Open Access

A review study of photovoltaic array maximum power tracking algorithms

Renewables: Wind, Water, and Solar20163:3

https://doi.org/10.1186/s40807-016-0022-8

Received: 26 November 2015

Accepted: 18 January 2016

Published: 18 February 2016

Abstract

There are numerous maximum power point tracking (MPPT) algorithms for improving the energy efficiency of solar photovoltaic (PV) systems. The main differences between these algorithms are digital or analog implementation, simplicity of the design, sensor requirements, convergence speed, range of effectiveness, as well as hardware costs. Therefore, choosing the right algorithm is very important to the users, because it affects the electrical efficiency of PV system and reduces the costs by decreasing the number of solar panels needed to get the desired power. This paper provides the comparison of 62 different techniques used in tracking the maximum power based on literature survey. This paper is intended to be a reference for PV systems users.

Keywords

Maximum power point tracking system (MPPT)Photovoltaic (PV)System efficiency

Background

Recently, renewable energy technology has been swiftly developed where it has an important role in clean energy application. An important type of renewable energy is solar energy that produces electrical energy directly using PV modules supported by MPPT algorithm to maximize the output power. The objective of obtaining MPP in PV systems is to regulate the actual operating voltage of PV panels to the voltage at MPP, by adjusting the output power of the inverter (Libo et al. 2007).

In literature, there are plentiful MPPT methods as in (Esram and Chapman 2007; Ali et al. 2012; Jusoh et al. 2014; Kamarzaman and Tan 2014; Liu et al. 2015; Lyden and Haque 2015). Kamarzaman and Tan (2014) used four categories to review MPPT algorithms as follows: conventional MPPT algorithms (perturb and observation P&O and incremental conductance IC); hill-climbing (open circuit voltage and short circuit current); ripple correlation current; and stochastic-based MPPT algorithms (particle swarm optimization, fuzzy logic controller, artificial neural network, and differential evolution). Liu et al. (2015) gave a review of MPPT techniques for use in partially shaded conditions. Lyden et al. (2015) divided the tracking techniques to three types: conventional MPPT techniques, global MPPT techniques, and power electronics-based approaches.

This paper presents a brief comparison between different techniques to help the users to choose an MPPT technique for a particular application. The comparison between the MPPT methods includes cost, analog or digital implementation, sensor dependence, convergence speed, hardware complexity, and effectiveness.

Second section illustrates the statement of the problem. Comparison between different MPPT techniques is given in third section. In fourth section, the methodology is presented followed by the fifth section in which results are introduced and three most popular algorithms are presented. Finally, the conclusion is presented in the last section.

Statement of the problem

In medium- and large-scale systems, sun tracking or MPPT or both are used to obtain maximum power (Tse et al. 2002). MPPT systems are considered the most popular in all PV systems. MPPT systems are used to reach MPP automatically from solar modules. That is the PV system will work at its maximum efficiency. The amount of energy gained by PV system depends on several factors including level of irradiance, temperature, and partial shading. Thus, these algorithms should consider the changes in these factors. The characteristic current–voltage curve and power–voltage curve are displayed in Fig. 1. These characteristic curves present the parameters that describe the operation of the PV cell such as the open-circuit voltage V OC , short circuit current I SC , and the cell voltage, current, and power at the maximum power point, V MPP , I MPP , and P MPP , respectively.
Fig. 1

Characteristic power–voltage and current–voltage curves (Tse et al. 2002)

In addition, the fill factor FF and efficiency η are considered. FF measures the quality of the PV array. It is the ratio of the actual MPP (P MAX ) to the product of V OC and I SC as in (1) (Chen 2011).
$$FF = \frac{{P_{MAX} }}{{P_{T} }} = \frac{{I_{MP} V_{MP} }}{{I_{SC} V_{OC} }}$$
(1)

While the efficiency, η, of a solar cell is defined as the ratio of the output electric power over the input solar radiation power under standard illumination conditions at the maximum power point (Chen 2011).

Comparison between MPPT techniques

The MPPT techniques vary in many aspects, which might help the users to decide the system that suits their unique applications. These parameters include hardware implementation, sensor, convergence speed, multiple local maximum, cost, application, and dependency on array parameter. Hardware implementation is simply the type of circuit: analog or digital (Esram and Chapman 2007). Sensors and their numbers affect the decision makers to decide which MPPT to use. The more precise MPPT requires more sensors (Reported issued by National Instruments 2009). Usually, it is easier to sense voltage than current. The irradiance or temperature sensors are very expensive and uncommon (Faranda and Leva 2008).

