Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey

Lina M. Elobaid; Ahmed K. Abdelsalam; Ezeldin E. Zakzouk

Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey

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Author(s): Lina M. Elobaid ¹ ; Ahmed K. Abdelsalam ¹ ; Ezeldin E. Zakzouk ¹
- Affiliations: 1: Electrical Engineering Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria, Egypt
Source: Volume 9, Issue 8, November 2015, p. 1043 – 1063
DOI: 10.1049/iet-rpg.2014.0359 , Print ISSN 1752-1416, Online ISSN 1752-1424

Received 29/10/2014, Accepted 19/05/2015, Revised 11/05/2015, Published

Recent researches oriented to photovoltaic (PV) systems feature booming interest in current decade. For efficiency improvement, maximum power point tracking (MPPT) of PV array output power is mandatory. Although classical MPPT techniques offer simplified structure and implementation, their performance is degraded when compared with artificial intelligence-based techniques especially during partial shading and rapidly changing environmental conditions. Artificial neural network (ANN) algorithms feature several capabilities such as: (i) off-line training, (ii) nonlinear mapping, (iii) high-speed response, (iv) robust operation, (v) less computational effort and (vi) compact solution for multiple-variable problems. Hence, ANN algorithms have been widely applied as PV MPPT techniques. Among various available ANN-based PV MPPT techniques, very limited references gather those techniques as a survey. Neither classification nor comparisons between those competitors exist. Moreover, no detailed analysis of the system performance under those techniques has been previously discussed. This study presents a detailed survey for ANN based PV MPPT techniques. The authors propose new categorisation for ANN PV MPPT techniques based on controller structure and input variables. In addition, a detailed comparison between those techniques from several points of view, such as ANN structure, experimental verification and transient/steady-state performance is presented. Recent references are taken into consideration for update purpose.

