This study is aimed at providing a comparison between fuzzy systems and convectional P&O for tracking MPP of a PV system. MATLAB/Simulink is used to investigate the response of both algorithms. Several weather conditions are simulated: (i) uniform irradiation, (ii) sudden changing, and (iii) partial shading. Under partial shading on a PV panel, multipeaks appeared in

Due to global warming along with high prices of fossil fuel and its hazards on the environment, searching for other renewable green sources of energy drew the attention of the world. Solar energy is considered the main source of renewable energy. Solar energy is a permanent, nonpolluting, and low-running-cost source of energy. Photovoltaic (solar cell) systems are one of the most favorable systems, and their installation is spreading widely. Photovoltaic (PV) systems can be connected to a grid or can be used as stand-alone systems [

Maximum power point tracking is essential to keep the system operating at its optimal power. Up to date, the overall PV efficiency reaches around 15%. Raising the power generated from PV systems can be achieved by tracking the maximum power point of the output power-voltage curve. This curve may contain multilocal maximum points under partially shaded conditions [

Several methods for finding MPP are developed over the last three decades [

As mentioned previously, when a PV system is subjected to a partially shaded condition, multiple peaks appeared in the

Intelligent systems such as neural networks (NN) and fuzzy logic controllers (FLC) have been used successfully in tracking the maximum power point of PV to decrease computation power requirement, while increasing the speed and efficiency of the tracking [

Detailed description of PV modeling can be found in [

Effect of irradiance changes on

Characteristic curves of PV under partial shading.

In this section, a brief description about the P&O controller and fuzzy logic controller is given.

A detailed description of the P&O controller can be found in [

Flowchart of the P&O algorithm.

To generate maximum power, a DC-DC boost converter is used and placed between the source and the load. To simulate the P&O algorithm, the PV system composed of a PV panel, a boost DC-DC converter, MPPT, and resistive load is built as shown in Figure

Schematic diagram of the PV system with MPPT.

The minimum values of inductance and capacitance of the converter necessary for stability (listed in Table

DC-DC booster converter design values.

Electrical characteristic | Values |
---|---|

Inductance | 9 × 10^{−4} H |

Output capacitor | 0.001 F |

Input capacitor | 1 × 10^{−9} F |

Resistance load | 28.18 Ω |

The P&O algorithm has been implemented in a Simulink model to control the duty cycle of the switching signal of the converter.

The proposed FL MPPT logic diagram shown in Figure

General diagram of a fuzzy controller.

The proposed fuzzy rules of the system are shown in Table

Rules of the fuzzy controller.

Very low | Low | Medium | High | Very high | |
---|---|---|---|---|---|

Very low | Very low | Very low | Medium | High | Very high |

Low | Very low | Very low | High | High | Very high |

Medium | Medium | Medium | Medium | Medium | Very high |

High | Medium | Medium | High | Medium | Very high |

Very high | Low | Low | Medium | High | Very high |

Based on the results of the Simulink model, tuning of the rules is performed to design the fuzzy logic controller. Values of fuzzy controller inputs are compared with twenty-five rules of the system and are implicated with the membership functions. The implication has been chosen to be an “and” operator that chooses minimum values of membership functions. The implicated rules were aggregated using a maximum method. Figure

Surface view for fuzzy inputs (

The performance of the proposed FLC is evaluated using MATLAB/Simulink. The electrical specifications of the PV cell are

Comparison between output power values with and without MPPT.

Case no. | Irradiance level (W/m^{2}) |
Output power (W) (without MPPT) | Output power (W) (with MPPT) | % increase |
---|---|---|---|---|

1 | 800 | 102.5 | 123.2 | 20.7% |

2 | 600 | 64.9 | 87 | 34% |

3 | 500 | 45 | 70.8 | 57% |

4 | 300 | 21.4 | 38.7 | 80% |

Figure

Comparison between fuzzy and P&O partial shading.