Convergence speed is the time taken to reach the MPP (Walker et al. 2011). For a high-performance MPPT system, the time taken to converge to the required operating voltage or current should be low. The lower time and periodic tuning taken to reach the MPP minimize power losses and maximize efficiency.

The ability to detect multiple local maxima when the system is under different irradiance levels is another important parameter. The power loss can reach 70 % under partial shading condition, if a local maximum is tracked instead of the real MPP (Reported issued by National Instruments 2009; Ji et al. 2009).

Performance cost is another parameter that concerns the users. It is usually cheaper to use analog system than digital system. Moreover, the number and type of sensors, using other power or electronic components, add extra cost to the system (Faranda and Leva 2008).

Different MPPTs are suitable for various applications. Depending on the application, different aspects may be considered important when choosing the PV system. As an example, in space satellites and orbital stations applications that involve a large amount of money, the costs and complexity of the MPP tracker are not as important as its performance and reliability. The tracker should be able to continuously track the true MPP in minimum amount of time and should not require periodic tuning (Khatib et al 2010).

The MPPT system might be independent (direct) or dependent (indirect) on array parameters. The direct methods use PV voltage and/or current measurements. These direct methods have the advantage of being independent from the prior knowledge of the PV array configuration and parameter values for their implementation. Thus, the operating point is independent of irradiance, temperature, or degradation levels. The indirect methods are based on the use of a database of parameters that include data of typical PV curves of PV systems for different irradiances and temperatures, or on the use of mathematical functions obtained from empirical data to estimate the MPP (Khatib et al. 2010; Jain and Agarwa 2007). Table 1 summarizes the most important characteristics of MPPT algorithm that is used to compare between different techniques.
Table 1

Parameters used to compare MPPT algorithms

Parameters

Statement

PV array dependent/independent

Methods can be applied to any PV array with or without the knowledge of its configuration and parameter values

True MPPT

The MPPT algorithm can operate at maxima or others. If the actual MPP is not the true MPP, then the output power will be less than the expected one actually

Types of circuitry

Analog or digital

Periodic tuning

Is there an oscillation around the MPP or not

Convergence speed

It is the amount of time required to reach MPP

Implementation complexity

This standard describes the method in general

Sensors

It depends on the number of variables under consideration

Methodology

In this work, we conducted a literature review to what is available in terms of MPP tracking algorithms. We analyzed theoretically the work presented in each paper and fetch the parameters as indicated in Table 1. We collected 45 different algorithms. The differences between 45 MPPT algorithms are listed in Table 2. Table 2 is an extended work to what have been presented in (Ali et al. 2012). Further, algorithms are collected from other resources.
Table 2

Comparison between different MPPT algorithms (V voltage, I current, Ir irradiance)

 

MPPT technique

PV array dependence

True MPPT

Analog/digital

Periodic tuning

Convergence speed

Implementation complexity

Sensors

1.

Hill-climbing P&O (Sera et al. 2006; Busa et al. 2012; Jusoh et al. 2014; Kamarzaman and Tan 2014)

No

Yes

Both

No

Vary

Low

V and I

2.

Incremental conductance (Esram and Chapman 2007; Yadav et al. 2012; Rashid 2011; Zainudin and Mekhilef 2010; Jusoh et al. 2014; Kamarzaman and Tan 2014)

No

Yes

Digital

No

Vary

Medium

V and I

3.

Fractional Voc (Kumari and Babu 2011; Lee 2011; Jusoh et al. 2014; Kamarzaman and Tan 2014)

Yes

No

Both

Yes

Medium

Low

V

4.

Fractional Isc (Kumari and Babu 2011; Lee 2011; Jusoh et al. 2014; Kamarzaman and Tan 2014)

Yes

No

Both

Yes

Medium

Medium

I

5.

Fuzzy logic control (Ali et al. 2012; Rezaei and Gholamian 2013; Takun et al. 2011; Rahmani et al. 2013; Jusoh et al. 2014; Kamarzaman and Tan 2014).

Yes

Yes

Digital

Yes

Fast

High

Varies

6.

