# Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm

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## Abstract

**:**

## 1. Introduction

## 2. Complete System Formation

#### 2.1. PV Generator

- I
_{PVG}= Photo Current - I
_{RSC}= Diode reverse saturation current - I
_{PVo}= Output PV current - V
_{PVo}= PV output voltage - R
_{series}= Resistance in series - R
_{Parallel}= Resistance in parallel - V
_{Thermal}= PV module thermal voltage - A = Ideality constant of diode

#### 2.2. Luo Converter Mathematical Modeling

_{1}) values are determined mathematically as:

- d
_{duty}= Duty ratio - f
_{Pulse}= Frequency of Switched pulse - V
_{0}= Output Voltage of Luo Converter

#### 2.3. A Hybrid Proposed FLC-ANN Tuned FPA MPPT

- ${\mu}_{P}\left(x\right)$ = Membership function of P fuzzy set in X Universe of discourse
- ${\mu}_{Q}\left(y\right)$ = Membership function of Q fuzzy set in Y Universe of discourse
- $X,Y=$x, y variables defined in Universe X and Y, respectively

- $\widehat{D}$ = Crisp output
- ${\mu}_{D}\left(D\right)$ = Membership function (Aggregated)
- D = Fuzzy output
- S = Subarea/Universe of discourse

- ${\mu}_{A}\left(E\right)$ = Membership function of fuzzy set A in E universe of discourse
- ${\mu}_{B}\left(dE\right)$ = Membership function of fuzzy set B in dE universe of discourse
- W
_{Li}= Weight of consequent ith layer

- P = Total sample
- D = Fuzzy output
- $\overline{D}$ = Neural network output

- ${X}_{i}^{T}$ = Vector representing solution
- T = No. of iteration
- L
_{f}= Levy flight factor - ${\gamma}_{scaling}$ = Scaling factor
- G
_{best}= Global best solution

- ${X}_{m}^{T}$ and ${X}_{n}^{T}$ = two unlike pollen in the species
- P
_{f}= Switched probability

- ${V}_{PV}\left(n\right)$ =nth iteration PV voltage
- ${I}_{PV}\left(n\right)$ =nth iteration PV current
- $d{V}_{PV}\left(n\right)$ = change in PV voltage (nth and (n − 1)th iteration)

#### 2.4. Electronic BLDC Commutator and VSI Switching

- V
_{ap}, V_{bp}, V_{cp}= Phase voltage of a 3-Phase BLDC motor - I
_{ap}, I_{bp}, I_{cp}= Phase Currents - E
_{ba}, E_{bb}, E_{bc}= Phase Back EMF of BLDC motor - L
_{1}= Each Phase self-inductance - M
_{1}= Two phase’s mutual inductance - T
_{EM}= Developed Electromagnetic torque of BLDC motor - ω
_{Rotor}= Rotor Speed

## 3. Experimental Results

#### 3.1. Steady-State Performance

^{2}. The proposed MPPT design technique is working effectively and tracks optimal power from PV module with unity duty cycle at 1000 W/m

^{2}solar insolation level depicted in Figure 9. Practical results obtained for the BLDC-driven Luo converter-employed PV pumping are described in Figure 9a PVG at 1000 W/m

^{2}. (Figure 9b) BLDC performance at 1000 W/m

^{2}. (Figure 9c) generated hall sensor pulses at 1000 W/m

^{2}(Figure 9d) switched and hall pulses at 1000 W/m

^{2}(Figure 9e) BLDC performance at 300 W/m

^{2}(Figure 9f) switched and hall pulses at 300 W/m

^{2}.The corresponding BLDC motor and torque (1500 rpm) has been demonstrated in Figure 9d presents the obtained hall sensor pulses with motor torque. The performance of the BLDC motor-pumping system has been evaluated with 300 W/m

^{2}solar irradiance. The motor torque is experimentally obtained, which is sufficient to operate PV water pumping. Based on duty cycle generation using the MPPT algorithm, the corresponding hall signals have been generated to trigger six switches of the inverter.

#### 3.2. Dynamic Behavior of PV System

^{2}to 1000 W/m

^{2}. According to variation in sun irradiance level, corresponding changes in PV current, DC link voltage, BLDC stator current and motor torque have been verified (Figure 10) and PV pumping is running without any interruption. The duty cycle for BLDC-PV pump control is generated with variation in sun insolation accordingly and outstanding motion control has been comprehended.

#### 3.3. Behavior at Starting

^{2}and 300 W/m

^{2}. Initially, the duty cycle is kept at 0.5 to run the motor. The sufficient motor speed is obtained by controlling the starting current, which runs the motor-pump system successfully. Figure 11 portrays the successful action of BLDC-PV pump at the start by limiting starting current, which reveals the progression with safe and soft start. The obtained results prove the more relevant performance conducted for the EMI reduction and soft starting for the experimental test conducted in [28].

