JNT University Kakinada, India
BibTex Citation Data :
@article{IJRED12958, author = {Santhan Cherukuri and Srinivasa Rayapudi}, title = {Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition}, journal = {International Journal of Renewable Energy Development}, volume = {6}, number = {3}, year = {2017}, keywords = {Enhanced Grey Wolf Optimizer, Maximum power point tracking, Partial shaded condition, PV system, Single diode model.}, abstract = { Partial shading condition is one of the adverse phenomena which effects the power output of photovoltaic (PV) systems due to inaccurate tracking of global maximum power point. Conventional Maximum Power Point Tracking (MPPT) techniques like Perturb and Observe, Incremental Conductance and Hill Climbing can track the maximum power point effectively under uniform shaded condition, but fails under partial shaded condition. An attractive solution under partial shaded condition is application of meta-heuristic algorithms to operate at global maximum power point. Hence in this paper, an Enhanced Grey Wolf Optimizer (EGWO) based maximum power point tracking algorithm is proposed to track the global maximum power point of PV system under partial shading condition. A Mathematical model of PV system is developed under partial shaded condition using single diode model and EGWO is applied to track global maximum power point. The proposed method is programmed in MATLAB environment and simulations are carried out on 4S and 2S2P PV configurations for dynamically changing shading patterns. The results of the proposed method are analyzed and compared with GWO and PSO algorithms. It is observed that proposed method is effective in tracking global maximum power point with more accuracy in less computation time compared to other methods. Article History : Received June 12nd 2017; Received in revised form August 13rd 2017; Accepted August 15 th 2017; Available online How to Cite This Article : Kumar, C.H.S and Rao, R.S. (2017 Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition. Int. Journal of Renewable Energy Development, 6(3), 203-212. https://doi.org/10.14710/ijred.6.3.203-212 }, pages = {203--212} doi = {10.14710/ijred.6.3.203-212}, url = {https://ejournal.undip.ac.id/index.php/ijred/article/view/12958} }
Refworks Citation Data :
Partial shading condition is one of the adverse phenomena which effects the power output of photovoltaic (PV) systems due to inaccurate tracking of global maximum power point. Conventional Maximum Power Point Tracking (MPPT) techniques like Perturb and Observe, Incremental Conductance and Hill Climbing can track the maximum power point effectively under uniform shaded condition, but fails under partial shaded condition. An attractive solution under partial shaded condition is application of meta-heuristic algorithms to operate at global maximum power point. Hence in this paper, an Enhanced Grey Wolf Optimizer (EGWO) based maximum power point tracking algorithm is proposed to track the global maximum power point of PV system under partial shading condition. A Mathematical model of PV system is developed under partial shaded condition using single diode model and EGWO is applied to track global maximum power point. The proposed method is programmed in MATLAB environment and simulations are carried out on 4S and 2S2P PV configurations for dynamically changing shading patterns. The results of the proposed method are analyzed and compared with GWO and PSO algorithms. It is observed that proposed method is effective in tracking global maximum power point with more accuracy in less computation time compared to other methods.
Article History: Received June 12nd 2017; Received in revised form August 13rd 2017; Accepted August 15th 2017; Available online
How to Cite This Article: Kumar, C.H.S and Rao, R.S. (2017 Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition. Int. Journal of Renewable Energy Development, 6(3), 203-212.
https://doi.org/10.14710/ijred.6.3.203-212
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