Neural network (Ali et al. 2012; Kamarzaman and Tan 2014)

Yes

Yes

Digital

Yes

Fast

High

Varies

7.

RCC (Ali et al. 2012; Jusoh et al. 2014)

No

Yes

Analog

No

Fast

Low

V and I

8.

Current weep (Ali et al. 2012)

Yes

Yes

Digital

Yes

Slow

High

V and I

9.

DC link capacitor droop control (Ali et al. 2012)

No

No

Both

No

Medium

Low

V

10.

Load I or V maximization (Ali et al. 2012)

No

No

Analog

No

Fast

Low

V and I

11.

dP/dV or dP/dI feedback control (Ali et al. 2012)

No

Yes

Digital

No

Fast

Medium

V and I

12.

β method (Ali et al. 2012)

Yes

Yes

Digital

No

Fast

High

V and I

13.

System oscillation method (Ali et al. 2012)

Yes

Yes

Analog

No

N/A

Low

V

14.

Constant voltage tracker (Ali et al. 2012; Coelho et al. 2010)

Yes

No

Digital

Yes

Medium

Low

V

15.

Lookup table method (Ali et al. 2012; Abdulmajeed et al. 2013)

Yes

Yes

Digital

Yes

Fast

Medium

V, I, T,

and Ir

16.

Online MPP search algorithm (Ali et al. 2012)

No

Yes

Digital

No

Fast

High

V and I

17.

Array reconfiguration (Ali et al. 2012; Israel 2015)

Yes

No

Digital

Yes

Slow

High

V and I

18.

Linear current control (Ali et al. 2012)

Yes

No

Digital

Yes

Fast

Medium

Ir

19.

IMPP and VMPP computation (Morales 2010)

Yes

Yes

Digital

Yes

N/A

Medium

Ir and T

20.

State based MPPT (Ali et al. 2012)

Yes

Yes

Both

Yes

Fast

High

V and I

21.

OCC MPPT (Ali et al. 2012)

Yes

No

Both

Yes

Fast

Medium

I

22.

BFV (Ali et al. 2012)

Yes

No

Both

Yes

N/A

Low

None

23.

LRCM (Esram and Chapman 2007)

Yes

No

Digital

No

N/A

High

V and I

24.

Slide control (Esram and Chapman 2007; Ali et al. 2012; Tse et al. 2002; Chen 2011; Reported issued by National Instruments 2009; Faranda and Leva 2008; Walker et al. 2011; Ji et al. 2009; Khatib et al. 2010; Jain and Agarwa 2007; Sera et al. 2006; Busa et al. 2012; Yadav et al. 2012; Rashid 2011; Zainudin and Mekhilef 2010; Kumari and Babu 2011; Lee 2011; Rezaei and Gholamian 2013; Takun et al. 2011; Rahmani et al. 2013; Coelho et al. 2010; Abdulmajeed et al. 2013; Israel 2015; Morales 2010; Ghazanfari and Farsangi 2013)

No

Yes

Digital

No

Fast

Medium

V and I

25.

Temperature method (Ali et al. 2012; Faranda and Leva 2008; Brito et al. 2013)

Yes

Yes

Digital

Yes

Medium

Low

V and T

26.

IC Based On PI (Brito et al. March 2013; Lyden and Haque 2015)

No

Yes

Digital

No

Fast

Medium

V & I

27.

Three point weight comparison (Ali et al. 2012)(Walker et al. 2011; Jiang et al. 2005).

No

Yes

Digital

No

Low

Low

V and I

28.

POS control (Ali et al. 2012)

No

Yes

Digital

No

N/A

Low

I

29.

Biological swarm chasing MPPT (Ali et al. 2012)

No

Yes

Digital

No

Varies

High

V, I, T and Ir,

30.

Variable inductor MPPT (Ali et al. 2012)

No

Yes

Digital

No

Varies

Medium

V and I

31.

INR method (Ali et al. 2012)

No

Yes

Digital

No

High

Medium

V and I

32.

Parasitic capacitances (Zainudin and Mekhilef 2010; Rekioua and Matagne 2012; Hohm and Ropp 2003).

No

Yes

Analog

No

High

Low

V and I

33.

dP-P&O MPPT (Sera et al. 2006; Mastromauro et al. 2012)

No

Yes

Digital

No

High

Medium

V and I

34.

