Enhanced Grey Wolf Optimizer based MPPT Algorithm of PV system under Partial Shaded Condition

DOI: https://doi.org/10.14710/ijred.0.X.xxx-xxx

Article Info
Submitted: 05-01-2017
Section: Articles
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.


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

JNNSM, India. (2016) http://www.mnre.gov.in.

Silvestre, S., Boronat, A. & Chouder, A. (2009) Study of bypass diodes configuration on PV modules. Applied Energy, 86, 1632-1640.

Ankit, Gupta., Yogesh, K, Chauhan. & Rupendra, Kumar, Pachauri. (2016) A comparative investigation of maximum power point tracking methods for solar PV system. Solar energy, 136, 236-253.

Deepak, Verma., Savita, Nema., A, M, Shandilya. & Soubhagya, K, Dash. (2016) Maximum power point tracking (MPPT) techniques:Recapitulation in solar photovoltaic systems. Renewable and sustainable energy reviews, 54, 1018-1034.

Makbul, A, M, Ramli., Ssennoga, Twaha., Kashif, Ishaque. & Yusuf, A, Al-Turki. (2017) A review on maximum power point tracking for photovoltaic systems with and without shading conditions. Renewable and sustainable energy reviews, 67, 144-159.

S, Saravanan. & Ramesh, Babu, N. (2016) Maximum power point tracking algorithms for photovoltaic system – A review. Renewable and sustainable energy reviews, 57, 192-204.

Zainal, Salam., Jubaer, Ahmed. & Beny, S.Merugu. (2013) The application of soft computing methods for MPPT of PV system: A technological and status review. Applied Energy, 107, 135-148.

Ishaque, K., Salam, Z., Amjad, M. & 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.

Ishaque, K., Salam, Z., Shamsudin, A. & 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.

Liu, Y.H., Huang, S.C., Huang, J.W. & 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.

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.

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.

Ahmed, J. & Salam, Z. (2014) A maximum power point tracking (MPPT) for PV system using Cuckoo search with Partial shading capability. Appl Energy, 119, 118-130.

Sundareswaran, K., Sankar, P. & 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.

Satyajit, Mohanty., Bidyadhar, Subudhi. & Pravat, Kumar.Ray. (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.

Santhan, Kumar, CH. & Srinivasa, Rao, R. (2016) A novel global MPP tracking of PV system under partial shaded condition based on WOA. International journal of renewable energy development, 5, 225-232.

A, Rezaee Jordehi. (2016) Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches. Renewable and sustainable energy reviews, 65, 1127-1138.

Mirjalili, S., Mirjalili, S, M. & Lewis, A. (2014) Grey wolf optimizer. Advances in Engg Software, 69, 46-61.

Giuseppina, Ciulla., Valerio, Lo, Brano., Vincenzo, Di, Dio. & Giovanni, Cipriani. (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.

Sangram, Bana. & R, P, Saini. (2016) A mathematical modeling framework to evaluate the performance of single diode and double diode based SPV systems. Energy reports, 2, 171-187.