References

1. 1)
  - 48. Singh, M.D., Shine, V.J., Janamala, V.: ‘Application of artificial neural networks in optimizing MPPT control for standalone solar PV system’. 2014 Int. Conf. on Contemporary Computing and Informatics (IC3I), 27–29 November 2014, pp. 162–166.
2. 2)
  - 19. Lu, H.-C., Shih, T.-L.: ‘Design of DC/DC boost converter with FNN solar cell maximum power point tracking controller’. 2010 the Fifth IEEE Conf. on Industrial Electronics and Applications (ICIEA), 15–17 June 2010, pp. 802–807.
3. 3)
  - 35. Hiyama, T., Kouzuma, S., Imakubo, T., Ortmeyer, T.H.: ‘Evaluation of neural network based real time maximum power tracking controller for PV system’, IEEE Trans. Energy Convers., 1995, 10, (3), pp. 543–548 (doi: 10.1109/60.464880).
4. 4)
  - 40. Veerachary, M., Senjyu, T., Uezato, K.: ‘Feedforward maximum power point tracking of PV systems using fuzzy controller’, IEEE Trans. Aerosp. Electron. Syst., 2002, 38, (3), pp. 969–981 (doi: 10.1109/TAES.2002.1039412).
5. 5)
  - 43. Pachauri, R.K., Chauhan, Y.K.: ‘Hydrogen generation/pressure enhancement using FC and ANN based MPPT assisted PV system’. 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH), 28–29 November 2014, pp. 427–432.
6. 6)
  - 8. De Giorgi, M.G., Congedo, P.M., Malvoni, M.: ‘Photovoltaic power forecasting using statistical methods: impact of weather data’, IET Sci. Meas. Technol., 2014, 8, (3), pp. 90–97 (doi: 10.1049/iet-smt.2013.0135).
7. 7)
  - 11. Cha, I.-S., Choi, J.-G., Yu, G.-J., Jung, M.-W., Baek, H.-L., Kim, D.-H.: ‘MPPT for temperature compensation of photovoltaic system with neural networks’. Photovoltaic Specialists Conf., 1997, Conf. Record of the 26th IEEE, 29 September–3 October 1997, pp. 1321–1324.
8. 8)
  - 18. Xu, Y., Chengbi, Z.: ‘Generalized dynamic fuzzy neural network-based tracking control of PV’. Power and Energy Engineering Conf. (APPEEC), 2010 Asia-Pacific, 28–31 March 2010, pp. 1–4.
9. 9)
  - 7. Bidram, A., Davoudi, A., Balog, R.S.: ‘Control and circuit techniques to mitigate partial shading effects in photovoltaic arrays’, IEEE J. Photovoltaics, 2012, 2, (4), pp. 532–546 (doi: 10.1109/JPHOTOV.2012.2202879).
10. 10)
  - 46. Natsheh, E.M., Natsheh, A.R., Albarbar, A.: ‘Intelligent controller for managing power flow within standalone hybrid power systems’, IET Sci. Meas. Technol., 2013, 7, (4), pp. 191–200 (doi: 10.1049/iet-smt.2013.0011).
11. 11)
  - 21. Baek, J.-W., Ko, J.-S., Choi, J.-S., Kang, S.-J., Chung, D.-H.: ‘Maximum power point tracking control of photovoltaic system using neural network’. 2010 Int. Conf. on Electrical Machines and Systems (ICEMS), 10–13 October 2010, pp. 638–643.
12. 12)
  - 37. Di Piazza, M.C., Luna, M., Pucci, M., Vitale, G.: ‘PV-based Li–ion battery charger with neural MPPT for autonomous sea vehicles’. Industrial Electronics Society, IECON 2013 – 39th Annual Conf. of the IEEE, 10–13 November 2013, pp. 7267–7273.
13. 13)
  - 23. Ko, J.-S., Jung, B.-J., Park, K.-T., Choi, C.-H., Chung, D.-H.: ‘Maximum power point tracking control of PV system for DC motors drive with neural network’. Int. Conf. on Smart Manufacturing Application, 2008. ICSMA 2008, 9–11 April 2008, pp. 514–519.
14. 14)
  - 13. Petchjatuporn, P., Ngamkham, W., Khaehintung, N., Sirisuk, P., Kiranon, W., Kunakorn, A.: ‘A solar-powered battery charger with neural network maximum power point tracking implemented on a low-cost PIC-microcontroller’. TENCON 2005 2005 IEEE Region 10, 21–24 November 2005, pp. 1–4.
15. 15)
  - 33. Li, X., Li, Y., Seem, J.E.: ‘Maximum power point tracking for photovoltaic system using adaptive extremum seeking control’, IEEE Trans. Control Syst. Technol., 2013, 21, (6), pp. 2315–2322 (doi: 10.1109/TCST.2012.2223819).
16. 16)
  - 20. Giraud, F., Salameh, Z.M.: ‘Analysis of the effects of a passing cloud on a grid-interactive photovoltaic system with battery storage using neural networks’, IEEE Trans. Energy Convers., 1999, 14, (4), pp. 1572–1577 (doi: 10.1109/60.815107).
17. 17)
  - 4. Subudhi, B., Pradhan, R.: ‘A comparative study on maximum power point tracking techniques for photovoltaic power systems’, IEEE Trans. Sustain. Energy, 2013, 4, (1), pp. 89–98 (doi: 10.1109/TSTE.2012.2202294).
18. 18)