Modified INC algorithm (Mastromauro et al. 2012)

No

Yes

Digital

No

Medium

High

V and I

35.

Pilot cell (Kumar et al. 2013)

Yes

No

Both

Yes

Medium

Low

V and I

36.

Modified Perturb and Observe (Liu et al. 2004)

No

Yes

Digital

No

High

Medium

V and I

37.

Estimate, Perturb and Perturb (Liu et al. 2004; Yafaoui et al. 2007)

No

Yes

Digital

No

High

Medium

V and I

38.

Numerical method quadratic interpolation (QI) (Hu et al. 2009)

No

Yes

Digital

No

High

Medium

V and I

39.

MPP locus characterization (Israel 2015) (Vladimir et al. 2009)

 

Yes

  

High

Low

V and I

40.

CVT + INC CON (P&O) + VSS method (Go et al. 2011)

Yes

Yes

Both

No

High

Medium

V

41.

Piecewise linear approximation with temperature compensated method (Yang and Yan 2013)

Yes

Yes

Both

Yes

High

Low

V, I, T, and Ir,

42.

Particle swarm optimization PSO algorithm (Mandour and Elamvazuthi 2013; Lyden and Haque 2015)

No

Yes

Digital

No

High

Low

V and I

43.

PSO-INC structure (Mandour and Elamvazuthi 2013)

No

Yes

Digital

No

High

Low

V and I

44.

Dual carrier chaos search algorithm (Zhou et al. 2012)

No

Yes

Digital

No

High

Medium

V and I

45.

Algorithm for stimulated annealing (SA) (Rahman et al. 2013)

Yes

Yes

Digital

No

High

High

V and I

46.

VH-P&O MPPT algorithm (Abdalla et al. 2011)

No

Yes

Digital

No

Medium

Medium

V

47.

Artificial neural network (ANN) based P&O MPPT (Amrouche et al. 2007; Kamarzaman and Tan 2014)

No

Yes

Both

No

High

Medium

V and I

48.

Ant colony algorithm (Qiang and Nan 2013)

No

Yes

Digital

No

High

Medium

V and I

49.

Variable DC link voltage algorithm (Lee and Lee 2013)

No

Yes

Digital

No

Medium

Medium

V

50.

Extremum seeking control method (ESC) (Reisi et al. 2013)

No

Yes

Both

No

Fast

Medium

V and I

51.

Gauss–Newton method (Xiao et al. 2007)

No

Yes

Digital

No

Fast

Low

V and I

52.

Steepest-descent method (Xiao et al. 2007)

No

Yes

Digital

No

Fast

Medium

V and I

53.

Analytic method (Rodriguez and Amaratunga 2007)

Yes

No

Both

Yes

Medium

High

V and I

54.

Azab method (Azab 2008)

Yes

Yes

Digital

Yes

Medium

Low

55.

Newton-like extremum seeking control method (Zazo et al. 2012)

No

Yes

Analog

No

Fast

High

V

56.

Sinusoidal extremum seeking control method (Leyva and Olalla 2012)

No

Yes

Analog

Yes

Fast

High

V and I

57.

low-power (<1 W) (Lapeña et al. 2010)

Yes

Yes

Analog

No

Fast

Low

V

58.

GA-optimized ANN (Kulaksiz and Akkaya 2012)

No

Yes

Digital

Yes

Fast

High

V, T and Ir

59.

Differential evolution (DE) (Kamarzaman and Tan 2014)

No

Yes

Digital

No

Fast

Low

V and I

60.

Ripple correlation control (Lyden and Haque 2015)

No

No

 

No

Fast

Low

61.

Chaos search (Lyden and Haque 2015)

No

Yes

 

No

Fast

Medium

62.

Simulated annealing (Lyden and Haque 2015)

No

Yes

 

No

Varies

Low/moderate

Results

The comparison between 62 algorithms is shown in Table 2. According to the table, the most common algorithms are perturb and observe (P&O)/”hill-climbing,” incremental conductance algorithm, and fuzzy logic controller (FLC).

Below is a quick review of these three well-known algorithms.

