access icon free Single sensor based MPPT for partially shaded solar photovoltaic by using human psychology optimisation algorithm

© The Institution of Engineering and Technology
Received 19/09/2016, Accepted 17/02/2017, Revised 15/01/2017, Published 21/02/2017

This study introduces a quick, highly efficient and a single sensor based maximum power point (MPP) tracking (MPPT) for partially shaded solar photovoltaic (PV) system. For this purpose, a novel ‘human psychology optimisation’ (HPO) algorithm is proposed, which is based on psychological and mental states of an ambitious person. The main objective of the HPO algorithm is the maximum extraction of the power from PV panel and efficiently supplying it to the load (battery). In this study, a single (current) sensor based MPPT for battery charging, by using HPO and some recent state-of-the-art MPPT algorithms, is tested on MATLAB simulation and verified on a developed prototype of the partially shaded solar PV system. The efficient battery charging and quickly reaching the MPP by HPO w.r.t. all other algorithms, in steady-state as well as in dynamic conditions, show the superiority over all the recent state-of-the-art control methods. Moreover, due to the single sensor, the cost of the MPPT system is reduced, as well as due to HPO the computational burden is very less, so it can be easily implemented on the low-cost microcontroller.

Inspec keywords: maximum power point trackers; optimisation; secondary cells; photovoltaic power systems; electric sensing devices; solar power stations

Other keywords: maximum power extraction; PV panel; ambitious person psychological state; low-cost microcontroller; battery charging; partially shaded solar photovoltaic system; human psychology optimisation algorithm; single sensor based maximum power point tracking; MATLAB simulation; HPO algorithm; single sensor based MPPT; ambitious person mental state

Subjects: Optimisation techniques; DC-DC power convertors; Solar power stations and photovoltaic power systems; Secondary cells; Sensing devices and transducers

