# A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

_{o}is fixed) and the solar panel produces the maximum amount of power [13,14]. The MPPT techniques monitor the output voltage and output current of the solar panels and give a particular operating point to the panel such that the maximum amount of power is delivered.

## 2. Modelling of a Photovoltaic Cell

_{ph}= generated photocurrent, I

_{sat}= reverse saturation diode current, q = charge of electron = 1.60217 × 10

^{−19}C, k = Boltzmann constant = 1.3807 × 10

^{−23}J/k, n = diode’s ideality factor, T = cell temperature, R

_{s}= series resistance, R

_{sh}= shunt resistance.

_{ph}generated is directly proportionate to sun radiation, and the arrangement of series and parallel resistance shows the voltage losses when connected to external contacts and leakage current. Figure 2 and Figure 3 depict the module’s p-v curve for different values of irradiation and temperature.

## 3. DC-DC Boost Converter

## 4. MPPT Control

_{T}, such that it sees a constant load, so as the output changes, the control input to the converter changes such that the input impedance of the controller is maintained as constant.

_{T}seen from PV will remain constant, irrespective of load resistance. From this, the operating point will be maintained constant and the maximum power will be drawn from the panel. The MPPT control scheme for the solar panel is shown in Figure 5, and the specifications can be found in Table 1.

#### 4.1. MPPT Algorithms

#### 4.2. Perturb and Observe Method

#### 4.3. Incremental Conductance Method

#### 4.4. Fuzzy Logic Controller Method

#### 4.4.1. Fuzzification

#### 4.4.2. Fuzzy Inference System (FIS)

**Case 1:**If the term (E) is +ve, the position is mainly found to the leftward of the MPP. If the term (CE) is +ve, the action point shifts closer to the MPP. If the term CE is −ve, the opposite happens.

**Case 2**: If the term E is −ve, the position is located to the rightward of the MPP. If the term CE is −ve, the action point drifts farther from the MPP, and the opposite happens if CE is −ve.

#### 4.4.3. Defuzzification

#### 4.5. Neural Network Method

^{2}).

^{−11}; the training state validations and the error histograms can be observed in (Figure 25c,d), and by the regression plot in Figure 25e, we observe the relationship between the dependent and independent variables, and we observe that the data and fit are aligned, which shows only the smallest possible error. From Figure 25f, we see the training, validation, and testing results of the trained data of 1000 points—indicating a very small error. The step-by-step executions of a neural network can be seen in Figure 26.

- Solar panel data used for training the neural network and ANFIS
- Short circuit current = 8.66 A;
- Maximum power point current = 8.15 A;
- Open-source voltage = 37.3 V;
- Maximum power point voltage = 30.7;
- Alpha = 0.086998;
- Beta = −0.36901;

#### 4.6. ANFIS Method (Adaptive Neuro-Fuzzy Inference System)

#### Adaptive Neuro-Fuzzy Controller

- Rule i: if E(k) is Xi1 and CE(k) is Xi2, then

- (Duty)i is the changing duty cycle and
- Xij is the membership function.

#### 4.7. Hybrid Method (Neural Network and P&O)

## 5. Results and Discussion

^{2}(Figure 44), we can see that at the starting conditions, the output power is more than the panel’s maximum output power, and incremental has reached the maximum point first, followed by P&O, ANFIS, and fuzzy. The P&O and incremental algorithms were closer in the initial time, but at a few points, they overlapped. The neural network and hybrid algorithms have taken some time to track the maximum power point, although up to 0.4 s, these two algorithms’ outputs were lower than other algorithms. After the load disturbance at 0.3 s, the incremental gained MPP faster but was overtaken by P&O at 0.316 s, followed by fuzzy and ANFIS. After the disturbance at 0.6 s, the fuzzy gained first, which was closely followed by incremental and hybrid algorithms, and after 0.61 s, all algorithms performed in close proximity where P&O, neural network and hybrid, and ANFIS were leading. After 0.8 s disturbance, we observe that P&O, incremental, and fuzzy logic have overlapped in many instances, producing max output, followed by the hybrid algorithm. The neural network has lagged behind all four algorithms and ANFIS has lagged behind all the algorithms producing the least output.

