Maximum Power Point Tracking of Wind Energy Conversion System using Fuzzy- Cuckoo Optimization Algorithm Strategy

الموضوعات : مهندسی هوشمند برق

Mohammad Sarvi ¹ (Electrical Engineering Department, Imam Khomeini International University, Qazvin, Iran.)
Mohammad Parpaei ² (Electrical Engineering Department, Imam Khomeini International University, Qazvin, Iran.)

تاريخ الإرسال : 26 الأربعاء , جمادى الثانية, 1436 تاريخ التأكيد : 26 الأربعاء , جمادى الثانية, 1436 تاريخ الإصدار : 25 الأحد , شوال, 1434

الکلمات المفتاحية: Intelligent controller, Metaheuristic optimization approach, Wind energy conversion systems,

ملخص المقالة :

Nowadays the position of the renewable energy is so important because of the environment pollution and the limitation of fossil fuels in the world. Energy can be generated more and more by the renewable sources, but the fossil fuels are non-renewable. One of the most important renewable sources is the wind energy. The wind energy is an appropriate alternative source of fossil fuel. The replacement rate of renewable energy to fossil fuels is rising, although the production cost is higher than fossil fuels. To further reduce cost of wind production, many methods have been proposed. One of the suitable approaches is the maximum power point tracking strategy. In this paper, a new intelligent maximum power point tracker called Fuzzy- Cuckoo strategy for small- scale wind energy conversion systems is proposed. The maximum power point tracker proposed uses measured wind speed to detect the maximum output power and its respective optimal rotational speed. The main contribution of the proposed approach is to exactly track the maximum power point, so the output power fluctuations captured by wind turbine are less than conventional approaches. The simulations are performed in MATAL/SIMULINK software. The superiority of the proposed approach is validated in two situations, low and rapid changes in wind speed. The maximum power point of wind energy conversion systems can be tracked by the proposed approach in any situation. The higher accuracy of the Fuzzy- cuckoo strategy than the conventional trackers is another advantage of the proposed approach.

المصادر:

[1]

Z. M. Dalal, Z. U. Zahid, W. Yu, and J. S. Lai, “Design and Analysis of an MPPT Technique for Smal- Scale Wind Energy Conversion System”, IEEE Transactions on Energy Conversion, Vol.28, No.3, pp.756-767, 2013.

[2]

A. Urtasun, P. Sanchis, I. S. Martín, J. López and L. Marroyo, “Modeling of samll wind turbines based on PMSG with diode bridge for sensorless maximum power tracking”, Renewable energy, Vol.55, pp.138-149, 2013.

[3]

C. Ming, C. H. Chen and C. H. Tu, “Maximum power point tracking- based control algorithm for PMSG wind generation system without mechanical sensors”, Energy conversion and management, Vol.69, pp.58-67, 2013.

[4]

Y. Xia, K. H. Ahmed and B. W. Williams, “A New Maximum Power Point Tracking Technique for Permanent Magnet Synchronous Generation Based Wind Energy Conversion System”, IEEE Transactions on Power Electronics, Vol.26, No.12, pp.3609-3620, 2011.

[5]

A. M. Eltamaly and H. M. Farh, “Maximum power extraction from wind energy system based on fuzzy logic control”, Electrical power systems research, Vol.97, pp.144-150, 2013.

[6]

M. Narayana, G. A. Putrus, M. Jovanovic, P. S. Leung and S. McDonald, ”Generic maximum power point tracking controller for small-scale wind turbines”, Renewable Energy, Vol.44, pp.72-79, 2012.

[7]

C. Y. Lee, P. H. Chen and Y. X. Shen, ”Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach”, Expert Systems with Applications, Vol.38, pp.12058-12065, 2011.

[8]

V. Agarwal, R. K. Aggarwal, P. Patidar and C. Patki, C, “A Novel Scheme for Rapid Tracking of Maximum Power Point in Wind Energy Generation Systems”, IEEE Transactions on Energy Conversion, Vol.25, No.1, pp.228-236, 2010.

[9]

E. Koutroulis, and K. Kalaitzakis, “Design of a Maximum Power Tracking System for Wind- Energy- Conversion Applications”, IEEE Transactions on Industrial Electronics, Vol.53, No.2, pp.486-494, 2006.

[10]

M. E. Haque, M. Negnevitsky and K. M. Muttaqi, “ A Novel Control Strategy for a Variable-Speed Wind Turbine With a Permanent-Magnet Synchronous Generator”, IEEE Transactions on Industry Applications, Vol.46, pp.331-339, 2010.

[11]

A. Meharra, M. Tioursi, M. Hatti and S. A. Boudghene, “A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system”, Expert Systems with Applications, Vol.38, pp.7659-7664, 2011.

[12]

W. M. Lin and C. M. Hong, ”Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system”, Energy, Vol.35, pp.2440-2447, 2010.

[3]

C. H. Chen, C. M. Hong and F. S. Cheng, ”Intelligent speed sensorless maximum power point tracking control for wind generation system”, Electrical Power and Energy Systems, Vol.42, pp.399-407, 2012.

[14]

I. Kortabarria, J. Andreu, I. M. Alegria and J. Jimenez, “A novel adaptative maximum power point tracking algorithm for small wind turbines”, Renewable Energy, Vol.63, pp.785-796, 2014.

[15]

T. Senjyu, Y. Ochi, Y. Kikunaga, M. Tokudome and A. Yona, “Sensor-less maximum power point tracking control for wind generation system with squirrel cage induction generator”, Renewable Energy, Vol. 34, pp.994-999, 2009.

[16]

T. Senjyu, S. Tamaki, E. Muhando, N. Urasaki, H. Kinijo, T. Funabashi, F. Hideki and H. Sekine,” Wind velocity and rotor position sensorless maximum power point tracking control for wind generation system”, Renewable Energy,Vol.31, pp.1764-1775, 2006.

[17]

R. Rajabioum, “Cuckoo Optimization Algorithm”, Applied Soft Computing, Vol.11, pp.5508-5518, 2011.