# A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions

^{*}

## Abstract

**:**

## 1. Introduction

^{11}MW, which is far larger than the global energy consumption rate [2,3]. The photovoltaic technology is considered the most prominent method of utilizing solar energy. The power generated from PV (Photovoltaic) system depends on irradiance levels, temperature, shading, and other weather conditions. The MPP (maximum power point) varies with the radiation, thus the maximum power point MPP tracking algorithm is crucial to find and maintain maximum power levels. Using DC/DC convertors, PV generators are continuously being driven to operate at the voltage proposed by the algorithm to supply the load with the maximum available power.

## 2. PV Modeling and Characteristics

_{PV}is the current generated by the cell, I

_{ph}is the solar generated current, I

_{sh}is the shunt resistance current and ID is the diode current, q is an electron charge (1.6 × 10

^{−19}C), T is the cell’s operating temperature, K

_{b}is the Boltzmann’s constant (1.38 × 10

^{−23}J/K), A is the diode ideality factor, R

_{s}is the series resistance, V

_{PV}and I

_{PV}are the photovoltaic operating voltage and current respectively, I

_{DS}is the diode saturation current. Equation (1) is modeled in MATLAB/Simulink employing 72 cells connected in series based on the electrical specifications provided by SUNTECH STP270-24/Vd PV module datasheet shown in Table 1.

_{o}to the input voltage Vi as:

## 3. MPPT Algorithms

#### 3.1. P&O Algorithm

#### 3.2. Fuzzy Logic Controller (FLC)

^{2}) in partial and full shading conditions. The second one not shown was used to seek the optimum duty cycle manually in an iterative manner and to record the corresponding open circuit voltage and the short circuit current. Fifty-five input–output data sets are finally produced. It is worth mentioning that these types of modelling work well if there are a large number of data to be used for training.

#### 3.3. Proposed Hybrid MPPT Algorithm

## 4. Results and Discussion

#### 4.1. Performance of FLC Alone

#### 4.2. Performance of P&O Alone

^{2}.

#### 4.3. Performance of the Hybrid Proposed Algorithm

^{2}to 700 W/m

^{2}, the power obtained from P&O controller changed from 259 W to 70 W in 0.4 s while it took only 0.15 s for the proposed controller to change the power from 267 W to 72 W. Table 4 lists comparisons between P&O with different step sizes, Fuzzy and the proposed hybrid controllers. It is clear from simulation results listed in Table 4 that the proposed hybrid controller successfully outcome the shortages of fuzzy and P&O algorithm alone.

#### 4.4. Testing the Performance at Random Weather Conditions

^{2}and the other 36 cells are exposed to 317 W/m

^{2}. These weather conditions points were not utilized for training. It can be seeing in Figure 11 that that P&O got trapped in the local maximum i.e., local maximum power point is 89 W, where the global maximums power point for this weather condition is 118 W. Moreover, the accuracy of FLC was extremely low. i.e., FLC alone was only able to deliver only 84.5% of the maximum available power. Finally, the proposed hybrid controller skipped the fake maxima because of the initial guess generated from fuzzy controller and improved this interpolation and successfully reached the maximum available power of 118 W. Finally, Table 5 compares between the performance of the proposed hybrid algorithm with FLC alone for several cases at weather conditions not utilized for training. It is clear that the hybrid algorithm was successfully able to harvest over 99% of the maximums power available for all random weather conditions, while the FLC efficiency decreases significantly when conditions points are far from those used for training.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Characteristics of STP270-24/Vd module under different radiation levels, (

**a**) P–V uniform; (

**b**) I–V characteristics, uniform; (

**c**) P–V, partial shading; (

**d**) I–V, partial shading.

**Figure 6.**Performance of FLC alone under different radiation conditions. (

**a**) Uniform radiation of 1000 W/m

^{2}; (

**b**) partial shading of (800, 400) W/m

^{2}; (

**c**) uniform radiation of 635 W/m

^{2}; (

**d**) partial shading of (985, 317) W/m

^{2}.

**Figure 7.**P&O performance. (

**a**) Effect of step size uniform shading; (

**b**) PV curve under partial shading; (

**c**) P&O controller performance under partial shading.

**Figure 8.**Simulation results under uniform shading conditions, (

**a**) insolation level of 800 W/m

^{2}; (

**b**) insolation level of 1000 W/m

^{2}; (

**c**) insolation level of 600 W/m

^{2}; (

**d**) insolation level of 400 W/m

^{2}.

**Figure 9.**Simulation results under partial shading conditions, (

**a**) (1000, 300) W/m

^{2}; (

**b**) (800, 300) W/m

^{2}; (

**c**) (500, 200) W/m

^{2}; (

**d**) (400, 100) W/m

^{2}.

