant Colony Optimization, Wiley EORMS, 2011.
http://dx.doi.org/10.1002/9780470400531.eorms0001

D. Karaboga, An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report, Erciyes University, 2005.

D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007.
http://dx.doi.org/10.1007/s10898-007-9149-x

L. Huang, Optimization of a new mathematical model for bacterial growth, Food Control, vol. 32, no. 01, pp. 283-288, July 2013.
http://dx.doi.org/10.1016/j.foodcont.2012.11.019

B. Niu et H. Wang, Bacterial Colony Optimization, Discrete Dynamics in Nature and Society, 2012.
http://dx.doi.org/10.1155/2012/698057

L. Zhang and Y. Zhang, The Human-Inspired Algorithm: A Hybrid Nature-Inspired Approach to Optimizing Continuous Functions with Constraints, Journal of Computational Intelligence and Electronic Systems, vol. 2, no. 1, pp. 80-87, 2013.
http://dx.doi.org/10.1166/jcies.2013.1039

A. Ahmadi-Javid, Anarchic Society Optimization: A human-inspired method, in 2011 IEEE Congress on Evolutionary Computation (CEC), New Orleans, LA, USA, 2011.
http://dx.doi.org/10.1109/cec.2011.5949940

Z. Geem, J. Kim and G. Loganathan, A New Heuristic Optimization Algorithm: Harmony Search, Simulation, vol. 76, no. 2, pp. 60-68, 2001.
http://dx.doi.org/10.1177/003754970107600201

C. Grosan, A. Abraham et M. Chis, Swarm Intelligence in Data Mining, Swarm Intelligence in Data Mining. Studies in Computational Intelligence, vol 34, Berlin, Heidelberg, Springer, 2006, pp. 1-20.
http://dx.doi.org/10.1007/978-3-540-34956-3_1

M. Taleb and M. Cherkaoui, Conventional and Adaptive Sliding Mode for Active and Reactive Power Control of Doubly Fed Induction Generator Wind Turbines, in 4ème Conférence Internationale des Energies Renouvelables (CIER-2016), Hammamat-Tunis, 2016.

J. Kennedy and Eberhart RC, Particle Swarm Optimization, Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942-1948, 1995.
http://dx.doi.org/10.1109/icnn.1995.488968

V. Utkin, Variable structure systems with sliding mode, IEEE. TAC, Vols. AC-22, no. 2, pp. 212-222, 1977.
http://dx.doi.org/10.1109/tac.1977.1101446

X. Yu and M. Önder Efe, Recent Advances in Sliding Modes: From Control to Intelligent Mechatronics, Springer, Oct 2016.
http://dx.doi.org/10.1007/978-3-319-18290-2

N. Khemiri, Adel Khelder and M.F. Mimouni, Wind Energy Conversion System using DFIG Controlled by Backstepping and Sliding Mode Strategies, IJRER, vol. 2, no. 3, 2012.

M. Taleb and M. Cherkaoui, Active and Reactive Power Robust Control of Doubly Fed Induction Generator Wind Turbine to Satisfy New Grid Codes, in Advances in Intelligent Systems and Computing 565 (Springer), AECIA'2016, Marrakech, Morocco, 2018.
http://dx.doi.org/10.1007/978-3-319-60834-1_12

G. Tarchala, Influence of the sign function approximation form on performance of the sliding-mode speed observer for induction motor drive, in 2011 IEEE International Symposium on Industrial Electronics, Gdańsk, Poland, 2011.
http://dx.doi.org/10.1109/isie.2011.5984364

V. I. Utkin and A. S. Poznyak, Adaptive Sliding Mode Control, in Advances in Sliding Mode Control, Springer, 2013, pp. 21-53.
http://dx.doi.org/10.1007/978-3-642-36986-5_2

A. Mehta and B. Bandyopadhyay, Frequency-Shaped and Observer-Based Discrete-time Sliding Mode Control, in Springer Briefs in Applied Sciences and Technology, Springer, 2015, pp. 10-25.
http://dx.doi.org/10.1007/978-81-322-2238-5

Y.-J. Huang, T.-C. Kuo and S.-H. Chang, Adaptive Sliding-Mode Control for Nonlinear Systems with Uncertain Parameters, IEEE Transactions on Systems, Man and Cybernetics – Part B: Cybernetics, vol. 38, no. 2, p. 534–539, 2008.
http://dx.doi.org/10.1109/tsmcb.2007.910741

G. Bartolini, A. Levant, F. Plestan et M. Taleb, Adaptation sliding mode, IMA Journal of mathematical control and information, 30 (3), pp. 285-300, 2013.
http://dx.doi.org/10.1093/imamci/dns019

Del Pizzo, A., Di Noia, L.P., Meo, S., Super Twisting Sliding mode control of Smart-Inverters grid-connected for PV applications, (2017) 2017 6th International Conference on Renewable Energy Research and Applications, ICRERA 2017, 2017-January, pp. 793-796.
http://dx.doi.org/10.1109/icrera.2017.8191168

Di Noia, L.P., Del Pizzo, A., Meo, S., Reduced-order averaged model and non-linear control of a dual active bridge Dc-Dc Converter for aerospace applications, (2017) International Review of Aerospace Engineering (IREASE), 10 (5), pp. 259-266.
http://dx.doi.org/10.15866/irease.v10i5.13818

Meo, S., Sorrentino, V., Discrete–time integral sliding mode control with disturbances compensation and reduced chattering for PV grid–connected inverter, (2015) Journal of Electrical Engineering, 66 (2), pp. 61-69.
http://dx.doi.org/10.1515/jee-2015-0010

Meo, S., Zohoori, A., Vahedi, A., Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach, (2016) Energy Conversion and Management, 110, pp. 230-239.
http://dx.doi.org/10.1016/j.enconman.2015.11.062


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