A Novel Method for Maximum Power Point Tracking of the Grid-Connected Three-Phase Solar Systems Based on the PV Current Prediction

Saeid Bairami; Mahdi Salimi; Davar Mirabbasi

doi:10.23919/cje.2021.00.218

Volume 32 Issue 2

Mar. 2023

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2023 > 32(2): 353-364

Saeid Bairami, Mahdi Salimi, Davar Mirabbasi, “A Novel Method for Maximum Power Point Tracking of the Grid-Connected Three-Phase Solar Systems Based on the PV Current Prediction,” Chinese Journal of Electronics, vol. 32, no. 2, pp. 353-364, 2023, doi: 10.23919/cje.2021.00.218

Citation:

Saeid Bairami, Mahdi Salimi, Davar Mirabbasi, “A Novel Method for Maximum Power Point Tracking of the Grid-Connected Three-Phase Solar Systems Based on the PV Current Prediction,” Chinese Journal of Electronics, vol. 32, no. 2, pp. 353-364, 2023, doi: 10.23919/cje.2021.00.218

Citation:

Saeid Bairami, Mahdi Salimi, Davar Mirabbasi, “A Novel Method for Maximum Power Point Tracking of the Grid-Connected Three-Phase Solar Systems Based on the PV Current Prediction,” Chinese Journal of Electronics, vol. 32, no. 2, pp. 353-364, 2023, doi: 10.23919/cje.2021.00.218

PDF( 4749 KB)

A Novel Method for Maximum Power Point Tracking of the Grid-Connected Three-Phase Solar Systems Based on the PV Current Prediction

doi: 10.23919/cje.2021.00.218

1.
Department of Electrical Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
2.
Faculty of Engineering and Science, University of Greenwich, Medway, Kent, ME4 4TB, UK

More Information

Author Bio:
Bairami Saeid was born in Ardabil, Iran, in 1981. He received the M.S. degrees in mechatronics from Semnan University, Semnan, Iran, in 2007. Since 2007, he has been with the Faculty of Electrical Engineering at Islamic Azad University, Ardabil Branch, Ardabil, Iran, where he is a Professor of Electrical Engineering. His research interests include renewable energy, mechatronics, photovoltaic, power system planning and power electronics applications. (Email: saeid.bairami@gmail.com)

Salimi Mahdi (corresponding author) graduated in electrical engineering from Islamic Azad University (IAU) (Science and Research Branch), Tehran, Iran, in 2012. He worked as an Assistant Professor from 2012 until 2019 at the IAU University, Ardabil Branch, Iran, in the Electrical and Electronics Department. In 2019 he joined the University of Nottingham, Power Electronics, Machine, and Control (PEMC) Group as a Researcher for three years. Since April 2022, he has worked at Greenwich University as a Lecturer of power electronics. He has published 31 papers in peer-reviewed journals with 24 papers published in international conferences. His research areas include design, closed-loop control, simulation, and practical implementation of the power electronics systems for more or full electric aircraft and vehicles, grid-connected renewable energy systems, and active power filters. (Email: m.salimi@greenwich.ac.uk)

Mirabbasi Davar was born in Ardabil, Iran, in 1982. He received the Ph.D. degree in electrical engineering from Shahid Chamran University of Ahwaz, Ahwaz, Iran, in 2014. Since 2006, he has been with the Faculty of Electrical Engineering at Islamic Azad University, Ardabil Branch, Ardabil, Iran, where he is a Professor of Electrical Engineering. His research interests include smart grids, power system planning, and power electronics applications. (Email: dmirabbasi@iauardabil.ac.ir)
Received Date: 2021-06-24
Accepted Date: 2022-07-06

Available Online: 2022-08-23

Publish Date: 2023-03-05

Abstract

Abstract

In this paper, it is first attempted to provide a small signal model of the photovoltaic (PV) system, DC-DC boost converter, and pulse width modulation (PWM) generator. Then, a technique is provided for maximum power point tracking (MPPT) in grid-connected solar systems based on variable and adaptive perturbation and observation with predictive control of the PV current. An innovative aspect of the proposed predictive current control method is to use the current controller to achieve the value of PV impedance, which has been used in DC-DC boost converter. The proposed method is to obtain the coming current value on the basis of the current predictive model. The goal of the proposed method is to make the DC-DC boost converter inductor current track the current reference. Voltage and current ripple minimization is added to the cost function simultaneously as a system constraint to optimize system performance. This reduces the amount of voltage and current fluctuations around the maximum power point. The proposed method is capable of detecting rapid changes in solar radiation. A sudden and simultaneous increase in voltage and current is detected by the algorithm and then the duty cycle becomes increasing instead of decreasing. The simulation is carried out in MATLAB Simulink environment in real-time for a 26.6 kW three-phase grid-connected solar system. The simulation results of current predictive control are compared with perturbation and observation techniques and linear voltage and current proportional integral derivative (PID) controller-based adaptive control. The results show that the total harmonic distortion (THD%) of the inverter voltage with proposed method has been reduced by 0.16% compared to the PID method. In addition, the THD% of the current in the proposed method is reduced by 0.1% compared to the PID method. The solar system output voltage variation of the proposed method is less than 5 V.
- Maximum power point (MPP) tracking,
- Current predictive control,
- Static converter,
- Solar system,
- Power electronics