## 6. Conclusions

## 7. Future Scope

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Beriber, D.; Talha, A. MPPT techniques for PV systems. In Proceedings of the 4th International Conference on Power Engineering, Energy and Electrical Drives, Istambul, Turkey, 13–17 May 2013; pp. 1437–1442. [Google Scholar] [CrossRef]
- Saleh, A.; Azmi, K.F.; Hardianto, T.; Hadi, W. Comparison of MPPT Fuzzy Logic Controller Based on Perturb and Observe (P&O) and Incremental Conductance (InC) Algorithm on Buck-Boost Converter. In Proceedings of the 2018 2nd International Conference on Electrical Engineering and Informatics (ICon EEI), Batam, Indonesia, 16–17 October 2018; pp. 154–158. [Google Scholar] [CrossRef]
- Selman, N.H. Comparison Between Perturb & Observe, Incremental Conductance and Fuzzy Logic MPPT Techniques at Different Weather Conditions. Int. J. Innov. Res. Sci. Eng. Technol.
**2016**, 5, 12556–12569. [Google Scholar] [CrossRef] - Sridhar, R.; Jeevananathan, D.; Selvan, N.; Banerjee, S. Modeling of PV Array and Performance Enhancement by MPPT Algorithm. Int. J. Comput. Appl.
**2010**, 7, 35–39. [Google Scholar] [CrossRef] - Zainudin, H.N.; Mekhilef, S. Comparison Study of Maximum Power Point Tracker Techniques for PV Systems. In Proceedings of the 14th International Middle East Power Systems Conference (MEPCON’10), Cairo, Egypt, 19–21 December 2010; pp. 750–755. [Google Scholar]
- Kalashani, M.B.; Farsadi, M. New Structure for Photovoltaic Systems with Maximum Power Point Tracking Ability. Int. J. Power Electron. Drive Syst.
**2014**, 4, 489. [Google Scholar] [CrossRef] - Berrera, M.; Dolara, A.; Faranda, R.; Leva, S. Experimental Test of Seven Widely-Adopted MPPT Algorithms. In Proceedings of the 2009 IEEE Bucharest PowerTech, Bucharest, Romania, 28 June–2 July 2009; pp. 1–8. [Google Scholar] [CrossRef]
- Jyothy, L.P.; Sindhu, M.R. An Artificial Neural Network based MPPT Algorithm for Solar PV System. In Proceedings of the 2018 4th International Conference on Electrical Energy Systems (ICEES), Chennai, India, 7–9 February 2018; pp. 375–380. [Google Scholar] [CrossRef]
- Reisi, A.R.; Moradi, M.H.; Jamasb, S. Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review. Renew. Sustain. Energy Rev.
**2013**, 19, 433–443. [Google Scholar] [CrossRef] - Subudhi, B.; Pradhan, R. A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems. IEEE Trans. Sustain. Energy
**2013**, 4, 89–98. [Google Scholar] [CrossRef] - Lee, H.-S.; Yun, J.-J. Advanced MPPT Algorithm for Distributed Photovoltaic Systems. Energies
**2019**, 12, 3576. [Google Scholar] [CrossRef] - Khosrojerdi, F.; Taheri, S.; Cretu, A.-M. An adaptive neuro-fuzzy inference system-based MPPT controller for photovoltaic arrays. In Proceedings of the 2016 IEEE Electrical Power and Energy Conference (EPEC), Ottawa, ON, Canada, 12–14 October 2016; pp. 1–6. [Google Scholar] [CrossRef]
- Hijazi, R.; Karami, N. Neural Network Assisted Variable-Step-Size P&O for Fast Maximum Power Point Tracking. In Proceedings of the 2020 32nd International Conference on Microelectronics (ICM), Aqaba, Jordan, 14–17 December 2020; pp. 1–6. [Google Scholar] [CrossRef]
- Narendiran, S.; Sahoo, S.K.; Das, R.; Sahoo, A.K. Sahoo, Fuzzy logic controller based maximum power point tracking for PV system. In Proceedings of the 2016 3rd International Conference on Electrical Energy Systems (ICEES), Chennai, India, 17–19 March 2016; pp. 29–34. [Google Scholar] [CrossRef]
- Abdullah, G.; Aziz, M.S.; Hamad, B.A. Comparison between neural network and P&O method in optimizing MPPT control for photovoltaic cell. Int. J. Electr. Comput. Eng.
**2020**, 10, 5083–5092. [Google Scholar] [CrossRef] - Sarvi, M.; Azadian, A. A comprehensive review and classified comparison of MPPT algorithms in PV systems. Energy Syst.
**2021**, 13, 281–320. [Google Scholar] [CrossRef] - Atri, P.K.; Modi, P.S.; Gujar, N.S. Comparison of Different MPPT Control Strategies for Solar Charge Controller. In Proceedings of the 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), Mathura, India, 28–29 February 2020; pp. 65–69. [Google Scholar] [CrossRef]
- Chtouki, I.; Wira, P.; Zazi, M. Comparison of Several Neural Network Perturb and Observe MPPT Methods for Photovoltaic Applications. In Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France, 20–22 February 2018; pp. 909–914. [Google Scholar] [CrossRef]
- Khosravi, M.; Heshmatian, S.; Khaburi, D.A.; García, C.; Rodríguez, J. A Novel Hybrid Model-Based MPPT Algorithm Based on Artificial Neural Networks for Photovoltaic Applications. In Proceedings of the 2017 IEEE Southern Power Electronics Conference (SPEC), Puerto Varas, Chile, 4–7 December 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Dahiya, A.K. Implementation and Comparison of Perturb & Observe, ANN and ANFIS Based MPPT Techniques. In Proceedings of the 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 11–12 July 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Kacimi, N.; Grouni, S.; Idir, A.; Boucherit, M.S. New improved hybrid MPPT based on neural network-model predictive control-kalman filter for photovoltaic system. Indones. J. Electr. Eng. Comput. Sci.
