An Investigation of Intelligent and Conventional Maximum Power Point Tracking Techniques for Uniform Atmospheric Conditions

Year 2019, Volume: 3 Issue: 2, 93 - 100, 10.10.2019

Ekrem Kandemır , Numan Sabit Cetin , Selim Borekci

Abstract

In recent years, power generation from
photovoltaic (PV) system has received great attention compared to other
renewable sources. Due to nonlinear characteristics of PV cells, the maximum
allowable power level from PV panel changes with atmospheric parameters which
are solar irradiance and temperature. In this context, maximum power point
tracking (MPPT) algorithms are essential to maximize the output power of PV
panel for any solar irradiance and temperature values. In the literature,
various MPPT techniques have been studied to deliver maximum power from PV
systems. Hence, this study discusses intelligent control techniques, which are
called fuzzy logic controller (FLC) and neural network controller (NNC), and
compares efficiency performance and convergence speed to conventional perturb
& observe (P&O) and incremental conductance (Inc. Cond.) tracking
techniques for MPPT of PV system.

In this paper, 150W PV panel model is
investigated for different atmospheric conditions in MATLAB. Results of
simulation show that NNC based and FLC based MPPTs have 4.66% better tracking
accuracy than conventional P&O and Inc. Cond. under standard test condition
(STC). NNC based MPPT has best iteration response rate among the other MPPTs
under uniform atmospheric conditions. Therefore, the NNC based MPPT presents
best superior quality in terms of efficiency and convergence speed for PV
systems among the other MPPTs.        

Keywords

PV Model, Maximum Power Point Tracking, Perturb & Observe, Incremental Conductance, Fuzzy Logic Control, Neural Network Control

References

• [1]. J. Ahmed and Z. Salam, “A Modified P&O Maximum Power Point Tracking Method With Reduced Steady-State Oscillation and Improved Tracking Efficiency,” IEEE Trans. Sustain. Energy, vol. 7, no. 4, pp. 1506–1515, Oct. 2016.
• [2]. A. B. . Bahgat, N. . Helwa, G. . Ahamd, and E. . El Shenawy, “Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks,” 2004.
• [3]. J.-K. Shiau, Y.-C. Wei, and B.-C. Chen, “A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables,” Algorithms, vol. 8, no. 2, pp. 100–127, Apr. 2015.
• [4]. P. Takun, S. Kaitwanidvilai, and C. Jettanasen, “Maximum Power Point Tracking using Fuzzy Logic Control for Photovoltaic Systems,” in Proceedings of the International MultiConference of Engineers and Computer Scientists, 2011.
• [5]. A. Gupta, Y. K. Chauhan, and R. K. Pachauri, “A comparative investigation of maximum power point tracking methods for solar PV system,” Sol. Energy, vol. 136, pp. 236–253, 2016.
• [6]. E. Kandemir, N. S. Cetin, and S. Borekci, “A comprehensive overview of maximum power extraction methods for PV systems,” Renew. Sustain. Energy Rev., vol. 78, pp. 93–112, 2017.
• [7]. S. Saravanan and N. Ramesh Babu, “Maximum power point tracking algorithms for photovoltaic system – A review,” Renew. Sustain. Energy Rev., vol. 57, pp. 192–204, 2016.
• [8]. M. A. G. de Brito, L. Galotto, L. P. Sampaio, G. de A. e Melo, and C. A. Canesin, “Evaluation of the Main MPPT Techniques for Photovoltaic Applications,” IEEE Trans. Ind. Electron., vol. 60, no. 3, pp. 1156–1167, Mar. 2013.
• [9]. K. Karabacak and N. Cetin, “Artificial neural networks for controlling wind–PV power systems: A review,” Renew. Sustain. Energy Rev., vol. 29, pp. 804–827, Jan. 2014.
• [10]. D. Gonzalez Montoya, C. A. Ramos Paja, and R. Giral, “Maximum power point tracking of photovoltaic systems based on the sliding mode control of the module admittance,” Electr. Power Syst. Res., vol. 136, pp. 125–134, 2016.
• [11]. E. Kandemir, N. S. Cetin, and S. Borekci, “A Comparison of Perturb & Observe and Fuzzy-Logic Based MPPT Methods for Uniform Environment Conditions,” Period. Eng. Nat. Sci., vol. 5, no. 1, pp. 16–23, 2017.
• [12]. A. R. Jordehi, “Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches,” Renew. Sustain. Energy Rev., vol. 65, pp. 1127–1138, 2016.
• [13]. S. Borekci, E. Kandemir, and A. Kircay, “A Simpler Single-Phase Single-Stage Grid-Connected PV System with Maximum Power Point Tracking Controller,” Elektron. ir Elektrotechnika, vol. 21, no. 4, pp. 44–49, Aug. 2015.
• [14]. C. S. Chiu, “T-S Fuzzy Maximum Power Point Tracking Control of Solar Power Generation Systems,” IEEE Transactions on Energy Conversion, vol. 25, no. 4. pp. 1123–1132, 2010.
• [15]. L. L. Jiang, D. R. Nayanasiri, D. L. Maskell, and D. M. Vilathgamuwa, “A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics,” Renew. Energy, vol. 76, pp. 53–65, 2015.