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Maximum Power Point Tracking (MPPT) algorithms are now widely used in PV systems independently of the weather conditions. In function of the application, a DC-DC converter topology is chosen without any previous performance test under normal weather conditions. This paper proposes an experimental evaluation of MPPT algorithms according to DC-DC converters topologies, under normal operation conditions. Four widely used MPPT algorithms i.e. Perturb and Observe (P & O), Hill Climbing (HC), Fixed step Increment of Conductance (INCF) and Variable step Increment of Conductance (INCV) are implemented using two topologies of DC-DC converters i.e. buck and boost converters. As input variables to the PV systems, recorded irradiance and temperature, and extracted photovoltaic parameters (ideality factor, series resistance and reverse saturation current) were used. The obtained results show that buck converter has a lot of power losses when controlled by each of the four MPPT algorithms. Meanwhile, boost converter presents a stable output power during the whole day. Once more, the results show that INCV algorithm has the best performance.

Energy extracted from a solar panel depends on weather conditions which bring it to be an intermittent energy source [

• A buck converter with the DC output voltage lower than or equal to the DC input voltage.

• A boost converter with the DC output voltage greater than or equal to the DC input voltage.

• A buck-boost converter with the DC output voltage greater than or lower than or equal to the DC input voltage.

Since 1968 with the first MPPT algorithm, many other types of MPPT algorithms have been developed: Perturb and Observe (P & O), Incremental Conductance (INC), Hill Climbing (HC), Voltage or Current fraction, Fussy logic, Neural network, Ripple correlation control (RCC), etc. [

This paper evaluates four of the various MPPT algorithms according to the buck and the boost converter, under normal weather conditions. The four MPPT algorithms are: P & O, HC and INC (fixed step, INCF and variable step, INCV). The main objective is to determine which MPPT algorithm and which DC-DC converter topology are suitable for sub-Saharan (tropical) weather conditions. In the second section, methods used for this work are presented. Then the results are presented and discussed.

In order to collect data for this research work, the system represented in

• A pyranometer for the measurement of the global irradiance (in W/m^{2}). Its specifications are given in

• Voltage and current sensors (ALMEMO^{®}) for the measurement of voltage and current respectively.

• A temperature sensor (ALMEMO^{®}) for the measurement of the temperature at the surface of the solar panel. Its specifications are given in

Spectral Range | 285 - 2800 nm |
---|---|

Sensibility | 2 - 20 µV/W/m^{2} |

Impedance | 20 - 200 Ω |

Output Range (0 to 1500 W/m^{2}) | 0 - 30 mV |

Maximum Irradiance | 2000 W/m^{2} |

Rising time (63%) | <1.2 s |

Rising time (95%) | <5 s |

Sensor type | Thermopile |

Temperature Range | −40˚C to +80˚C |

Type | CTN FNA 611 |
---|---|

Measurement Range | −10˚C to +90˚C |

T90 | 20 s |

Cable | 2 m PVC |

• An ALMEMO^{®} data acquisition unit having five inputs, two outputs, an EEPROM of 1 MB (200,000 measures).

1) DC-DC converters

Two topologies of DC-DC converters are used in this work: the buck and the boost converters.

• The buck converter is represented in

V S = α V p v (1)

I S = I p v α (2)

With α the duty ratio ( 0 < α ≤ 1 ).

• The boost converter is represented in

V S = V p v 1 − α (3)

I S = ( 1 − α ) I p v (4)

With α the duty ratio ( 0 < α ≤ 1 ).

2) MPPT algorithms

Many MPPT algorithms have been developed. In this paper, the focus was only on the most popular and the worldwide algorithms: P & O, HC and INC (fixed and variable steps).