  - 29. Di Piazza, M.C., Pucci, M., Ragusa, A., Vitale, G.: ‘A growing neural gas network based MPPT technique for multi-string PV plants’. 2010 IEEE Int. Symp. on Industrial Electronics (ISIE), 4–7 July 2010, pp. 544–549.
19. 19)
  - 1. de Brito, M.A.G., Galotto, L.Jr., Sampaio, L.P., et al: ‘Evaluation of the main MPPT techniques for photovoltaic applications’, IEEE Trans. Ind. Electron., 2013, 60, (3), pp. 1156–1167 (doi: 10.1109/TIE.2012.2198036).
20. 20)
  - 28. Mathew, A., Selvakumar, A.I.: ‘MPPT based stand-alone water pumping system’. 2011 Int. Conf. on Computer, Communication and Electrical Technology (ICCCET), 18–19 March 2011, pp. 455–460.
21. 21)
  - 39. Veerachary, M., Senjyu, T., Uezato, K.: ‘Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller’, IEEE Trans. Ind. Electron., 2003, 50, (4), pp. 749–758 (doi: 10.1109/TIE.2003.814762).
22. 22)
  - 17. Dzung, P.Q., Khoa, L.D., Lee, H.-H., Phuong, L.M., Vu, N.T.: ‘The new MPPT algorithm using ANN-based PV’. Strategic Technology (IFOST), 13–15 October 2010, pp. 402–407.
23. 23)
  - 14. Choi, J.-S., Kim, D.-Y., Park, K.-T., Choi, J.-H., Chung, D.-H.: ‘Tracking system and MPPT control for efficiency improvement of photovoltaic’. Int. Conf. on Control, Automation and Systems, 2008. ICCAS 2008, 14–17 October 2008, pp. 1341–1344.
24. 24)
  - 36. Bastidas-Rodriguez, J.D., Franco, E., Petrone, G., Andrés Ramos-Paja, C., Spagnuolo, G.: ‘Maximum power point tracking architectures for photovoltaic systems in mismatching conditions: a review’, IET Power Electron., 2014, 7, (6), pp. 1396–1413 (doi: 10.1049/iet-pel.2013.0406).
25. 25)
  - 51. Yona, A., Senjyu, T., Funabashi, T., Kim, C.-H.: ‘Determination method of insolation prediction with fuzzy and applying neural network for long-term ahead PV power output correction’, IEEE Trans. Sustain. Energy, 2013, 4, (2), pp. 527–533 (doi: 10.1109/TSTE.2013.2246591).
26. 26)
  - 31. Syafaruddin, K.E., Hiyama, T.: ‘Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions’, IET Renew. Power Gener., 2009, 3, (2), pp. 239–253 (doi: 10.1049/iet-rpg:20080065).
27. 27)
  - 16. Wu, M.-K., Widodo, S.: ‘Single input cerebellar model articulation controller (CMAC) based maximum power point tracking for photovoltaic system’. 2010 Int. Symp. on Computer Communication Control and Automation (3CA), 5–7 May 2010, vol. 2, pp. 439–442.
28. 28)
  - 27. Ramaprabha, R., Gothandaraman, V., Kanimozhi, K., Divya, R., Mathur, B.L.: ‘Maximum power point tracking using GA-optimized artificial neural network for Solar PV system’. 2011 First Int. Conf. on Electrical Energy Systems (ICEES), 3–5 January 2011, pp. 264–268.
29. 29)
  - 10. Abdelsalam, A.K., Goh, S., Abdelkhalik, O., Ahmed, S., Massoud, A.: ‘Iterated unscented Kalman filter-based maximum power point tracking for photovoltaic applications’. Industrial Electronics Society, IECON 2013 – 39th Annual Conf. of the IEEE, 10–13 November 2013, pp. 1685–1693.
30. 30)
  - 47. Di Piazza, M.C., Pucci, M., Ragusa, A., Vitale, G.: ‘Analytical versus neural real-time simulation of a photovoltaic generator based on a DC–DC converter’, IEEE Trans. Ind. Appl., 2010, 46, (6), pp. 2501–2510 (doi: 10.1109/TIA.2010.2072975).
31. 31)
  - 8. Esram, T., Chapman, P.L.: ‘Comparison of photovoltaic array maximum power point tracking techniques’, IEEE Trans. Energy Convers., 2007, 22, (2), pp. 439–449 (doi: 10.1109/TEC.2006.874230).
32. 32)
  - 22. Zhang, L., Bai, Y., Al-Amoudi, A.: ‘GA-RBF neural network based maximum power point tracking for grid-connected photovoltaic systems’. Int. Conf. on Power Electronics, Machines and Drives, 2002 (Conf. Publication No. 487), 4–7 June 2002, pp. 18–23.
33. 33)
  - 49. Liu, J., Fang, W., Zhang, X., Yang, C.: ‘An improved photovoltaic power forecasting model with the assistance of aerosol index data’, IEEE Trans. Sustain. Energy, 2015, 6, (2), pp. 434–442 (doi: 10.1109/TSTE.2014.2381224).
34. 34)
  - 38. Elobaid, L.M., Abdelsalam, A.K., Zakzouk, E.E.: ‘Artificial neural network based maximum power point tracking technique for PV systems’. IECON 2012 – 38th Annual Conf. on IEEE Industrial Electronics Society, 25–28 October 2012, pp. 937–942.
35. 35)
  - 21. Pilawa-Podgurski, R.C.N., Perreault, D.J.: ‘Submodule integrated distributed maximum power point tracking for solar photovoltaic applications’, IEEE Trans. Power Electron., 2013, 28, (6), pp. 2957–2967 (doi: 10.1109/TPEL.2012.2220861).