**Figure 10.**Simulation results under sudden changes in radiation levels. (

**a**) Dropping from 1000 to 300 W/m

^{2}; (

**b**) Dropping from 900 to 400 W/m

^{2}; (

**c**) rising from 250 to 850 W/m

^{2}; (

**d**) rising from 300 to 700 W/m

^{2}.

**Figure 11.**Simulation results under partial shading conditions of 985–317 W/m

^{2}, (

**a**) performance of; (

**b**) P–V characteristics of STP270 PV module with the power levels obtained by the three controllers.

**Table 1.**Electrical specifications the STP 270-24/Vd PV module [49].

Electrical Characteristic | STP270-24/Vd |
---|---|

Optimum Operating Voltage (V_{mp}) | 35.0 V |

Optimum Operating Current (I_{mp}) | 7.71 A |

Open-Circuit Voltage (V_{oc}) | 44.5 V |

Short-Circuit Current (I_{sc}) | 8.20 A |

Maximum Power at STC (P_{max}) | 270 W |

Temperature Coefficient of V_{oc} | −0.34%/°C |

Temperature Coefficient of I_{sc} | 0.045%/°C |

Fuzzy Logic Type | Sugeno |
---|---|

Number of inputs | 2 |

Number of membership function | 10 |

No of TRAINING a epochs | 3000 |

Input membership function type | Gaussian |

output membership function type | Linear |

Algorithm used | Grid partitioning |

Optimization method | Hybrid |

**Table 3.**Performance of fuzzy controller under partial shading conditions at points not utilized in training.

Case No. | Weather Condition | Ir1 (Watt/m^{2}) | Ir2 (Watt/m^{2}) | Nominal Power (Watt) | Power after Fuzzy (Watt) | Efficiency |
---|---|---|---|---|---|---|

1 | Uniform Irradiation | 1000 | 1000 | 258 | 258 | 100% |

2 | 900 | 900 | 232 | 232 | 100% | |

3 | 800 | 800 | 207 | 206 | 99.6% | |

4 | 700 | 700 | 181 | 179.1 | 99% | |

5 | 600 | 600 | 155 | 154.2 | 99.5% | |

6 | 500 | 500 | 128.5 | 127.6 | 99.3% | |

7 | 400 | 400 | 103.2 | 102.6 | 99.6% | |

8 | 740 | 740 | 190.3 | 163.4 | 85.7 | |

9 | 585 | 585 | 150.63 | 139.2 | 92.4 | |

10 | 597 | 597 | 153.8 | 151.6 | 98.5 | |

11 | Partial Shading | 1000 | 300 | 128.1 | 122.5 | 96% |

12 | 800 | 300 | 101.4 | 99.3 | 98% | |

13 | 500 | 200 | 58.1 | 56.2 | 96.7% | |

14 | 400 | 100 | 46.3 | 44.8 | 96.5% | |

15 | 700 | 300 | 48.7 | 47.5 | 99% | |

16 | 892 | 407 | 126.3 | 106.8 | 84.4% | |

17 | 644 | 596 | 103.7 | 92.3 | 89.0% | |

18 | 400 | 100 | 46.3 | 38 | 82.1% |

**Table 4.**Comparison between P&O with different step sizes, fuzzy and the proposed hybrid controllers.

Controller | Accuracy | Convergence | Oscillations | Trapping |
---|---|---|---|---|

Fuzzy | Moderate | Fast | Low | No |

P&O/large step size | Low | Fast | High | Yes |

P&O/small step size | High | Slow | Moderate | Yes |

Hybrid | High | Fast | Moderate | No |

**Table 5.**Performance of hybrid algorithm under partial shading conditions at points not utilized in training.

Case No. | Ir1 (Watt/m^{2}) | Ir2 (Watt/m^{2}) | Nominal Power (Watt) | FLC Alone (Watt) | Hybrid Proposed Algorithm (Watt) |
---|---|---|---|---|---|

1 | 1000 | 300 | 128.1 | 122.5 | 127.9 |

2 | 800 | 300 | 101.4 | 99.3 | 101.2 |

3 | 500 | 200 | 58.1 | 56.2 | 57.9 |

4 | 400 | 100 | 46.3 | 44.8 | 46.2 |

5 | 700 | 300 | 48.7 | 47.5 | 48.5 |

6 | 892 | 407 | 126.3 | 106.8 | 126.2 |

7 | 644 | 596 | 103.7 | 92.3 | 103.5 |

8 | 400 | 100 | 46.3 | 38 | 46.1 |

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**MDPI and ACS Style**

Bataineh, K.; Eid, N.
A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions. *Resources* **2018**, *7*, 68.
https://doi.org/10.3390/resources7040068

**AMA Style**

Bataineh K, Eid N.
A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions. *Resources*. 2018; 7(4):68.
https://doi.org/10.3390/resources7040068

**Chicago/Turabian Style**

Bataineh, Khaled, and Naser Eid.
2018. "A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions" *Resources* 7, no. 4: 68.
https://doi.org/10.3390/resources7040068