Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition

DOI: https://doi.org/10.14710/ijred.6.3.203-212

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Article Info
Published: 06-11-2017
Section: Original Research Article
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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.



Enhanced Grey Wolf Optimizer, Maximum power point tracking, Partial shaded condition, PV system, Single diode model.

  1. Santhan Kumar Cherukuri 
    JNT University Kakinada, India
  2. Srinivasa Rao Rayapudi 
    JNT University Kakinada, India
  1. Ahmed, J. and Salam, Z. (2014) A maximum power point tracking (MPPT) for PV system using Cuckoo search with Partial shading capability. Appl Energy, 119, 118-130.
  2. Ciulla.G., Brano, V.L., Dio, V.D. and Cipriani, G. (2014) A comparison of different one-diode models for the representation of I–V characteristic of a PV cell. Renewable and sustainable energy reviews, 32, 684-696.
  3. Gupta, A., Chauhan, Y.K and Rupendra, K.P. (2016) A comparative investigation of maximum power point tracking methods for solar PV system. Solar energy, 136, 236-253.
  4. Ishaque, K., Salam, Z., Amjad, M. and Mekhilef, S. (2012) An improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady state oscillations. IEEE Trans Power Electron, 27(8), 3627-3637.
  5. Ishaque, K., Salam, Z., Shamsudin, A. and Amjad, M. (2012) A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm. Applied Energy, 99, 414-422.
  6. Jiang, L.L., Maskell, D.L. & Patra, J.C. (2013) A novel ant colony optimization based maximum power point tracking for photovoltaic systems under partially shaded condition. Energy & Buildings, 58, 227-236.
  7. JNNSM, India. (2016) http://www.mnre.gov.in.
  8. Jordehi, A.R. (2016) Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches. Renewable and sustainable energy reviews, 65, 1127-1138.
  9. Kumar, C., & Rao, R. (2016). (2016). A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm. International Journal of Renewable Energy Development, 5(3), 225-232.
  10. Liu, Y.H., Huang, S.C., Huang, J.W. and Liang, W.C. (2012) A particle swarm optimization based maximum power point tracking algorithm for PV systems operating under partially shaded conditions. IEEE Trans Energy Conv, 27(4), 1027-1035.
  11. Mirjalili, S., Mirjalili, S, M. & Lewis, A. (2014) Grey wolf optimizer. Advances in Engg Software, 69, 46-61.
  12. Ramli., M.A.M., Twaha,S., Ishaque, K. and Al-Turki, Y.A.. (2017) A review on maximum power point tracking for photovoltaic systems with and without shading conditions. Renewable and sustainable energy reviews, 67, 144-159.
  13. Sangram, B. and Saini, R, P. (2016) A mathematical modeling framework to evaluate the performance of single diode and double diode based SPV systems. Energy reports, 2, 171-187.
  14. Saravanan, S. and Ramesh, B., N. (2016) Maximum power point tracking algorithms for photovoltaic system – A review. Renewable and sustainable energy reviews, 57, 192-204.
  15. Satyajit, M., Bidyadhar, S. and Pravat, K.R. (2016) A new MPPT design using grey wolf optimizer technique for photovoltaic system under partial shading conditions. IEEE Trans on sustainable energy, 7(1), 181-188.
  16. Silvestre, S., Boronat, A. and Chouder, A. (2009) Study of bypass diodes configuration on PV modules. Applied Energy, 86, 1632-1640.
  17. Sundareswaran, K., Sankar, P. and Sankaran, P. (2014) MPPT of PV systems under partial shaded Conditions through a colony of flashing fireflies. IEEE Trans Ener Conv, 29(2), 463-472.
  18. Sundareswaran, K., Sankar, P., Nayak, P.S.R., Simon, S.P. & Palani, S. (2015) Enhanced energy output from a PV system under partial shaded conditions through Artificial bee colony. IEEE Trans Sustainable Ener, 6(1), 198-209.
  19. Verma, D., Nema.S., Shandilya, A., M., and Dash, S.K. (2016) Maximum power point tracking (MPPT) techniques:Recapitulation in solar photovoltaic systems. Renewable and sustainable energy reviews, 54, 1018-1034.
  20. Zainal, S., Jubaer, A. and Beny, S.M. (2013) The application of soft computing methods for MPPT of PV system: A technological and status review. Applied Energy, 107, 135-148.