**2020**, 20, 1230–1241. [Google Scholar] [CrossRef] - Kanimozhi, K.; Rabi, B.R.M. Development of Hybrid MPPT Algorithm for Maximum Power Harvesting under Partial Shading Conditions. Circuits Syst.
**2016**, 7, 1611–1622. [Google Scholar] [CrossRef] [Green Version] - Bataineh, K. Improved hybrid algorithms-based MPPT algorithm for PV system operating under severe weather conditions. IET Power Electron.
**2019**, 12, 703–711. [Google Scholar] [CrossRef] - Aurilio, G.; Balato, M.; Graditi, G.; Landi, C.; Luiso, M.; Vitelli, M. Fast Hybrid MPPT Technique for Photovoltaic Applications: Numerical and Experimental Validation. Adv. Power Electron.
**2014**, 2014, 125918. [Google Scholar] [CrossRef] - Sarwar, S.; Javed, M.Y.; Jaffery, M.H.; Arshad, J.; Rehman, A.U.; Shafiq, M.; Choi, J.-G. A Novel Hybrid MPPT Technique to Maximize Power Harvesting from PV System under Partial and Complex Partial Shading. Appl. Sci.
**2022**, 12, 587. [Google Scholar] [CrossRef] - Bollipo, R.B.; Mikkili, S.; Bonthagorla, P.K. Hybrid, optimization, intelligent and classical PV MPPT techniques: A Review. CSEE J. Power Energy Syst.
**2021**, 7, 9–33. [Google Scholar] [CrossRef] - Azzouz, S.; Messalti, S.; Harrag, A. A Novel Hybrid MPPT Controller Using (P&O)-neural Networks for Variable Speed Wind Turbine Based on DFIG. Model. Meas. Control A
**2019**, 92, 23–29. [Google Scholar] [CrossRef] - Arjun, M.; Zubin, J.B. Artificial Neural Network Based Hybrid MPPT for Photovoltaic Modules. In Proceedings of the 2018 International CET Conference on Control, Communication, and Computing (IC4), Thiruvananthapuram, India, 5–7 July 2018; pp. 140–145. [Google Scholar] [CrossRef]
- Sher, H.A.; Murtaza, A.F.; Noman, A.; Addoweesh, K.E.; Al-Haddad, K.; Chiaberge, M. A New Sensorless Hybrid MPPT Algorithm Based on Fractional Short-Circuit Current Measurement and P&O MPPT. IEEE Trans. Sustain. Energy
**2015**, 6, 1426–1434. [Google Scholar] [CrossRef] [Green Version] - Pakkiraiah, G.B.; Durga, S. Research Survey on Various MPPT Performance Issues to Improve the Solar PV System Efficiency. J. Sol. Energy
**2016**, 2016, 8012432. [Google Scholar] [CrossRef] [Green Version] - Bhukya, L.; Kedika, N.R.; Salkuti, S.R. Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review. Algorithms
**2022**, 15, 365. [Google Scholar] [CrossRef] - Javed, M.R.; Waleed, A.; Virk, U.S.; Hassan, S.Z.U. Comparison of the Adaptive Neural-Fuzzy Interface System (ANFIS) based Solar Maximum Power Point Tracking (MPPT) with other Solar MPPT Methods. In Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan, 5–7 November 2020; pp. 1–5. [Google Scholar] [CrossRef]
- Singh, M.D.; Shine, V.J.; Janamala, V. Application of Artificial Neural Networks in Optimizing MPPT Control for Standalone Solar PV System. In Proceedings of the 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, India, 27–29 November 2014; pp. 162–166. [Google Scholar] [CrossRef]
- Khanam, J.; Foo, S.Y. Neural Networks Technique for Maximum Power Point Tracking of Photovoltaic Array. In Proceedings of the SoutheastCon, St. Petersburg, FL, USA, 19–22 April 2018; pp. 1–4. [Google Scholar] [CrossRef]
- Hayder, W.; Sera, D.; Ogliari, E.; Lashab, A. On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions. Energies
**2022**, 15, 7668. [Google Scholar] [CrossRef] - Dagal, I.; Akın, B.; Akboy, E. MPPT mechanism based on novel hybrid particle swarm optimization and salp swarm optimization algorithm for battery charging through simulink. Sci. Rep.
**2022**, 12, 2664. [Google Scholar] [CrossRef] [PubMed] - Ali, Z.M.; Alquthami, T.; Alkhalaf, S.; Norouzi, H.; Dadfar, S.; Suzuki, K. Novel hybrid improved bat algorithm and fuzzy system based MPPT for photovoltaic under variable atmospheric conditions. Sustain. Energy Technol. Assess.
**2022**, 52, 102156. [Google Scholar] [CrossRef] - Gong, L.; Hou, G.; Huang, C. A two-stage MPPT controller for PV system based on the improved artificial bee colony and simultaneous heat transfer search algorithm. ISA Transactions
**2022**. [Google Scholar] [CrossRef] - Manna, S.; Akella, A.K.; Singh, D.K. A Novel MRAC-MPPT Scheme to Enhance Speed and Accuracy in PV Systems. Iran. J. Sci. Technol. Trans. Electr. Eng.
**2022**. [Google Scholar] [CrossRef] - Manna, S.; Singh, D.K.; Akella, A.K.; Abdelaziz, A.Y.; Prasad, M. A novel robust model reference adaptive MPPT controller for Photovoltaic systems. Sci. Iran.
**2022**. [Google Scholar] [CrossRef] - Badoud, A.E.; Mekhilef, S.; Bouamama, B.O. A Novel Hybrid MPPT Controller Based on Bond Graph and Fuzzy Logic in Proton Exchange Membrane Fuel Cell System: Experimental Validation. Arab. J. Sci. Eng.
**2021**, 47, 3201–3220. [Google Scholar] [CrossRef]