• The Hill Climbing algorithm is based on the perturbation of the duty ratio. Its flow chart is represented in

• The Perturb and Observe (P & O) algorithm with its flowchart represented in

• The Incremental Conductance (INC) algorithm which is divided into two types: the fixed step one and the variable step one. The fixed step incremental conductance (INCF) algorithm operates like the P & O algorithm with the same performance. Its flowchart is shown in _{step}) given in Equation (5). Coefficient N is determined according the condition given in Equation (6) [

D step = N | P p v ( k ) − P p v ( k − 1 ) V p v ( k ) − V p v ( k − 1 ) | (5)

N < D stepmax | d P p v d V p v | fixedstep = D stepmax (6)

Jae-Hoon and Won-Pyo developed in 2013 [_{p}, a and c are constants which value values are determined according to the convergence condition of the system.

D step = N p | 1 − 1 1 + exp [ − a ( Δ P p v − c ) ] | (7)

3) Modelling of the PV system

The electrical model of a PV panel is widely known. In this research paper, the single-diode model is used because the experimental data were recorded on a monocrystalline silicon PV panel. The equivalent circuit of this panel is given by Dandoussou et al., 2015 [

• The first block is the PV panel having nine input variables. The surface temperature was recorded during one day as shown in

• The second block is the DC-DC converter. Each of the two topologies (boost and buck converters) has been implemented.

• The third block is the MPPT block. Using Stateflow under Simulink®, the four MPPT algorithms (HC, P & O, INCF and INCV) have been implemented.

• The fourth block is the load block which is a rheostat of 7.5 Ω.

red waveform (PoutHVBoost2) is for L = 9350 µH and C = 2.67 µF and the blue waveform is for L = 935 µH and C = 2.67 µH. It’s clear that when increasing the value of the inductance (L), even though the output power becomes more stable, the response time of the algorithm becomes too high. For a very small value of the inductance (L), the output power becomes very unstable.

waveform (PoutINCVBuck2) is for L = 935 µH and C = 26.7 µF. This means that the value of the capacitor (C) affects the performance of the INCV algorithm, controlling a buck converter. When C is small, the output power is unstable. When C is increasing, the output power is still oscillating, with power losses decreasing. The response time is not affected.

The simulated results for these two algorithms are shown in

proved by Dandoussou et al., 2017 [

The output powers are shown in

more suitable for the buck converter. There are power losses during the whole day.

^{®} command: E = trapz(t, P), with t the time (from 10:00 to 15:30) and P the produced power (in W). The measured energy is given by Emes =

E_{HC} (Wh) | E_{P&O,INCF} (Wh) | E_{INCV} (Wh) | |
---|---|---|---|

Buck Converter | 170.65 | 140.13 | 209.37 |

Boost Converter | 179.31 | 240.12 | 262.80 |

160.93 Wh. With the buck converter controlled by P & O or INCF algorithm, the output energy is low compare to the measured energy on the system without MPPT. Once more the INCV algorithm has the best performance whatever which converter is used.

d E = P d t ⇒ E = ∫ t 0 t 1 P d t (8)

This paper focused on the simulations of MPPT algorithms according to two topologies of DC-DC converters. Recorded temperature and irradiance and extracted PV parameters (ideality factor, series resistance and reverse saturation current) were used to simulate the PV systems using Matlab^{®}/Simulink^{®}. From the obtained results, it is clear that there are some power losses during the whole day, independent from the weather conditions, when the buck converter is used. The output powers fluctuate during the whole day. The boost converter is suitable for all the MPPT algorithms, with the INCV algorithms having the best performance. However, in further works, it would be better to take into consideration others factors like the PV technologies, the other DC-DC converter topologies, additional electronics circuits (a stabiliser for example).

Authors thank the Director of HTTTC Kumba for providing some key equipment for this research work to be realized smoothly.

The authors declare no conflicts of interest regarding the publication of this paper.

Dandoussou, A., Kenfack, P., Perabi, S.N. and Kamta, M. (2021) Simulations of the Performance of Maximum Power Point Tracking Algorithms Based on Experimental Data According to the Topologies of DC-DC Converters. Journal of Power and Energy Engineering, 9, 76-92. https://doi.org/10.4236/jpee.2021.95005