Perturb and Observe (P&O)/”hill-climbing”

The P&O is the most popular for its low cost, ease of implantation, simple structure, and few measured parameters, which are required. It only measures the voltage (V) and current (I) of the PV array. PV system controller changes PV array output with a smaller step in each control cycle. The step size is generally fixed, while mode can be increased or decreased. Both PV array output voltage and output current can be the control object; this process is called “perturbation.” It depends on the fact that the derivative of power with respect to voltage is zero at MPP point (Sera et al. 2006; Busa et al. 2012). This method fails under rapidly changed atmospheric conditions and has a slow response speed oscillation around the MPP (Sera et al. 2006).

Incremental conductance algorithm

The incremental conductance method is based on the fact that the sum of the instantaneous conductance (I/V) and the incremental conductance is zero at MPP. Figure 2 shows the slope of the PV array power curve compared to (I/V). Thus, incremental conductance can determine that the MPPT has reached the MPP and stop perturbing the operating point of the PV array as explained in Fig. 2.
Fig. 2

Slope of the P–V array power curve (Yadav et al. 2012)

Although incremental conductance is an improved version of P&O, it can track rapidly increasing and decreasing irradiance conditions with higher accuracy than P&O. However, this algorithm is more complex than P&O. This increases computational time and slows down the sampling frequency of the array voltage and current (Esram and Chapman 2007; Chen 2011; Yadav et al. 2012; Rashid 2011; Zainudin and Mekhilef 2010).

Fuzzy logic controller (FLC)

FLC consists of four categories as fuzzification, inference engine, rule base, and defuzzification. The numerical input variables are converted into fuzzy variable known as linguistic variable based on a membership function similar to Fig. 3. In this case, five fuzzy levels are used: NB (negative big), NS (negative small), ZE (zero), PS (positive small), and PB (positive big). For more accuracy seven fuzzy levels are used. In Fig. 3, a and b are based on the range of values of the numerical variable. Conventional fuzzy MPPT consists of two inputs and one output. The two input variables are the error (E) and the error change (ΔE), at sampled times k. The input E (k) shows if the load operation point at the instant k is located on the left or on the right of the maximum power point on the PV characteristic, while the input ΔE(k) expresses the moving direction of this point (Esram and Chapman 2007; Ali et al. 2012; Faranda and Leva 2008; Brito et al. 2013).
Fig. 3

Membership function (Esram and Chapman 2007)

Conclusion

In this work, we presented a comparison of 62 MPPT algorithms. In the comparison, we used several parameters including the complexity of the system, number of sensors, kind of circuitry (digital or analog), tuning, convergence speed, and the dependency of the parameters. The results are shown in the table to serve the users to choose the suitable system that suits their specific applications. Moreover, we presented a summary of three most common MPPT algorithms.

Declarations

Authors’ contributions

HJE and TS originated the problem idea. KM collected and demonstrated the idea. RE contributed in writing and reviewing the paper. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Electrical Engineering Department, Islamic University of Gaza
(2)
Physics Department, Al-Aqsa University
(3)
Dept of Electrical Engineering, Tampere University of Technology

