Design and Performance Analysis of Robust Adaptive Neuro-Fuzzy Inference System-Based Modified P&O Algorithm of MPPT Controller for a Solar PV System
Main Article Content
Abstract
Perturb and observe (P&O) is a well-known maximum power point tracking (MPPT) algorithm that is used in solar photovoltaic (PV) systems to increase its efficiency. However, as the PV system uses solar irradiance and temperature for making electric power, the fast change of these two affects the performance of P&O and the efficiency of the PV system. Thus, the P&O algorithm fails to detect maximum power point (MPP) if temperature and irradiance change quickly. Therefore, this paper presents an adaptive neuro-fuzzy inference system (ANFIS) based P&O algorithm of MPPT controller for a solar PV system to solve the issues mentioned earlier. The utilization of the proposed ANFIS in the P&O algorithm can track the fast changes in solar irradiance and temperature to extract the maximum power from the solar PV panel. Comparative analysis has been done on MATLAB/Simulink software for both the traditional P&O and the proposed ANFIS-based P&O algorithm to show the effectiveness of the proposed MPPT controller.
Article Details
How to Cite
[1]
A. C. Dafader, M. R. Hazari, S. Ahmad, and M. A. Mannan, “Design and Performance Analysis of Robust Adaptive Neuro-Fuzzy Inference System-Based Modified P&O Algorithm of MPPT Controller for a Solar PV System”, AJSE, vol. 23, no. 2, pp. 92 - 104, Aug. 2024.
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References
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[18] J. M. Lopez-Guede, J. Ramos-Hernanz, N. Altın, S. Ozdemir, E. Kurt, and G. Azkune, “Neural Modeling of Fuzzy Controllers for Maximum Power Point Tracking in Photovoltaic Energy Systems,” J Electron Mater, vol. 47, no. 8, pp. 4519–4532, Aug. 2018, doi: 10.1007/s11664-018-6407-2.
[19] A. Al-Hmouz, J. Shen, R. Al-Hmouz, and J. Yan, “Modeling and simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for mobile learning,” IEEE Transactions on Learning Technologies, vol. 5, no. 3, pp. 226–237, 2012, doi: 10.1109/TLT.2011.36.
[20] M. K. Islam, M. A. Mannan and M. R. Hazari, “Fault Ride-Through and Transient Stability Augmentation of Grid-Connected PV Station Using Virtual Synchronous Generator” AIUB Journal of Science and Engineering (AJSE), Vol. 20, No. 4, pp. 118-126, Dec. 2021.
[21] K. Premkumar and B. V. Manikandan, “Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor,” Neurocomputing, vol. 138, pp. 260–270, Aug. 2014, doi: 10.1016/j.neucom.2014.01.038.
[22] M. K. J. Rathi and Ali, “NFC Design using ANFIS for Power Electronics Circuits,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 4, pp. 2321–5526, 2016, doi: 10.17148/IJIREEICE.2016.4303.
[23] K. Premkumar and B. V. Manikandan, “Stability and Performance Analysis of ANFIS Tuned PID Based Speed Controller for Brushless DC Motor,” Curr Signal Transduct Ther, vol. 13, no. 1, pp. 19–30, Feb. 2018, doi: 10.2174/1574362413666180226105809.
[2] M. R. Hazari, E. Jahan, M. A. Mannan, and N. Das, “Transient stability enhancement of a grid-connected large-scale pv system using fuzzy logic controller,” Electronics (Switzerland), vol. 10, no. 19, Oct. 2021, doi: 10.3390/electronics10192437.
[3] A. Ahmed, M. R. Hazari, E. Jahan, and M. A. Mannan, “A Robust Control Strategy to Improve Low Voltage Ride-through of a Grid-connected Photovoltaic System,” International Journal of Power and Energy Systems, vol. 41, no. 1, 2021, doi: 10.2316/j.2021.203-0299.