FullText(HTML)

References(17)

References

[1]	Z. Song, Y. Tian, W. Chen, Z. Zou, and Z. Chen, “Predictive duty cycle control of three-phase active-front-end rectifiers,” IEEE Transactions on Power Electronics, vol.31, pp.698–710, 2015.
[2]	S. Motahhir, A. El Hammoumi, and A. El Ghzizal, “Photovoltaic system with quantitative comparative between an improved MPPT and existing INC and P&O methods under fast varying of solar irradiation,” Energy Reports, vol.4, pp.341–350, 2018. doi: 10.1016/j.egyr.2018.04.003
[3]	S. Sajadian and R. Ahmadi, “Model predictive-based maximum power point tracking for grid-tied photovoltaic applications using a Z-source inverter,” IEEE Transactions on Power Electronics, vol.31, pp.7611–7620, 2016. doi: 10.1109/TPEL.2016.2537814
[4]	H. Rezk, A.-O. Mazen, M. R. Gomaa, M. A. Tolba, A. Fathy, M. A. Abdelkareem, “A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system,” Renewable and Sustainable Energy Reviews, vol.115, article no.109372, 2019. doi: 10.1016/j.rser.2019.109372
[5]	K. Sundareswaran, V. Vigneshkumar, P. Sankar, S. P. Simon, P. S. R. Nayak, and S. Palani, “Development of an improved P&O algorithm assisted through a colony of foraging ants for MPPT in PV system,” IEEE Transactions on Industrial Informatics, vol.12, pp.187–200, 2015.
[6]	J.-C. Kim, J.-H. Huh, and J.-S. Ko, “Improvement of MPPT control performance using fuzzy control and VGPI in the PV system for micro grid,” Sustainability, vol.11, article no.5891, 2019. doi: 10.3390/su11215891
[7]	K.-Y. Chou, S.-T. Yang, and Y.-P. Chen, “Maximum power point tracking of photovoltaic system based on reinforcement learning,” Sensors, vol.19, article no.5054, 2019. doi: 10.3390/s19225054
[8]	M. H. Anowar and P. Roy, “A modified incremental conductance based photovoltaic MPPT charge controller,” 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’s Bazar, Bangladesh, pp.1–5, 2019,.
[9]	K. Ali, L. Khan, Q. Khan, S. Ullah, S. Ahmad, S. Mumtaz, et al., “Robust integral backstepping based nonlinear MPPT control for a PV system,” Energies, vol.12, article no.3180, 2019. doi: 10.3390/en12163180
[10]	J. Gosumbonggot and G. Fujita, “Global maximum power point tracking under shading condition and hotspot detection algorithms for photovoltaic systems,” Energies, vol.12, article no.882, 2019. doi: 10.3390/en12050882
[11]	W. Xu, C. Mu, and L. Tang, “Advanced control techniques for PV maximum power point tracking,” in Advances in Solar Photovoltaic Power Plants, Springer, pp.43–78, 2016.
[12]	A. Kusuma, M. Hakim, G. Alvianingsih, D. Aryani, and A. Utomo, “An improved P&O algorithm for single-stage grid-connected photovoltaic system under rapidly changing environment conditions,” International Conference on Green Energy and Environment, Pangkal Pinang, Indonesia, article no. 012016, 2019.
[13]	C. C. Hua, Y. H. Fang, and C. J. Wong, “Improved solar system with maximum power point tracking,” IET Renewable Power Generation, vol.12, pp.806–814, 2018. doi: 10.1049/iet-rpg.2017.0618
[14]	L. Abualigah, A. Diabat, S. Mirjalili, M. Abd Elaziz, and A. H. Gandomi, “The arithmetic optimization algorithm,” Computer Methods in Applied Mechanics and Engineering, vol.376, article no.113609, 2021. doi: 10.1016/j.cma.2020.113609
[15]	A. M. Furtado, F. Bradaschia, M. C. Cavalcanti, and L. R. Limongi, “A reduced voltage range global maximum power point tracking algorithm for photovoltaic systems under partial shading conditions,” IEEE Transactions on Industrial Electronics, vol.65, no.4, pp.3252–3262, 2017. doi: 10.1109/TIE.2017.2750623
[16]	R. K. Pachauri, O. P. Mahela, A. Sharma, J. Bai, Y. K. Chauhan, B. Khan, et al., “Impact of partial shading on various PV array configurations and different modeling approaches: A comprehensive review,” IEEE Access, vol.8, pp.181375–181403, 2020. doi: 10.1109/ACCESS.2020.3028473
[17]	L. Samani and R. Mirzaei, “Maximum power point tracking for photovoltaic systems under partial shading conditions via modified model predictive control,” Electrical Engineering, vol.103, pp.1923–1947, 2021. doi: 10.1007/s00202-020-01201-5