Journal Press India^®

Author Details ( * ) denotes Corresponding author

It is vital to determine or extract the highest obtainable wind energy at any wind speed because of the natures unforeseeable wind limitations. Therefore, a smart controller that can monitor the extreme pitch irrespective of wind velocity. This article concentrates on several previous and present techniques for achieving maximum wind energy capacity. The maximum power point tracking (MMPT) solution is numerous, but the issues lends itself to their efficient selection and requires expert understanding about every method to choose an efficient MPP method, as such method alone presents some benefits and drawbacks. Different MPP techniques are discussed and compered in terms of convergence moment, effectiveness, training and implementation complexity and they are characterized based on continual wind speed as well as variable wind speed.

Keywords

Wind Energy Conversion System, Optimum Relation Based, Growing Neural Gas, Perturb, Observe method, Particle Swarm Optimization.

1. KSM Raza, H Goto, HJ Guo, O Ichinokura. A novel algorithm for fast and efficient speed-Sensorless maximum power point tracking in wind energy conversion system, IEEE Transactions on Industrial Electronics, vol. 58, pp. 29-39, 2011.


2. SN Bhadra, D Kastha, S Banerjee. Wind Electrical Systems, Oxford University Press, USA.


3. D Sera, R Teodorescu, J Hantschel, M Knoll. Optimized maximum power point tracker for fast-changing environmental conditions, IEEE Trans. Ind. Electron., 55(7), 2008,  2629–2637.


4. AW Manyonge, RM Ochieng, FN Onyango, JM Shichikha. Mathematical modelling of wind turbine in wind energy conversion system: power coefficient analysis, Applied Mathematical Sciences, 6(91), 2012, 4527-4536.


5. P Verma, B Chakri Study of grid connected induction generator for wind power applications, National institute of technology Rourkela, 2012.


6. S Ichikawa, M Tomita, S Doki, S Okuma. Sensorless control of permanent-magnet synchronous motors using online parameter identification based on system identification theory, IEEE Trans. Ind. Electron., 53(2), 2006, 363–372.


7. G Hou, Z Jiang, Y Yang, J Zhang. Variable universe fuzzy controllers used in MPPT based on DFIG wind energy conversion systems,  IEEE Conference on Power and Energy, August 2016.


8. MA Abuullah, AHM. Yatim, CW Tan. An online optimum-relation-based maximum power point tracking algorithm for wind energy conversion systems, IEEE International Conference on Power Engineering, October 2014.


9. M Cirrincione, M Pucci, G Vitale. Growing Neural Gas (GNG)-Based Maximum Power Point Tracking for High-Performance Wind Generator with an Induction Machine, IEEE Transactions on Industry Applications, 47(2), 2011.


10. RM Linus, P Damodharan. Maximum power point tracking method    using a modified perturb and observe algorithm for grid connected wind energy conversion systems, IET Renewable Power Generation, 9(6), 2015, 682-689.


11. N Fernia, G Petrone, G Spagnuolo, M Vitelli. Optimization of perturb and observe maximum power point tracking method, IEEE Trans. Power Electron., 20(4), 2005, 963–973.


12. MA Abdullah, AHM Yatim, CW Tan, AS Samosir. Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for Wind Energy Conversion System, IEEE Conference on Power and Energy, December, 2012.