**Figure 6.**dp/dv sign at various points of the curve [11].

**Figure 9.**Algorithm performance at constant atmospheric conditions; T = 25 °C, w = 1000 W/m

^{2}, R = 10 Ω.

**Figure 13.**Slope at various regions of the PV curve [6].

**Figure 20.**Error, change in error, and duty cycle membership functions of fuzzy inference system: (

**a**) membership functions of input variable Ipv; (

**b**) membership functions of input variable Vpv; (

**c**) membership functions of output variable PWM (or) duty.

**Figure 25.**Neural network training: (

**a**) NNTOOL for NN training in MATLAB; (

**b**) performance plot of neural network; (

**c**) training state plot; (

**d**) error histogram; (

**e**) regression plot; (

**f**) results of trained data.

**Figure 38.**Flow chart for hybrid MPPT based on neural network and P&O [23].

Parameter | Specifications |
---|---|

Maximum power | 250.20 W |

Open circuit voltage | 37.3 V |

Voltage at maximum power point | 30.7 V |

Short circuit current | 8.66 A |

Current at maximum power point | 8.15 A |

Parameter | Time (sec) | Value |
---|---|---|

Irradiance | (0, 0.2, 0.4, 0.6, 0.8) | (1000, 800, 600, 400, 200) w/m^{2} |

Temperature Load | (0, 0.2, 0.4, 0.6, 0.8) (0, 0.3, 0.6) | (−15, 10, 25, 30, 45) Celsius (20, 30, 40) Ω |