_{OPENCkt}state) and reaches a global power point with variable solar irradiance. With application of hybrid ANFIS-FPA MPPT, steady GPP is attained over a complete day. The performance of the MPPT controllers for two algorithms ANFIS-FPA and FPA are tested with stepped irradiance input. Figure 13a illustrates that the proposed ANFIS-FPA imparts accurate and precise PV system outcomes with zero variation around GPP with fluctuating sun insolation. However, the FPA employed algorithm provides inconsistent and more oscillation nearby GPP that equates to the ANFIS-FPA algorithm described using Figure 13b. Under these situations, ANFIS-FPA has high tracked PV power with proportionately less GPP time. Practical results demonstrate that ANFIS-FPA algorithm contributes rapid and insignificant swinging differentiated with FPA MPPT illustrated in Figure 13a,b. Figure 14 demonstrates the behavior of numerous MPPT Viz. FPA, PSO, FLC and P and O control under standard test conditions. Under standard test conditions, ANFIS-FPA has better PV tracking efficiency compared to ANFIS-PSO, FLC and P and O methods, as illustrated with Figure 14. A hybrid ANFIS-FPA algorithm has global power point trajectory with the most tracked power and has zero oscillation throughout, equated with different controllers. The PV tracked trajectories are also examined under fluctuating weather situations (Figure 15). Under dynamic weather conditions, the PV tracking trajectory is found to be more accurate compared to conventional algorithms and has a zero GPP oscillation around this point, which is explained by Figure 15. Practical results reveal that ANFIS-FPA-optimized MPPT provides optimal tuning with high performance index.

## 4. Conclusions

## Author Contributions

## Conflicts of Interest

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**Figure 9.**BLDC-driven Luo converter-employed PV pumping (

**a**) PVG at 1000 W/m

^{2};(

**b**) BLDC performance at 1000 W/m

^{2}; (

**c**) generated hall sensor pulses at 1000 W/m

^{2}; (

**d**) switched and hall pulses at 1000 W/m

^{2}; (

**e**) BLDC performance at 300 W/m

^{2}; (

**f**) switched and hall pulses at 300 W/m

^{2}.

**Figure 11.**BLDC driven Luo converter employed PV pumping under soft starting (

**a**) 1000 W/m

^{2}; (

**b**) 400 W/m

^{2}

S.N | Parameters | Values |
---|---|---|

1. | Inductor (L) | 0.02 mH |

2. | Capacitor (C and C_{1}) | 20 µF, 15 µF |

3. | Switching Frequency (f_{pulse}) | 10 KHz |

4. | Duty Ratio (d_{duty}) | 0.58 |

S.N | Parameters | Value |
---|---|---|

1 | Total fuzzy rule base fired | 25 |

2 | Total number of Epoch | 740 |

3 | Types of membership function | Gaussian type |

4 | Total layer (neural network) | 5 |

5 | Total neural network training data sets | 200 |

S.N | Parameters | Values |
---|---|---|

1. | Switched Probability (P_{f}) | 0.7 |

2. | Scaling Factor | 1.25 |

3. | No of Epoch | 740 |

4. | RMSE (Obtained) | 106 × 10^{−6} |

5. | Total Rule Based Fired | 25 |

6. | ANFIS Obtained (Training Error) | 0.6255 × 10^{−6} |

Angle | Hall Signals | Switching States | |||||||
---|---|---|---|---|---|---|---|---|---|

H_{1} | H_{2} | H_{3} | ${\mathit{S}}_{1}$ | ${\mathit{S}}_{2}$ | ${\mathit{S}}_{3}$ | ${\mathit{S}}_{4}$ | ${\mathit{S}}_{5}$ | ${\mathit{S}}_{6}$ | |

0–π/3 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 |

π/3–2π/3 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 |

2π/3–π | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 |

π–4π/3 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |

4π/3–5π/3 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |

5π/3–2π | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |

S.N | Parameters | Value |
---|---|---|

1 | Resistance of stator | 4.16 Ω |

2 | Inductance value of stator | 2.2 mH |

3 | Speed rating | 1500 rpm |

4 | Number of Pole pair | 2 |

5 | Constants(Voltage & torque) | 86 V_{LL}/KRPM & 0.85 Nm/Ampere |

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**MDPI and ACS Style**

Priyadarshi, N.; Padmanaban, S.; Mihet-Popa, L.; Blaabjerg, F.; Azam, F.
Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm. *Energies* **2018**, *11*, 1067.
https://doi.org/10.3390/en11051067

**AMA Style**

Priyadarshi N, Padmanaban S, Mihet-Popa L, Blaabjerg F, Azam F.
Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm. *Energies*. 2018; 11(5):1067.
https://doi.org/10.3390/en11051067

**Chicago/Turabian Style**

Priyadarshi, Neeraj, Sanjeevikumar Padmanaban, Lucian Mihet-Popa, Frede Blaabjerg, and Farooque Azam.
2018. "Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm" *Energies* 11, no. 5: 1067.
https://doi.org/10.3390/en11051067