References

  1. Abdalla, I., Zhang, L., & Corda, J. (2011). “Voltage-Hold Perturbation & Observation Maximum Power Point Tracking Algorithm (VH-P&O MPPT) for Improved Tracking over the Transient Atmospheric Changes,” presented at Power Electronics and Applications (EPE 2011) of the 2011-14th European Conference, (pp.1–10).Google Scholar
  2. Abdulmajeed, Q. M., Kazem, H. A., Mazin, H., Abd Malek, M. F., Maizana, D., Alwaeli, A. H. A., Albadi, M. H., Sopian, K., & Said Al Busaidi, A. (2013). “Photovoltaic maximum tracking power point system: review and research challenges,” International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), Vol. 2, No. 5. (pp.16–21).Google Scholar
  3. Ali, A., Saied, M., Mostafa, M., & Moneim, T. (2012). A survey of maximum PPT techniques of PV Systems. Energytech, 2012 IEEE.Google Scholar
  4. Amrouche, B., Belhamel, M., & Guessoum, A. (2007). ”Artificial intelligence based P&O MPPT method for photovoltaic systems,” Revue des Energies Renouvelables ICRESD, Vol. 7, (pp. 11–16).Google Scholar
  5. Azab, M. (2008). A new maximum power point tracking for photovoltaic systems. World Academy of Science, Engineering and Technology, 44, 571–574.Google Scholar
  6. Brito, M., Galotto, L., Sampaio, L., Melo, G., & Canesin, C. (2013). Evaluation of the Main MPPT Techniques for Photovoltaic Applications. IEEE Transactions on Industrial Electronics, 60(3), 1156–1167.View ArticleGoogle Scholar
  7. Busa, V., Narsingoju, K. K., & Kumar, G. V. (2012). Simulation analysis of maximum power control of photo voltaic power system. International Journal on Advanced Electrical and Electronics Engineering (IJAEEE), 1(1), 9–14.Google Scholar
  8. Chen, C. J. (2011). Physics of solar energy. New Jersey: Wiley.View ArticleGoogle Scholar
  9. Coelho, R., Concer, F., & Martins, D. (2010). ”A MPPT Approach Based on Temperature Measurements Applied in PV Systems,” IEEE/IAS International Conference on Industry Applications, (pp. 1–6).Google Scholar
  10. Esram, T., & Chapman, P. (2007). Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on Energy Conversion, 22(2), 439–449.View ArticleGoogle Scholar
  11. Faranda, R., & Leva, S. (2008a). Energy comparison of MPPT techniques for PV systems. Wseas Transaction on Power Systems, 3, 446–455.Google Scholar
  12. Faranda, R., & Leva, S. (2008b). Energy comparison of MPPT techniques for PV systems. Wseas Transaction on Power Systems, 3, 446–455.Google Scholar
  13. Ghazanfari, J., & Farsangi, M. (2013). Maximum power point tracking using sliding mode control for photovoltaic array. Iranian Journal of Electrical & Electronic Engineering, 9(3), 189–196.Google Scholar
  14. Go, S., Ahn, S., Choi, J., Jung, W., Yun Yun, S., & Song, II. “Simulation and Analysis of Existing MPPT Control Methods in a PV Generation System.” Journal of International Council on Electrical Engineering, Vol. 1, No. 4, pp. 446-451, 2011.Google Scholar
  15. Hohm, D. P., & Ropp, M. E. (2003). ”Comparative Study of Maximum Power Point Tracking Algorithms,” Progress in Photovoltaic: Research and Application, (pp. 47–62).Google Scholar
  16. Hu, J., Zhang, J., & Wu, H. (2009). “Novel MPPT control algorithm based on numerical calculation for PV generation systems,” presented at Power Electronics and Motion Control Conference (pp. 2103–2107). China: Baoding.Google Scholar
  17. Israel, J. (2015). “Summary of maximum power point tracking methods for photovoltaic cells,” electronic matter, retrieved on May 2015.Google Scholar
  18. Jain, S., & Agarwa, V. (2007). Comparison of the performance of maximum power point tracking schemes applied to single-stage grid-connected photovoltaic systems. The Institution of Engineering and Technology Power Appl., 1(5), 753–762.Google Scholar
  19. Ji, Y. H., Jung, D. Y., Won, C. Y., Lee, B. K., & Kim, J. W. (2009). Maximum power point tracking method for PV array under partially shaded condition. Energy Conversion Congress and Exposition, 2009. ECCE 2009. IEEE. (pp. 307–312).Google Scholar
  20. Jiang, J., Huang, T., Hsiao, Y., & Chen, Ch. (2005). Maximum power tracking for photovoltaic power systems. Tamkang Journal of Science and Engineering, 8(2), 147–153.Google Scholar
  21. Jusoh, A., Sutikno, T., Guan, T. K., & Mekhilef, S. (2014). A Review on favourable maximum power point tracking systems in solar energy application. Telkomnika, 12(1), 6–22.View ArticleGoogle Scholar
