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Enhancement of vibration based piezoelectric energy harvester using hybrid optimization techniques

  • P. MangaiyarkarasiEmail author
  • P. Lakshmi
  • V. Sasrika
Technical Paper
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Abstract

MEMS unimorph piezoelectric vibration energy harvester is designed and optimized based on Hybrid Optimization techniques [genetic algorithm (GA), bat algorithm (BA), grey wolf optimization (GWO), hybrid bat algorithm tuned genetic algorithm (HBAGA) and hybrid grey wolf optimizer tuned genetic algorithm (HGWOGA)] for performance improvement. In this research work, unimorph piezoelectric vibration energy harvester is modeled using analytical equations, which is derived to determine the amount of voltage and power, harvested from the piezoelectric energy harvester. These derived analytical equations are used as the fitness function of Hybrid optimization techniques, which is a design optimization technique to improve the amount of power and voltage harvested from the unimorph piezoelectric energy harvester, by optimizing the parameters and a comparison is made within these techniques. The effects of geometry optimization on natural frequency and stress are also studied. The un-optimized results are compared with the optimized results (GA, BA, GWO, HBAGA and HGWOGA) and the results obtained, proves that, the optimized results are more efficient in all aspects, which has significantly improved the efficiency of the piezoelectric harvester to a larger extent. Finally, a comparison is made within the optimization techniques and HGWOGA has proved to be more efficient than HBAGA, GWO, BA, GA and this work of MEMS piezoelectric vibration energy harvester is simulated using the MATLAB software.

