Maximum Power Point Tracking Using Fuzzy Logic Controller under Partial Conditions

Scientific Research Publishing, Smart Grid and Renewable Energy, 2015.

Maximum Power Point  ABSTRACT: This study proposes a fuzzy system for tracking the maximum power point of a PV system for solar panel. The solar panel and maximum power point tracker have been modeled using MATLAB/Simulink. A simulation model consists of PV panel, boost converter, and maximum power point tack MPPT algorithm is developed. Three different conditions are simulated: 1) Uniform irradiation; 2) Sudden changing; 3) Partial shading. Results showed that fuzzy controller successfully find MPP for all different weather conditions studied. FLC has excellent ability to track MPP in less than 0.01 second when PV is subjected to sudden changes and partial shading in irradiation.

KEYWORDS:

  • Fuzzy Logic Controller
  • Maximum Power Point
  • Photovoltaic System
  • Partial Shading

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Figure 1. Schematic diagram of PV system with MPPT.

EXPECTED SIMULATION RESULTS:

 

Figure 2. P-V characteristics at different irradiations.

Figure 3. P-V characteristics when partial shading from 1000 to 600 Watt/m2.

Figure 4. Output of fuzzy at1000 Watt/m2.

Figure 5. Output of fuzzy controller. (a) Full shading from 600 to 300 Watt/m2; (b) Full shading from 700 to 400 Watt/m2; (c) Full shading from 900 to 400 Watt/m2; (d) Increasing shading from 300 to 800 Watt/m2.

Figure 6. Comparison between fuzzy and P & O partial shading (partial shading 1000 to 800 Watt/m2).

CONCLUSION:

 In this study, FLC has been developed to track the maximum power point of PV system. PV panel, boost converter with FLC connected to a resistive load has been simulated using Matlab/Simulink. Simulation results have been compared to nominal power values. The proposed system showed its ability to reach MMP under uniform irradiation, sudden changes of irradiation, and partial shading. Simulation results have shown that using FLC has great advantages over conventional methods. It is found that Fuzzy controller always finds the global MPP. It is found that fuzzy logic systems are easily implemented with minimal oscillations with fast convergence around the desired MP

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 [1] Devabhaktuni, V., Alam, M., Reddy Depuru, S.S.S., Green II, R.C., Nims, D. and Near, C. (2013) Solar Energy: Trends and Enabling Technologies. Renewable and Sustainable Energy Reviews, 19, 555-556. http://dx.doi.org/10.1016/j.rser.2012.11.024

[2] Bataineh, K.M. and Dalalah, D. (2012) Optimal Configuration for Design of Stand-Alone PV System. Smart Grid and Renewable Energy, 3, 139-147. http://dx.doi.org/10.4236/sgre.2012.32020

[3] Bataineh, K. and Dalalah, D. (2013) Assessment of Wind Energy Potential for Selected Areas in Jordan. Journal of Renewable Energy, 59, 75-81.

[4] Bataineh, K.M. and Hamzeh, A. (2014) Efficient Maximum Power Point Tracking Algorithm for PV Application under Rapid Changing Weather Condition. ISRN Renewable Energy, 2014, Article ID: 673840. http://dx.doi.org/10.1155/2014/673840

[5] International Energy Agency (2010) Trends in Photovoltaic Applications. Survey Report of Selected IEA Countries between 1992 and 2009. http://www.ieapvps.org/products/download/Trends-in Photovoltaic_2010.pdf

Maximum Power Point

Adaptive fuzzy controller based MPPT for photovoltaic systems

 

ABSTRACT:

This paper presents an intelligent approach to optimize the performances of photovoltaic systems. The system consists of a PV panel, a DC–DC boost converter, a maximum power point tracker controller and a resistive load. The key idea of the proposed approach is the use of a fuzzy controller with an adaptive gain as a maximum power point tracker. The proposed controller integrates two different rule bases. The first is used to adjust the duty cycle of the boost converter as in the case of a conventional fuzzy controller while the second rule base is designed for an online adjusting of the controller’s gain. The performances of the adaptive fuzzy controller are compared with those obtained using a conventional fuzzy controllers with different gains and in each case, the proposed controller outperforms its conventional counterpart.

KEYWORDS:

  1. PV panel
  2. Adaptive fuzzy controller
  3. Output scaling factor
  4. Fuzzy rules

 SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

Fig.1.Block diagram of the adaptive fuzzy controller.

EXPECTED SIMULATION RESULTS:

Fig. 2.Comparative study under changing atmospheric conditions.

 CONCLUSION:

In this work, an adaptive fuzzy controller is used to track the maximum power point in photovoltaic systems. The gain of the controller is adjusted online by fuzzy rules defined on error and change of error. Simulation results show that the proposed controller can track the maximum power point with better performances when compared to its conventional counterpart. Thus the introducing of an adaptive gain in the structure of conventional fuzzy controllers is well justified.

REFERENCES:

[1] Xiao W, Dunford WG. A modified adaptive hill climbing MPPT method for photovoltaic power systems. In: 35th Annual IEEE Power Electronics, Specialists Conference, Aachen, Germany; 2004. p. 1957–63.

[2] Femia N, Petrone G, Spagnuolo G, Vitelli M. Optimization of perturb and observe maximum power point tracking method. IEEE Trans Power Electron 2004;20(4):16–9.

[3] Kuo YC, Liang TJ, Chen JF. Novel maximum power point tracking controller for photovoltaic energy conversion system. IEEE Trans Ind Electron 2001;48(3):594–601.

[4] Liao CC. Genetic k-means algorithm based RBF network for photovoltaic MPP prediction. Energy 2010;35:529–36.

[5] Hadji S, Krimand F, Gaubert JP. Development of an algorithm of maximum power point tracking for photovoltaic systems using genetic algorithms. In: 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA); 2011. p. 43–6.