  22. Kamarzaman, N., & Tan, C. W. (2014). A comprehensive review of maximum power point tracking algorithms for photovoltaic systems. Renewable and Sustainable Energy Reviews, 37, 585–598.View ArticleGoogle Scholar
  23. Khatib, T. T. N., Mohamed, A., & Amim, N. (2010). An improved indirect maximum power point tracking method for standalone photovoltaic systems,” presented at Proceedings of the 9th WSEAS International Conference on Applications of Electrical Engineering, Selangor, Malaysia, pp. (56–62).Google Scholar
  24. Kulaksiz, A., & Akkaya, R. (2012). Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system. Turk J Elec Eng and Comp Sci, 20(2), 241–254.Google Scholar
  25. Kumar, Ch., Dinesh, T., & Babu, S. (2013). Design and Modelling of PV System and Different MPPT Algorithms. International Journal of Engineering Trends and Technology (IJETT), 4, 4104–4112.Google Scholar
  26. Kumari, J., & Babu, Ch. (2011). Comparison of maximum power point tracking algorithms for photovoltaic system. International Journal of Advances in Engineering and Technology, 1, 133–148.Google Scholar
  27. Lapeña, O., Penella, M., & Gasulla, M. (2010). A New MPPT Method for Low-Power Solar Energy Harvesting. IEEE Transactions on Industrial Electronics, 57(9), 3129–3138.View ArticleGoogle Scholar
  28. Lee, J. (2011). Advanced electrical and electronic engineering. Berlin: Springer.View ArticleGoogle Scholar
  29. Lee, J. S., & Lee, K. B. (2013). Variable DC-link voltage algorithm with a wide range of maximum power point tracking for a two-string PV System. Energies, 6, 58–78.View ArticleGoogle Scholar
  30. Leyva, R., Olalla Martinez, C., Zazo, H., Cabal, C., Cid-Pastor, A., Queinnec, I., & Alonso, C. (2012). “MPPT Based on Sinusoidal Extremum-Seeking Control in PV Generation,”. International Journal of Photoenergy, 2012, 1–7.View ArticleGoogle Scholar
  31. Libo, W., Zhengming, Z., & Jianzheng, L. (2007). A single-stage three-phase grid-connected photovoltaic system with modified MPPT method and reactive power compensation. IEEE Transactions on Energy Conversion, 22(4), 881–886.View ArticleGoogle Scholar
  32. Liu, Y., Chen, J., & Huang, J. (2015). A review of maximum power point tracking techniques for use in partially shaded conditions. Renewable and Sustainable Energy Reviews, 41, 436–453.View ArticleGoogle Scholar
  33. Liu, C., Wu, B., & Cheung R. (2004). “Advanced Algorithm for MPPT Control of Photovoltaic System,” presented at Canadian Solar Buildings Conference, Montreal.Google Scholar
  34. Lyden, S., & Haque, M. E. (2015). ” Maximum Power Point Tracking techniques for photovoltaic systems: A comprehensive review and comparative analysis,” Vol. 52, (pp.1504–1518).Google Scholar
  35. Mandour, R., & Elamvazuthi, I. (2013). Optimization of maximum power point tracking (MPPT) of photovoltaic system using artificial intelligence (AI) algorithms.” Journal of Emerging Trends in Computing and Information Sciences, Vol. 4, No. 8.Google Scholar
  36. Mastromauro, R., Liserre, M., & Aquila, A. (2012). Control issues in single-stage photovoltaic systems: MPPT, current and voltage control. IEEE Transactions on Industrial Informatics, 8(2), 241–254.View ArticleGoogle Scholar
  37. Morales, D. S. (2010). “Maximum power point tracking algorithms for photovoltaic applications, “A thesis presented to the faculty of electronics. Communications and Automation: Aalto University, Finland.Google Scholar
  38. Qiang, F., & Nan, T. (2013). A Strategy Research on MPPT Technique in Photovoltaic Power Generation System. Telkomnika, 11(12), 7627–7633.View ArticleGoogle Scholar
  39. Rahman, Md, Poddar, S., Mamun, M., Mahmud, S., & Yeasin, Md. (2013). Efficiency comparison between different algorithms for maximum power point tracker of a solar system. International Journal of Scientific Research and Management (IJSRM), 1, 157–167.Google Scholar
  40. Rahmani, R., Seyedmahmoudian, M., Mekhilef, S., & Yusof, R. (2013). Implementation of fuzzy logic maximum power point tracking controller for photovoltaic system. American Journal of Applied Sciences, 10, 209–218.View ArticleGoogle Scholar
  41. Rashid, M. H. (2011). Power Electronic Handbook (3rd ed.). USA: Butterworth-Heinemann.Google Scholar
