© The Institution of Engineering and Technology
This study deals with a new version of hillclimbing (HC) algorithm for maximum power peak estimation of the solar photovoltaic (PV) panel, which has selfestimation (SEn) and decisiontaking ability. Moreover, it is based on a single sensor, which is applicable for the PV arrayfed battery charging. The working principle of SelfEstimated HC (SEHC) algorithm is based on three consecutive operating points on the powercurrent characteristic. By using perpendicular line analogy (PLA), these points decide direction, and an optimised operating position for next iteration, which is responsible for quick maximum power point tracking as well as improved dynamic performance. Moreover, in every new iteration, the step size is reduced by 90% from the previous step size, which provides an oscillationfree steadystate performance. Owing to a single sensor, the computational burden, as well as calculation complexity of the SEHC algorithm is less, so it can be simply implemented on a cheaper microcontroller. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on the experimental prototype. Moreover, the performance of SEHC algorithm is compared with recent stateoftheart techniques. The highly suitable and satisfactory results of SEHC algorithm show the superiority over the stateoftheart methods.
References


1)

1. Chattopadhyay, S.K., Chakraborty, C.: ‘A new asymmetric multilevel inverter topology suitable for solar PV applications with varying irradiance’, IEEE Trans. Sustain. Energy, 2017, 8, (4), pp. 1496–1506.

2)

2. Schittekatte, T., Stadler, M., Cardoso, G., et al: ‘The impact of shortterm stochastic variability in solar irradiance on optimal microgrid design’, IEEE Trans. Smart Grid, 2017, PP, (99), pp. 1–1, .

3)

3. Das, M., Agarwal, V.: ‘Novel highperformance standalone solar PV system with highgain highefficiency DC–DC converter power stages’, IEEE Trans. Ind. Appl., 2015, 51, (6), pp. 4718–4728.

4)

4. Abdelmoaty, A.A., AlShyoukh, M., Hsu, Y.C., et al: ‘A MPPT circuit with 25 μW power consumption and 99.7% tracking efficiency for PV systems’, IEEE Trans. Circuits Syst. I, Regul. Pap., 2017, 64, (2), pp. 272–282.

5)

5. Kjaer, S.B.: ‘Evaluation of the ‘hill climbing’ and the ‘incremental conductance’ maximum power point trackers for photovoltaic power systems’, IEEE Trans. Energy Convers., 2012, 27, (4), pp. 922–929.

6)

6. Piegari, L., Rizzo, R.: ‘Adaptive perturb and observe algorithm for photovoltaic maximum power point tracking’, IET Renew. Power Gener., 2010, 4, (4), pp. 317–328.

7)

7. Elgendy, M.A., Atkinson, D.J., Zahawi, B.: ‘Experimental investigation of the incremental conductance maximum power point tracking algorithm at high perturbation rates’, IET Renew. Power Gener., 2016, 10, (2), pp. 133–139.

8)

8. Huynh, D.C., Dunnigan, M.W.: ‘Development and comparison of an improved incremental conductance algorithm for tracking the MPP of a solar PV panel’, IEEE Trans. Sustain. Energy, 2016, 7, (4), pp. 1421–1429.

9)

9. Alajmi, B.N., Ahmed, K.H., Finney, S.J., et al: ‘Fuzzylogiccontrol approach of a modified hillclimbing method for maximum power point in microgrid standalone photovoltaic system’, IEEE Trans. Power Electron., 2011, 26, (4), pp. 1022–1030.

10)

10. Elbaset, A.A., Ali, H., AbdEl Sattar, M., et al: ‘Implementation of a modified perturb and observe maximum power point tracking algorithm for photovoltaic system using an embedded microcontroller’, IET Renew. Power Gener., 2016, 10, (4), pp. 551–560.

11)

11. Zakzouk, N.E., Elsaharty, M.A., Abdelsalam, A.K., et al: ‘Improved performance lowcost incremental conductance PV MPPT technique’, IET Renew. Power Gener., 2016, 10, (4), pp. 561–574.

12)

12. Kumar, N., Hussain, I., Singh, B., et al: ‘Maximum power peak detection of partially shaded PV panel by using intelligent monkey king evolution algorithm’, IEEE Trans. Ind. Appl., 2017, PP, (99), pp. 1–1, .

13)

13. Kumar, N., Hussain, I., Singh, B., et al: ‘Maximum power peak detection of partially shaded PV panel by using intelligent monkey king evolution algorithm’. 2016 IEEE Int. Conf. Power Electronics, Drives and Energy Systems (PEDES), Trivandrum, 2016, pp. 1–6.

14)

14. Kumar, N., Hussain, I., Singh, B., et al: ‘Framework of maximum power extraction from solar PV panel using self predictive perturb and observe algorithm’, IEEE Trans. Sustain. Energy, 2017, PP, (99), pp. 1–1, .