References

    1. 1)
      • 19. Moubayed, N., Kouta, J., El-Ali, A., et al: ‘Parameter identification of the lead-acid battery model’. 33rd IEEE Photovoltaic Specialists Conf., San Diego, CA, USA, 2008, pp. 1–6.
    2. 2)
      • 17. Koad, R., Zobaa, A., El Shahat, A.: ‘A novel MPPT algorithm based on particle swarm optimisation for photovoltaic systems’, IEEE Trans. Sustain. Energy, 2016, 99, (PP), pp. 1–1.
    3. 3)
      • 4. Khouzam, K.Y.: ‘Optimum load matching in direct-coupled photovoltaic power systems-application to resistive loads’, IEEE Trans. Energy Convers., 1990, 5, (2), pp. 265–271.
    4. 4)
      • 10. Sundareswaran, K., Sankar, P., Nayak, P.S.R., et al: ‘Enhanced energy output from a PV system under partial shaded conditions through artificial bee colony’, IEEE Trans. Sustain. Energy, 2015, 6, (1), pp. 198–209.
    5. 5)
      • 25. Kahneman, D.: ‘Thinking fast and slow’ (Farrar, Straus and Giroux, New York, 2011).
    6. 6)
      • 16. Manickam, C., Raman, G.R., Raman, G.P., et al: ‘A hybrid algorithm for tracking of global MPP based on perturb and observe and particle swarm optimization with reduced power oscillation in string inverters’, IEEE Trans. Ind. Electron., 2016, 63, (10), pp. 6097–6106.
    7. 7)
      • 29. pbase.com: ‘measuring lead acid battery state of charge’, 2009. Available at http://www.pbase.com/mainecruising/battery_state_of_charge, accessed 12 June 2016.
    8. 8)
      • 9. Guruambeth, R., Ramabadran, R.: ‘Fuzzy logic controller for partial shaded photovoltaic array fed modular multilevel converter’, IET Power Electron., 2016, 9, (8), pp. 1694–1702.
    9. 9)
      • 18. Patel, H., Agarwal, V.: ‘MPPT scheme for a PV-fed single-phase single-stage grid-connected inverter operating in CCM with only one current sensor’, IEEE Trans. Energy Convers., 2009, 24, (1), pp. 256–263.
    10. 10)
      • 26. Goleman, D.: ‘Emotional intelligence: why it can matter more than IQ’ (Bantam Books, New York, 1995).
    11. 11)
      • 24. Haidt, J.: ‘The happiness hypothesis’ (Basic Books, New York, 2006).
    12. 12)
      • 23. Cialdini, R.B.: ‘Influence: the psychology of persuasion’ (Harper Collins Publications, New York, 2003).
    13. 13)
      • 20. Perez, R.: ‘Lead-acid battery state of charge vs. voltage’, Home Power, 1993, 36, pp. 66–69.
    14. 14)
      • 1. Jain, C., Singh, B.: ‘Single-phase single-stage multifunctional grid interfaced solar photo-voltaic system under abnormal grid conditions’, IET. Gener. Transm. Distrib., 2015, 9, (10), pp. 886–894.
    15. 15)
      • 27. Villalva, M.G., Gazoli, J.R., Filho, E.R.: ‘Comprehensive approach to modeling and simulation of photovoltaic arrays’, IEEE Trans. Power Electron., 2009, 24, (5), pp. 1198–1208.
    16. 16)
      • 11. Sundareswaran, K., Peddapati, S., Palani, S.: ‘MPPT of PV systems under partial shaded conditions through a colony of flashing fireflies’, IEEE Trans. Energy Convers., 2014, 29, (2), pp. 463–472.
    17. 17)
      • 21. Gladwell, M.: ‘Outliers: the story of success’ (Little, Brown and Company, Boston, 2008).
    18. 18)
      • 30. Rabideau, S.T.: ‘Effects of achievement motivation on behavior’, 2005. Available at http://www.personalityresearch.org/papers/rabideau.html, accessed 3 January 2016.
    19. 19)
      • 14. Seyedmahmoudian, M., Rahmani, R., Mekhilef, S., et al: ‘Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method’, IEEE Trans. Sustain. Energy, 2015, 6, (3), pp. 850–862.
    20. 20)
      • 22. Csikszentmihalyi, M.: ‘Flow: the psychology of happiness’ (Harper Collins, New York, 1994).
    21. 21)
      • 12. Mohanty, S., Subudhi, B., Ray, P.K.: ‘A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions’, IEEE Trans. Sustain. Energy, 2016, 7, (1), pp. 181–188.
    22. 22)
      • 15. Renaudineau, H., Donatantonio, F., Fontchastagner, J., et al: ‘A PSO-based global MPPT technique for distributed PV power generation’, IEEE Trans. Ind. Electron., 2015, 62, (2), pp. 1047–1058.
    23. 23)
      • 6. Linus, R.M., Damodharan, P.: ‘Maximum power point tracking method using a modified perturb and observe algorithm for grid connected wind energy conversion systems’, IET Renew. Power Gener., 2015, 9, (6), pp. 682–689.
    24. 24)
      • 5. Sahu, H.S., Nayak, S.K.: ‘Numerical approach to estimate the maximum power point of a photovoltaic array’, IET. Gener. Transm. Distrib., 2016, 10, (11), pp. 2670–2680.
    25. 25)
      • 7. Zakzouk, N.E., Elsaharty, M.A., Abdelsalam, A.K., et al: ‘Improved performance low-cost incremental conductance PV MPPT technique’, IET Renew. Power Gener., 2016, 10, (4), pp. 561–574.
    26. 26)
      • 28. Mäki, A., Valkealahti, S.: ‘Effect of photovoltaic generator components on the number of MPPs under partial shading conditions’, IEEE Trans. Energy Convers., 2013, 28, (4), pp. 1008–1017.
    27. 27)
      • 2. Gow, J.A., Manning, C.D.: ‘Photovoltaic converter system suitable for use in small scale stand-alone or grid connected applications’, IEE Proc. Electr. Power Appl., 2000, 147, (6), pp. 535–543.
    28. 28)
      • 13. Rani, B.I., Ilango, G.S., Nagamani, C.: ‘Enhanced power generation from PV array under partial shading conditions by shade dispersion using Su Do Ku configuration’, IEEE Trans. Sustain. Energy, 2013, 4, (3), pp. 594–601.
    29. 29)
      • 8. Elobaid, L.M., Abdelsalam, A.K., Zakzouk, E.E.: ‘Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey’, IET Renew. Power Gener., 2015, 9, (8), pp. 1043–1063.
    30. 30)
      • 3. Bhattacharjee, A.: ‘Design and comparative study of three photovoltaic battery charge control algorithms in MATLAB/SIMULINK environment’, Int. J. Adv. Comput. Res., 2012, 2, (3), pp. 1–6.
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