36. 36)
  - 9. Zakzouk, N.E., Abdelsalam, A.K., Helal, A.A., Williams, B.W.: ‘Modified variable-step incremental conductance maximum power point tracking technique for photovoltaic systems’. Industrial Electronics Society, IECON 2013 – 39th Annual Conf. of the IEEE, 10–13 November 2013, pp. 1741–1748.
37. 37)
  - 24. Ramaprabha, R., Mathur, B.L., Sharanya, M.: ‘Solar array modeling and simulation of MPPT using neural network’. 2009 Int. Conf. on Control, Automation, Communication and Energy Conservation, 2009. INCACEC 2009, 4–6 June 2009, pp. 1–5.
38. 38)
  - 36. Hiyama, T., Kouzuma, S., Imakubo, T.: ‘Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control’, IEEE Trans. Energy Convers., 1995, 10, (2), pp. 360–367 (doi: 10.1109/60.391904).
39. 39)
  - 12. Khaehintung, N., Sirisuk, P., Kurutach, W.: ‘A novel ANFIS controller for maximum power point tracking in photovoltaic systems’. Fifth Int. Conf. on Power Electronics and Drive Systems, 2003. PEDS 2003, 17–20 November 2003, vol. 2, pp. 833–836.
40. 40)
  - 45. Sekhar, P.C., Mishra, S., Sharma, R.: ‘Data analytics based neuro-fuzzy controller for diesel-photovoltaic hybrid AC microgrid’, IET Gener. Transm. Distrib., 2015, 9, (2), pp. 193–207 (doi: 10.1049/iet-gtd.2014.0287).
41. 41)
  - 3. Chatterjee, A., Keyhani, A.: ‘Neural network estimation of microgrid maximum solar power’, IEEE Trans. Smart Grid, 2012, 3, (4), pp. 1860–1866 (doi: 10.1109/TSG.2012.2198674).
42. 42)
  - 42. Khaldi, N., Mahmoudi, H., Zazi, M., Barradi, Y.: ‘The MPPT control of PV system by using neural networks based on Newton–Raphson method’. 2014 Int. Renewable and Sustainable Energy Conf. (IRSEC), 17–19 October 2014, pp. 19–24.
43. 43)
  - 48. Singh, M.D., Shine, V.J., Janamala, V.: ‘Application of artificial neural networks in optimizing MPPT control for standalone solar PV system’. 2014 Int. Conf. onContemporary Computing and Informatics (IC3I), 27–29 November 2014, pp. 162–166.
44. 44)
  - 30. Otieno, C.A., Nyakoe, G.N., Wekesa, C.W.: ‘A neural fuzzy based maximum power point tracker for a photovoltaic system’. AFRICON, 2009. AFRICON ’09, 23–25 September 2009, pp. 1–6.
45. 45)
  - 34. Ocran, T.A., Cao, J., Cao, B., Sun, X.: ‘Artificial neural network maximum power point tracker for solar electric vehicle’, Tsinghua Sci. Technol., 2005, 10, (2), pp. 204–208 (doi: 10.1016/S1007-0214(05)70055-9).
46. 46)
  - 41. Al-Amoudi, A., Zhang, L.: ‘Application of radial basis function networks for solar-array modelling and maximum power-point prediction’, IEE Proc., Gener. Transm. Distrib., 2000, 147, (5), pp. 310–316 (doi: 10.1049/ip-gtd:20000605).
47. 47)
  - 32. Samangkool, K., Premrudeepreechacharn, S.: ‘Maximum power point tracking using neural networks for grid-connected photovoltaic system’. 2005 Int. Conf. on Future Power Systems, 18 November 2005, pp. 4–18.
48. 48)
  - 44. Bendib, B., Krim, F., Belmili, H., Almi, M.F., Bolouma, S.: ‘An intelligent MPPT approach based on neural-network voltage estimator and fuzzy controller, applied to a stand-alone PV system’. 2014 IEEE 23rd Int. Symp. on Industrial Electronics (ISIE), 1–4 June 2014, pp. 404–409.
49. 49)
  - 6. Zakzouk, N.E., Abdelsalam, A.K., Helal, A.A., Williams, B.W.: ‘DC-link voltage sensorless control technique for single-phase two-stage photovoltaic grid-connected system’. 2014 IEEE Int. Energy Conf. (ENERGYCON), 13–16 May 2014, pp. 58–64.
50. 50)
  - 25. Kaliamoorthy, M., Sekar, R.M., Rajaram, R.: ‘Solar powered single stage boost inverter with ANN based MPPT algorithm’. 2010 IEEE Int. Conf. on Communication Control and Computing Technologies (ICCCCT), 7–9 October 2010, pp. 165–170.
51. 51)
  - 26. Chaouachi, A., Kamel, R.M., Nagasaka, K.: ‘Microgrid efficiency enhancement based on neuro-fuzzy MPPT control for photovoltaic generator’. 2010 35th IEEE Photovoltaic Specialists Conf. (PVSC), 20–25 June 2010, pp. 002889–002894.
52. 52)
  - 15. Chun-hua, L., Xu, J., Xin-jian, Z.: ‘Study on control strategy for photovoltaic energy systems based on recurrent fuzzy neural networks’. Fifth Int. Conf. on Natural Computation, 2009. ICNC’09, 14–16 August 2009, vol. 2, pp. 282–286.

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Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey

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