Partial shading—1000 to 800 W/m^{2}

Partial shading—800 to 500 W/m^{2}

Partial shading—600 to 400 W/m^{2}

Partial shading—500 to 300 W/m^{2}

Power values on the load after applying FLC have been compared with the nominal values of the maximum power points for several cases of uniform irradiation and partial shading. Results listed in Table

Values of model outputs.

Case no. | Condition | Ir1 (W/m^{2}) |
Ir2 (W/m^{2}) |
Nominal power (W) | Power after fuzzy (W) | Efficiency |
---|---|---|---|---|---|---|

1 | Uniform irradiation | 1000 | 1000 | 149 | 149 | 100% |

2 | Uniform irradiation | 900 | 900 | 133 | 133 | 100% |

3 | Uniform irradiation | 800 | 800 | 118 | 117.5 | 99.6% |

4 | Uniform irradiation | 700 | 700 | 102.5 | 101.5 | 99% |

5 | Uniform irradiation | 600 | 600 | 87.2 | 87 | 99.8% |

6 | Uniform irradiation | 500 | 500 | 71.3 | 70.8 | 99.3% |

7 | Uniform irradiation | 400 | 400 | 56.5 | 56 | 99% |

8 | Uniform irradiation | 300 | 300 | 41.5 | 41 | 99% |

9 | Uniform irradiation | 200 | 200 | 26.6 | 24.5 | 92% |

10 | Partial shading | 1000 | 800 | 126.3 | 126 | 99.8% |

11 | Partial shading | 900 | 600 | 95.7 | 94 | 98% |

12 | Partial shading | 800 | 500 | 79.5 | 79 | 99.4% |

13 | Partial shading | 700 | 500 | 78.2 | 78 | 99.7% |

14 | Partial shading | 600 | 400 | 62.3 | 62 | 99.5% |

15 | Partial shading | 900 | 300 | 61.4 | 60 | 97.7% |

16 | Partial shading | 700 | 300 | 48 | 47.5 | 99% |

17 | Partial shading | 500 | 300 | 46.3 | 46 | 99.4% |

To further prove the ability of FLC to track the MPP under partial shading, a comparison between the proposed algorithm and the P&O algorithm is conducted. Results listed in Table

Comparison between the fuzzy controller and P&O.

Ir1 (W/m^{2}) |
Ir2 (W/m^{2}) |
Local peak (W) | Global peak (W) | Output of P&O (W) | Output of fuzzy (W) |
---|---|---|---|---|---|

1000 | 800 | 71.24 | 126.3 | 68 | 126 |

800 | 500 | 55.7 | 79.5 | 53 | 79 |

600 | 400 | 41.5 | 62.3 | 39 | 61 |

500 | 300 | 34 | 46.3 | 31 | 46 |

In this study, the fuzzy logic controller (FLC) for maximum power point tracking (MPPT) of a photovoltaic system under variable insolation conditions has been developed to track the maximum power point of the PV system. A MATLAB/Simulink model consists of a PV panel and a boost converter with FLC connected to a resistive load that has been built in order to evaluate the performance of the proposed controller. The proposed system showed its ability to recover from sudden changes and maintain stability under partial shading conditions. A comparison between the performance of the proposed FLC and perturbation and observation controller has been made. The results show that the fuzzy logic controller has reached the global point with an efficiency of 96% while the P&O controller failed to reach this point.

Ideal factor of the diode

Minimum capacitance value (F)

Duty cycle

Voltage difference (V)

Fill factor

Switching frequency (Hz)

Maximum current (A)

Photovoltaic current (A)

Saturation of dark current (A)

Minimum inductance value (H)

Maximum power (W)

Maximum power (W)

_{pv}:

Photovoltaic power (W)

Resistance (

Series resistance (Ω)

Shunt resistance (Ω)

Output voltage (V)

Photovoltaic voltage (V)

Input voltage of the converter (V)

Efficiency (%).

The FLC training data used to support the findings of this study are available from the corresponding author upon request.

There is no conflict of interest regarding the publication of this article.