Improved Grey Wolf assists MPPT Approach for Solar Photovoltaic System under Partially Shaded and Gradually Atmospheric Changing Condition

Dipanwita Debnath, Nirmala Soren, Arun Dev Pandey, Noman Hanif Barbhuiya


Solar energy seems to be most sustainable way which can conserve the environment from negative impacts caused by electrical power generation from fossil fuels. For achieving the utmost power in output, photovoltaic (PV) array should operate on its optimal power point. When PV arrays are subjected to a partial shading state, several local peaks are experienced by power-voltage performance curve of PV system which significantly trims down the efficiency of complete system. So it becomes vital to locate the global peak of PV system for harvesting the upper limit power for better performance. This paper presents an improved grey wolf optimization (IGWO) technique which employs the concepts of parallel grey wolf optimization and opposition-based learning to trace the GMPP under partial shading situation and also figure out the performance for gradually varying with irradiance and temperature for PV system. This IGWO-based MPPT scheme has been implemented in MATLAB simulation environment. The outcome of the simulation is verified by the past-proposed MPPT techniques namely perturb and observe (P&O) and particle swarm optimization (PSO). It has been found from the simulation results that the performance of the suggested IGWO based MPPT design is advanced than the P&O and PSO techniques in terms of tracking speed, steady-state oscillation under partial shading conditions (PSCs) and accuracy.


maximum power point tracking; opposition-based learning; parallel grey wolf optimizer; partial shading conditions (PSCs); photovoltaic (PV)

Full Text:



Bhattacharjee S. and B.J. Saharia. 2014. A comparative study on converter topologies for maximum power point tracking application in photovoltaic generation. Journal of Renewable and Sustainable Energy 6(5): 531401-531421.

Sedaghati F., Nahavandi A., Badamchizadeh M.A., Ghaemi S., and Fallah M.A., 2012. PV maximum power-point tracking by using artificial neural network. Mathematical Problems in Engineering 1–10 (article ID 506709), DOI:

Hiyama T., Kouzuma S., and Imakubo T., 1995. Evaluation of neural network based real time maximum power tracking controller for PV system. IEEE Transactions on Energy Conversion 10(3): 543-548.

Islam M.A., Talukdar A.B., Mohammad N., and Khan P.K.S., 2010. Maximum power point tracking of photovoltaic arrays in Matlab using fuzzy logic controller. In the Proceedings of the IEEE India Conference on Green Energy, Computing and Communication (INDICON), 17-19 December, Kolkata, India, 1-4.

Jones D.C. and R.W. Erickson. 2013. Probabilistic analysis of a generalized perturbs and observe algorithm featuring robust operation in the presence of power curve traps. IEEE Transactions on Power Electronics 28(6): 2912– 2926.

Bahari M.I., Tarassodi P., Naeini Y.M., Khalilabad A.K., and Shirazi P., 2016. Modeling and simulation of hill climbing MPPT algorithm for photovoltaic application. In International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM, IEEE): 1041-1044.

Kollimalla S.K. and Mishra M.K. 2014. Variable perturbation size adaptive P&O MPPT algorithm for sudden changes in irradiance. IEEE Transactions on Sustainable Energy 5(3): 718-728.

Xiao W. and Dunford W.G. 2004. A modified adaptive hill climbing MPPT method for photovoltaic power systems. In 35th Annual IEEE Power Electronics Conference (IEEE Cat. No. 04CH37551): 1957-1963.

Sundareswaran K., Vigneshkumar V., Sankar P., Simon S.P., Nayak P.S.R., and Palani S., 2016. Development of an improved P&O algorithm assisted through a colony of foraging ants for MPPT in PV system. IEEE Transactions on Industrial Information 12(1): 187–200.

Elgendy M.A., Zahawi B., and Atkinson D.J., 2013. Assessment of the incremental conductance maximum power point tracking algorithm. IEEE Transactions on Sustainable Energy 4: 108-117.

Motahhir S., Ghzizal A.E., Sebti S., and Derouich A., 2018. Modeling of photovoltaic system with modified incremental conductance algorithm for fast changes of irradiance. International Journal of Photoenergy: 1-13 (article ID 3286479).

Miyatake M., Veerachary M., Toriumi F., Fujii N., and Ko H., 2011. Maximum power point tracking of multiple photovoltaic arrays: A PSO approach. IEEE Transactions Aerospace Electronic Systems 47(1): 367–380.

Ishaque K., Salam Z., Amjad M., and Mekhilef S., 2012. An improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillation. IEEE Transactions on Power Electronics 27(8): 3627–3638.

Ishaque K. and Salam Z., 2013. A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition. IEEE Transactions on Industrial Electronics 60(8): 3195–3206.

Patel H. and Agarwal V. 2008. Maximum power point tracking scheme for PV systems operating under partially shaded conditions. IEEE Transactions on Industrial Electronics 55(4):1689–1698.

Benyoucef A.S., Chouder A., Kara K., Silvestre S., and Sahed O.A., 2015. Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Applied Soft Computing 32: 38–48.

Ram J.P. and Rajasekar N. 2016. A novel flower pollination based global maximum power point method for solar maximum power point tracking. IEEE Transactions on Power Electronics 32(11): 8486-8499.

Mohanty S., Subudhi B., and Ray P.K., 2016. A New MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Transaction on Sustainable Energy 7(1): 181–188.

Raman G., Raman G., Manickam C., and Ganesan S.I., 2016. Dragonfly algorithm based global maximum power point tracker for photovoltaic systems. International conference on swarm intelligence (ICSI): 211-219.

Mohanty S., Subudhi B., and Ray P.K., 2017. A grey wolf-assisted perturb and observe MPPT algorithm for a PV system. IEEE Transactions on Energy Conversion 32(1): 340–347.

Mirjalili S., Mirjalili S.M., and Lewis A., 2014. Grey wolf optimizer. Advance Engineering Software 69: 46–61.

Nasrabadi M.S., Sharafi Y., and Tayari M., 2016. A Parallel Grey Wolf Optimizer combined with opposition based learning. 1st Conf. on Swarm Intelligence and Evolutionary Computation (CSIEC, IEEE): 18- 23.

Tizhoosh H.R. 2005. Opposition-based learning: a new scheme for machine intelligence. In Proceedings of International Conference on Computational Intelligence for Modeling Control and Automation (CIMCA-IAWTIC): 695-701.

Sundareswaran K., Peddapati S., and Palani S., 2014. MPPT of PV systems under partial shading conditions through a colony of flashing fireflies. IEEE Transactions on Energy Conversion 29(2): 463-472.

Sher H.A., Murtaza A.F., Noman A., Addoweesh K.E., Al-Haddad K., and Chiaberge M., 2015. A new sensor less hybrid MPPT algorithm based on fractional short circuit current measurement and P&O MPPT. IEEE Transactions on Sustainable Energy 6(4): 1426–1434.

Sundareswaran K., Kumar V., and Palani S., 2014. Application of a combined particle swarm optimization and perturbs and observes method for MPPT in PV systems under partial shading conditions. Renewable Energy 75: 308–317.