$\mathrm{d}\mathrm{v}/\mathrm{d}\mathrm{p}$ | NB | NS | ZE | PS | PB |
---|---|---|---|---|---|

NB | PB | PS | NB | NS | NS |

NS | PS | PS | NB | NS | NS |

ZE | NS | NS | NS | PB | PB |

PS | NS | PB | PS | NB | PB |

PB | NB | NB | PB | PS | PB |

Specifications | Data | Validation and Test Data of 1000 Samples | ||
---|---|---|---|---|

Toolbox | NNTOOL box | Type of Sample | Samples (%) | Total Samples |

Wizard: Input-output and curve fitting | Fitting app | Training | 70% | 700 samples |

Input data to network | 1000 points input data of irradiation, temperature | Validation | 15% | 150 samples |

Target data/desired network output | 1000 points data of voltage | Testing | 15% | 150 samples |

Samples | Matrix—rows | |||

Number of hidden neurons | 10 | |||

Training Algorithm | Levenberg–Marquardt |

Items | P&O Method | Incremental Conductance Method | Fuzzy Logic Control Method | ANFIS Method | Neural Network Method | Hybrid Controller Model |
---|---|---|---|---|---|---|

Dynamic behaviour | Poor | Medium | Medium | Good | Good | Fast |

Transient behaviour | Bad | Bad | Good | Good | Good | Fast |

(oscillations) Steady-state | Large | Moderate | Small | Small | Small | Very small |

requirements | P&O algorithm | Incremental conductance algorithm | Fuzzy logic membership functions | ANFIS training data | Neural network training data | Neural network and P&O combined |

Static error | High | High | Low | Low | Low | low |

Controller accuracy | Low | Medium | Accurate | Accurate | Accurate | Accurate |

Tracking speed | Slow | Slow | Fast | Fast | Fast | Faster |

System complexity | Simple power calculations | Simple | Medium | Medium | Medium | Medium |

Temperature characteristics | Poor | Poor | Good | Good | Good | Better |

Parameters tuning | No | No | Yes | Yes | Yes | Yes |

MPPT Method | Convergence Time(s) | Irradiation: 1000 w/m^{2} | Values | Comment |
---|---|---|---|---|

P_max: 250 w/m^{2} | ||||

P&O method | 0.004 | P_avg | 237.4 | Oscillations occur |

%ɳ_{PV} | 94.96 | |||

Incremental Conductance method | 0.006 | P_avg | 239.1 | Oscillations occur |

%ɳ_{PV} | 95.60 | |||

Fuzzy logic control method | 0.04 | P_avg | 242.2 | Long convergence time |

%ɳ_{PV} | 96.88 | |||

Neural network method | 0.205 | P_avg | 244.6 | Better dynamic performance |

%ɳ_{PV} | 97.84 | |||

ANFIS control method | 0.046 | P_avg | 244.4 | Under dynamic response |

%ɳ_{PV} | 97.76 | |||

Hybrid Method | 0.2005 | P_avg | 247 | Fast response |

%ɳ_{PV} | 98.80 |

Method Name | Real-Time (s) (Varying Conditions) | Real-Time (s) (Constant Conditions) |
---|---|---|

P&O | 4.633 | 3.875 |

Incremental conductance | 9.2635 | 5.771 |

Fuzzy logic control | 423.88 | 372.41 |

Neural network | 7.701 | 4.6345 |

ANFIS | 50.5595 | 41.07 |

Hybrid | 6.154 | 5.667 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Devarakonda, A.K.; Karuppiah, N.; Selvaraj, T.; Balachandran, P.K.; Shanmugasundaram, R.; Senjyu, T.
A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems. *Energies* **2022**, *15*, 8776.
https://doi.org/10.3390/en15228776

**AMA Style**

Devarakonda AK, Karuppiah N, Selvaraj T, Balachandran PK, Shanmugasundaram R, Senjyu T.
A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems. *Energies*. 2022; 15(22):8776.
https://doi.org/10.3390/en15228776

**Chicago/Turabian Style**

Devarakonda, Ashwin Kumar, Natarajan Karuppiah, Tamilselvi Selvaraj, Praveen Kumar Balachandran, Ravivarman Shanmugasundaram, and Tomonobu Senjyu.
2022. "A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems" *Energies* 15, no. 22: 8776.
https://doi.org/10.3390/en15228776