  42. Reisi, A., Moradi, M., & Jamasb, S. (2013). Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review. Renewable and Sustainable Energy Reviews, 19, 433–443.View ArticleGoogle Scholar
  43. Rekioua, D., & Matagne, E. (2012). Optimization of photovoltaic power systems modelization, simulation and control. London: Springer.View ArticleGoogle Scholar
  44. Reported issued by National Instruments. (2009). Maximum power point tracking. http://www.ni.com/white-paper/8106/en.
  45. Rezaei, A., & Gholamian, S. A. (2013). Optimization of New Fuzzy Logic Controller by Genetic Algorithm for Maximum Power Point Tracking in Photovoltaic System. Journal of Science and Technology, 9(1), 9–16.Google Scholar
  46. Rodriguez, C., & Amaratunga, G. (2007). Analytic solution to the photovoltaic maximum power point problem. IEEE Transactions on Circuits and System, 54(9), 2054–2060.View ArticleMathSciNetGoogle Scholar
  47. Sera, D., Kerekes, T., Teodorescu, R., & Blaabjerg, F. (2006a). Improved MPPT algorithms for rapidly changing environmental conditions presented at Power Electronics and Motion Control Conference, 2006. EPE-PEMC, 2006, 1614–1619.Google Scholar
  48. Sera, D., Kerekes, T., Teodorescu, R., & Blaabjerg, F. (2006b). “Improved MPPT Algorithms for Rapidly Changing Environmental Conditions,” presented at Power Electronics and Motion Control Conference, 2006. EPE-PEMC, 2006, 1614–1619.Google Scholar
  49. Takun, P., Kaitwanidvilai, S., & Jettanasen, C. (2011)“Maximum power point tracking using fuzzy logic control for photovoltaic systems,” presented at International Multi Conference of Engineers and Computer Scientists, Hong Kong, Vol. 2.Google Scholar
  50. Tse, K. K., Ho, M. T., Chung, H. S.-H., & Hui, S. Y. (2002). “A novel maximum power point tracker for PV panels using switching frequency modulation,”. IEEE Transactions on Power Electronics, 17(6), 980–989.View ArticleGoogle Scholar
  51. Vladimir V. R., Scarpa, S., Buso, G., & Spiazzi. (2009). “Low-complexity MPPT technique exploiting the PV module MPP locus characterization.” IEEE Transactions on Industrial Electronics, Vol. 56, No. 5.Google Scholar
  52. Walker, S., Sooriyaarachchi, N., Liyanage, N., Abeynayake, P., & Abeyratne, S. (2011). Comparative analysis of speed of convergence of MPPT techniques. presented at 6th International Conference on Industrial and Information Systems, Sri Lanka, (pp. 522–526).Google Scholar
  53. Walker, S., Sooriyaarachchi, N., Liyanage, N., Abeynayake, P., & Abeyratne, S. (2011)”Comparative Analysis of Speed of Convergence of MPPT Techniques,” presented at 6th International Conference on Industrial and Information Systems, Sri Lanka, (pp. 522-526).Google Scholar
  54. Xiao, W., Dunford, W., Palmer, P., & Capel, A. (2007). Application of centered differentiation and steepest descent to maximum power point tracking. IEEE Transactions on Industrial Electronics, 54(5), 2539–2549.View ArticleGoogle Scholar
  55. Yadav, A., Thirumaliah, S., & Haritha, G. (2012). Comparison of MPPT algorithms for DC–DC converters based PV systems. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 1, 18–23.Google Scholar
  56. Yafaoui, A., Wu, B., & Cheung, R. (2007). ” Implementation of Maximum Power Point Tracking Algorithm For Residential Photovoltaic Systems,” presented at 2nd Canadian Solar Buildings Conference, Calgary.Google Scholar
  57. Yang, Y., & Yan, Z. (2013). A MPPT method using piecewise linear approximation and temperature compensation. Journal of Computational Information Systems, 9(21), 8639–8647.Google Scholar
  58. Zainudin, H., & Mekhilef, S. (2010). “Comparison study of maximum power point tracker techniques for PV systems,” presented at international middle east power systems conference (MEPCON’10) (pp. 750–755). Egypt: Cairo University.Google Scholar
  59. Zazo, H., Leyva, R., & Castillo, E. (2012). “Analysis of Newton-Like Extremum Seeking Control in Photovoltaic Panels,” presented at International Conference on Renewable Energies and Power Quality (ICREPQ‘12), Santiago de Compostela, Spain.Google Scholar
  60. Zhou, L., Chen, Y., Liu, Q., & Wu, J. (2012). Maximum power point tracking (MPPT) control of a photovoltaic system based on dual carrier chaotic search. J Control Theory Appl, 10(2), 244–250.View ArticleMathSciNetGoogle Scholar

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