Design and Analysis of Maximum Power Point Tracking (MPPT) Controller for PV System


Muhammad Yousaf Ali Khan,Faheem Khan,Hamayun Khan,Sheeraz Ahmed,Mukhtar Ahmad,



Electricity,Renewable Energy,Solar Charge Controller, Maximum Power Point Tracking,


With the passage of time, the demand of electricity is increasing day by day. The conventional electricity resources are getting depleted because of limited reserves of coal, natural gas and oil. Also most of the electricity resources are not environmental friendly. There was a need to design a mechanism that can be used as an alternative resource for the production of electricity that can be environmental friendly as well as a cheap source of generation. In the last couple of years, it is indicated that energy obtained from the sun can be the best alternate resource for energy. In this research work, the system design approach based on the Maximum Power Point Tracking (MPPT) Controller has been designed. This approach is utilized for extracting maximum available power from PV module through simulation in protius software. This system is quite efficient, effective and has high performances. Buck and boost converter have been utilized for better efficiency.


I.A. Ali, Y. Wang, W. Li and X. He, “Implementation of simple moving voltage average technique with direct control incremental conductance method to optimize the efficiency of DC microgrid,” in Emerging Technologies (ICET), 2015 International Conference on, 2015.

II.A. Argentiero, C. A. Bollino, S. Micheli and C. Zopounidis, “Renewable energy sources policies in a Bayesian DSGE model,” Renewable Energy, vol. 120, pp. 60-68, 2018.

III.A. Naserbegi, M. Aghaie, A. Minuchehr and G. Alahyarizadeh, “A novel exergy optimizationof Bushehr nuclear power plant by Gravitational Search Algorithm (GSA),” Energy, 2018.

IV.A. Soetedjo, A. Lomi and B. J. Puspita, “A Hardware Testbed of Grid-Connected Wind-Solar Power System,” International Journal of Smart Grid and Sustainable Energy Technologies, vol. 1, pp. 52-56, 2018.

V.A. M. Atallah, A. Y. Abdelaziz and R. S. Jumaah, “Implementation of perturb and observe MPPT of PV system with direct control method using buck and buck-boost converters,” Emerging Trends in Electrical, Electronics & Instrumentation Engineering: An international Journal (EEIEJ), vol. 1, pp. 31-44, 2014.

VI.B. Gjorgiev and G. Sansavini, “Electrical power generation under policy constrained water-energy nexus,” Applied Energy, vol. 210, pp. 568-579, 2018.

VII.F. Zhou, Y.-F. Chang, B. Fowler, K. Byun and J. C. Lee, “Stabilization of multiple resistance levels by current-sweep in SiOx-based resistive switching memory,” Applied Physics Letters, vol. 106, p. 063508, 2015.

VIII.J. Ahmed and Z. Salam, “An improved perturb and observe (P&O)maximum power point tracking (MPPT) algorithm for higher efficiency,” Applied Energy, vol. 150, pp. 97-108, 2015.

IX.K. Khanafer and K. Vafai, “A review on the applications of nanofluids in solar energy field,” Renewable Energy, 2018.

X.K. Ishaque, Z. Salamand G. Lauss, “The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions,” Applied Energy, vol. 119, pp. 228-236, 2014.

XI.K. S. Tey and S. Mekhilef, “Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level,” Solar Energy, vol. 101, pp. 333-342, 2014.

XII.L.-L. Li, G.-Q. Lin, M.-L. Tseng, K. Tan and M. K. Lim, “A Maximum Power Point Tracking Method for PV System with Improved Gravitational Search Algorithm,” Applied Soft Computing, 2018.

XIII.M. Peng, Y. Li, Z. Zhao and C. Wang, “System architecture and key technologies for 5G heterogeneous cloud radio access networks,” IEEE network, vol. 29, pp. 6-14, 2015.

XIV.P. Sivakumar, A. A. Kader, Y. Kaliavaradhan and M. Arutchelvi, “Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions,” Renewable Energy, vol. 81, pp. 543-550, 2015.

XV.P. Ghamisi and J. A. Benediktsson, “Feature selection based on hybridization of genetic algorithm and particle swarm optimization,” IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 309-313, 2015.

XVI.R. Kardooni, S. B. Yusoff, F. B. Kari and L. Moeenizadeh, “Public opinion on renewable energy technologies and climate change in Peninsular Malaysia,” Renewable Energy, vol. 116, pp. 659-668, 2018.

XVII.R. M. Linus and P. Damodharan, “Maximum power point tracking method using a modified perturb and observe algorithm for grid connected wind energy conversion systems,” IET Renewable Power Generation, vol. 9, pp. 682-689, 2015.

XVIII.R. Cheng and Y. Jin, “A social learning particle swarmoptimization algorithm for scalable optimization,” Information Sciences, vol. 291, pp. 43-60, 2015.

XIX.S. Dincer and I. Dincer, “Comparative Evaluation of Possible Desalination Options With Various Nuclear Power Plants,” in Exergetic, Energetic and Environmental Dimensions, Elsevier, 2018, pp. 569-582.

XX.S. Krauter, “Simple and effective methods to match photovoltaic power generation to the grid load profile for a PV based energy system,” Solar Energy, vol. 159, pp. 768-776, 2018.

XXI.S. Carley, “State renewable energy electricity policies: An empirical evaluation of effectiveness,” Energy policy, vol. 37, pp. 3071-3081, 2009.

XXII.S. P. Ayeng’o, T. Schirmer, K.-P. Kairies, H. Axelsen and D. U. Sauer, “Comparison of off-grid power supply systems using lead-acid and lithium-ion batteries,” Solar Energy, vol. 162, pp. 140-152, 2018.

XXIII.S. Kiranyaz, T. Ince and M. Gabbouj, “Particle swarm optimization,” in Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition, Springer, 2014, pp. 45-82.

XXIV.T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transactions on energy conversion, vol. 22, pp. 439-449, 2007.

XXV.V. Salas, E. Olias, A. Barrado and A. Lazaro, “Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems,” Solar energy materials and solar cells, vol. 90, pp. 1555-1578, 2006.

XXVI.Y. Shi and R. C. Eberhart, “Fuzzy adaptive particle swarm optimization,” in Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 2001.

Muhammad Yousaf Ali Khan, Faheem Khan, Hamayun Khan, Sheeraz Ahmed, Mukhtar Ahmad View Download