15)

15. Kumar, N., Hussain, I., Singh, B., et al: ‘Normal harmonic search algorithm based MPPT of solar PV system’. 2016 Seventh IEEE India Int. Conf. Power Electronics, Patiala, 2016, pp. 1–6.

16)

16. Kumar, N., Hussain, I., Singh, B., et al: ‘Maximum power extraction from partially shaded PV panel in rainy season by using improved antlions optimization algorithm’. 2016 Seventh Power India Int. Conf. (PIICON), Bikaner, 2016, pp. 1–6.

17)

17. Syafaruddin, E.K., Hiyama, T.: ‘Artificial neural networkpolar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions’, IET Renew. Power Gener., 2009, 3, (2), pp. 239–253.

18)

18. Kumar, N., Hussain, I., Singh, B., et al: ‘Single sensor based MPPT for partially shaded solar photovoltaic by using human psychology optimisation algorithm’, IET Gener. Transm. Distrib., 2017, 11, (10), pp. 2562–2574.

19)

19. Mohd Zainuri, M.A.A., Mohd Radzi, M.A., Soh, A.C., et al: ‘Development of adaptive perturb and observefuzzy control maximum power point tracking for photovoltaic boost dc–dc converter’, IET Renew. Power Gener., 2014, 8, (2), pp. 183–194.

20)

20. Kumar, N., Hussain, I., Singh, B., et al: ‘MPPT in dynamic condition of partially shaded PV system by using WODE technique’, IEEE Trans. Sustain. Energy, 2017, 8, (3), pp. 1204–1214.

21)

21. Lal, V.N., Singh, S.N.: ‘Modified particle swarm optimisationbased maximum power point tracking controller for singlestage utilityscale photovoltaic system with reactive power injection capability’, IET Renew. Power Gener., 2016, 10, (7), pp. 899–907.

22)

22. Kumar, N., Hussain, I., Singh, B., et al: ‘Peak power detection of PS solar PV panel by using WPSCO’, IET Renew. Power Gener., 2017, 11, (4), pp. 480–489.

23)

23. 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.

24)

24. Teshome, D.F., Lee, C.H., Lin, Y.W., et al: ‘A modified firefly algorithm for photovoltaic maximum power point tracking control under partial shading’, IEEE J. Emerging Sel. Top. Power Electron., 2017, 5, (2), pp. 661–671.

25)

25. Kumar, N., Hussain, I., Singh, B., et al: ‘Rapid MPPT for uniformly and partial shaded PV system by using JayaDE algorithm in highly fluctuating atmospheric conditions’, IEEE Trans. Ind. Inf., 2017, 13, (5), pp. 2406–2416.

26)

26. Sundareswaran, K., Peddapati, S., Palani, S.: ‘Application of random search method for maximum power point tracking in partially shaded photovoltaic systems’, IET Renew. Power Gener., 2014, 8, (6), pp. 670–678.

27)

27. Kumar, N., Hussain, I., Singh, B., et al: ‘Single sensor based MPPT of partially shaded PV system for battery charging by using Cauchy and Gaussian sine cosine optimization’, IEEE Trans. Energy Convers., 2017, 32, (3), pp. 983–992.

28)

28. Blanes, J.M., Toledo, F.J., Montero, S., et al: ‘Insite realtime photovoltaic I–V curves and maximum power point estimator’, IEEE Trans. Power Electron., 2013, 28, (3), pp. 1234–1240.

29)

29. Wang, J.C., Su, Y.L., Shieh, J.C., et al: ‘Highaccuracy maximum power point estimation for photovoltaic arrays’, Sol. Energy Mater. Sol. Cells, 2011, 95, pp. 843–851.

30)

30. Garrigós, A., Blanes, J.M., Carrasco, J.A., et al: ‘Real time estimation of photovoltaic modules characteristics and its application to maximum power point operation’, Renew. Energy, 2007, 32, pp. 1059–1076.

31)

31. Urayai, C., Amaratunga, G.A.J.: ‘Singlesensor maximum power point tracking algorithms’, IET Renew. Power Gener., 2013, 7, (1), pp. 82–88.

32)

32. Moubayed, N., Kouta, J., ElAli, A., et al: ‘Parameter identification of the lead–acid battery model’. 33rd IEEE Photovoltaic Specialists Conf., San Diego, CA, USA, 2008, pp. 1–6.

33)

33. Babu, C., Gurjar, S.: ‘A novel simplified twodiode model of photovoltaic (PV) module’, IEEE J. Photovolt., 2014, 4, (4), pp. 1156–1161.

34)

34. Energy Matters: ‘Deep cycle battery voltage & state of charge’, 2016. .
http://iet.metastore.ingenta.com/content/journals/10.1049/ietrpg.2017.0019
Related content
content/journals/10.1049/ietrpg.2017.0019
pub_keyword,iet_inspecKeyword,pub_concept
6
6