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Notes

References

  1. Abbass HA (2001) MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach. In: IEEE-CEC Seoul, pp 207–214Google Scholar
  2. Athavale AA (2015) An analytical model for piezoelectric unimorph cantilever subjected to an impulse load. M.S. thesis, Graduate School-New Brunswick Rutgers, the State Univ. of New Jersey, USAGoogle Scholar
  3. Balguvhar S, Bhalla S (2018) Green Energy harvesting using piezoelectric materials from bridge vibrations. In: IEEE-ICGEA, Singapore, pp 134–137Google Scholar
  4. Du M, Lei X, Wu Z et al (2014) A simplified glowworm swarm optimization algorithm. In: IEEE-CEC, Beijing, pp 2861–2868Google Scholar
  5. Duque FG, deOliveira LW, deOliveira EJ, Marcato ALM, Silva IC et al (2015) Allocation of capacitor banks in distribution systems through a modified monkey search optimization technique. Int J Elec Power 73:420–432CrossRefGoogle Scholar
  6. Eggborn T (2003) Analytical models to predict power harvesting with piezoelectric materials. M.S. thesis, Viginia Polytech. Inst. State Univ., Blacksburg, VA, USAGoogle Scholar
  7. Flora EE, Sunithamani S, Lakshmi P et al (2013) Simulation of MEMS Energy Harvester with Different Geometries and Cross sections. In: IEEE-ICT, India, pp 20–123Google Scholar
  8. Guha D, Roy PK, Banerjee S et al (2016) Load frequency control of interconnected power system using grey wolf optimization. Swarm Evol Comput 27:97–115CrossRefGoogle Scholar
  9. Hedayatzadeh R, Akhavan FS, Keshtgari M, Akbari R, Ziarati K et al (2010) Termite colony optimization: A novel approach for optimizing continuous problems. In: IEEE-ICEC, Isfahan, pp 553–558Google Scholar
  10. Hibbeler RC (1997) Mechanics of materials. Educational Technology Publications, Englewood CliffsGoogle Scholar
  11. Islam T, Islam ME, Ruhin MR et al (2018) An analysis of foraging and echolocation behavior of swarm intelligence algorithms in optimization: ACO, BCO and BA. Int J Intell Sci 8(1):1–27CrossRefGoogle Scholar
  12. Jayarathne WM, Nimansala WAT, Adikary SU et al (2018) Development of a vibration energy harvesting device using piezoelectric sensors. In: IEEE-MERCon, Moratuwa, pp 197–202Google Scholar
  13. Kalaivani R, Lakshmi P, Sudhagar K et al (2014) Hybrid (DEBBO) fuzzy logic controller for quarter car model: DEBBOFLC for quarter car model. In: IEEE-UKACC, UK, pp 301–306Google Scholar
  14. Kong X, Chen Y, Xie W, Wu X et al (2012) A novel paddy field algorithm based on pattern search method. In: IEEE-ICIA, Shenyang, pp 686–690Google Scholar
  15. Lakshmi P, Khan MA (2000) Stability enhancement of a multimachine power system using fuzzy logic based power system stabilizer tuned through genetic algorithm. Int J Elec Power 22:137–145CrossRefGoogle Scholar
  16. Liang H, Liu Y, Shen Y et al (2018) A hybrid bat algorithm for economic dispatch with random wind power. IEEE Trans Power Syst 33(5):5052–5061CrossRefGoogle Scholar
  17. Marinakis Y, Marinaki M (2014) A bumble bees mating optimization algorithm for the open vehicle routing problem. Swarm Evol Comput 15:80–94CrossRefzbMATHGoogle Scholar
  18. Meitzler AH (1985) Additional comments on IEEE standard on piezoelectricity 176-1978. IEEE Trans Sonics Ultrason 32(4):610–611CrossRefGoogle Scholar
  19. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249CrossRefGoogle Scholar
  20. Mirjalili S, Mirjalili SM, Lewis A et al (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  21. Moura S (2006) Design of an energy harvester. ME128 Project 3c, Dept. of Mech.Engg.,Univ. of California, BerkeleyGoogle Scholar
  22. Nabavi S, Zhang L (2017) Design and optimization of piezoelectric energy harvesters based on genetic algorithm. IEEE Sensors J 17(22):7372–7382CrossRefGoogle Scholar
  23. Saleh MH, Saad SZ (2016) Artificial immune system based PID tuning for DC servo speed control. Int J Comput Appl 155(2):23–26Google Scholar
  24. Santosa B, Ningrum MK (2009) Cat swarm optimization for clustering. In: IEEE-SoCPaR, Malacca, pp 54–59Google Scholar
  25. Song H (2017) Ultra-low resonant piezoelectric MEMS energy harvester with high power density. J Microelectromech Syst 26(6):1226–1234CrossRefGoogle Scholar
  26. Sunithamani S, Lakshmi P (2015) Simulation study on performance of MEMS piezoelectric energy harvester with optimized substrate to piezoelectric thickness ratio. Microsyst Technol 21(4):733–738CrossRefGoogle Scholar
  27. Sunithamani S, Lakshmi P (2017) Experimental study and analysis of unimorph piezoelectric energy harvester with different substrate thickness and different proof mass shapes. Microsyst Technol 23(7):2421–2430CrossRefGoogle Scholar
  28. Sunithamani S, Lakshmi P, Eba FE et al (2012) Simulation and optimization of MEMS piezoelectric energy harvester with a non-traditional geometry, COMSOL conference in BangaloreGoogle Scholar
  29. Sunithamani S, Lakshmi P, Eba FE et al (2014) PZT length optimization of MEMS piezoelectric energy harvester with a non-traditional cross section: simulation study. Microsyst Technol 20(12):2165–2171CrossRefGoogle Scholar
  30. Sunithamani S, Lakshmi P, Eba FE et al (2017) Design of piezoelectric energy harvester with controlled and regulated output voltage: simulation study. WSEAS Trans Power Syst 12:63–72Google Scholar
  31. Tawhid MA, Ahmed FA (2017) A hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function. Memetic Comp 9(4):347–359CrossRefGoogle Scholar
  32. Todaro MT (2018) Biocompatible, flexible, and compliant energy harvesters based on piezoelectric thin films. IEEE Trans Nanotechnol 17(2):220–230.sCrossRefGoogle Scholar
  33. Tupia M, Cueva R, Guanira M et al (2018) A bat algorithm for job scheduling in ceramics production lines. In: IEEE-ICTUS, Dubai, pp 266–270Google Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringAnna UniversityChennaiIndia

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