[4] S. M. I. Rahman, M. R. Hazari, S. U. Hani, B. B. Pathik, M. A. Mannan, A. Mahfuz, M. K. Alam, M. K. Hassan., “Primary frequency control of large-scale pv-connected multi-machine power system using battery energy storage system,” International Journal of Power Electronics and Drive Systems, vol. 12, no. 3, pp. 1862–1871, Sep. 2021, doi: 10.11591/ijpeds.v12.i3.pp1862-1871.
[5] M. Sakiluzzaman, M. S. Hasan, K. Anam, M. Almustanjid Akash, and M. R. Hazari, “Voltage Stability Augmentation of Hybrid Power System using Robust Reactive Power Control Strategy of PV Plant,” International Journal of Renewable Energy Research, vol. 11, no. 3, September, 2021, doi:10.20508/ijrer.v11i3.12245.g8289.
[6] J. Macaulay and Z. Zhou, “A fuzzy logical-based variable step size P&O MPPT algorithm for photovoltaic system,” Energies (Basel), vol. 11, no. 6, Jun. 2018, doi: 10.3390/en11061340.
[7] H. S. Lee and J. J. Yun, “Advanced MPPT algorithm for distributed photovoltaic systems,” Energies (Basel), vol. 12, no. 18, Sep. 2019, doi: 10.3390/en12183576.
[8] D. Remoaldo and I. S. Jesus, “Analysis of a traditional and a fuzzy logic enhanced perturb and observe algorithm for the mppt of a photovoltaic system,” Algorithms, vol. 14, no. 1, Jan. 2021, doi: 10.3390/a14010024.
[9] M. N. Ali, K. Mahmoud, M. Lehtonen, and M. M. F. Darwish, “An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems,” IEEE Access, vol. 9, pp. 26420–26430, 2021, doi: 10.1109/ACCESS.2021.3058052.
[10] N. Priyadarshi, F. Azam, A. K. Bhoi, and S. Alam, “An Artificial Fuzzy Logic Intelligent Controller Based MPPT for PV Grid Utility,” in Lecture Notes in Networks and Systems, Springer, 2019, pp. 901–909. doi: 10.1007/978-981-13-1217-5_88.
[11] S. M. Sadek, F. H. Fahmy, A. E.-S. A. Nafeh, and M. A. El-Magd, “Fuzzy P & O Maximum Power Point Tracking Algorithm for a Stand-Alone Photovoltaic System Feeding Hybrid Loads,” Smart Grid and Renewable Energy, vol. 05, no. 02, pp. 19–30, 2014, doi: 10.4236/sgre.2014.52003.
[12] A. Aurairat and B. Plangklang, “An Alternative Perturbation and Observation Modifier Maximum Power Point Tracking of PV Systems,” Symmetry (Basel), vol. 14, no. 1, Jan. 2022, doi: 10.3390/sym14010044.
[13] L. Farah, A. Hussain, A. Kerrouche, C. Ieracitano, J. Ahmad, and M. Mahmud, “A highly-efficient fuzzy-based controller with high reduction inputs and membership functions for a grid-connected photovoltaic system,” IEEE Access, vol. 8, pp. 163225–163237, 2020, doi: 10.1109/ACCESS.2020.3016981.
[14] S. D. Al-Majidi, M. F. Abbod, and H. S. Al-Raweshidy, “A particle swarm optimisation-trained feedforward neural network for predicting the maximum power point of a photovoltaic array,” Eng Appl Artif Intell, vol. 92, Jun. 2020, doi: 10.1016/j.engappai.2020.103688.
[15] I. Haseeb et al., “Solar power system assessments using ann and hybrid boost converter based mppt algorithm,” Applied Sciences (Switzerland), vol. 11, no. 23, Dec. 2021, doi: 10.3390/app112311332.
[16] L. Chen and X. Wang, “Enhanced MPPT method based on ANN-assisted sequential Monte–Carlo and quickest change detection,” IET Smart Grid, vol. 2, no. 4, pp. 635–644, Dec. 2019, doi: 10.1049/iet-stg.2019.0012.
[17] L. Bouselham, M. Hajji, B. Hajji, and H. Bouali, “A New MPPT-based ANN for Photovoltaic System under Partial Shading Conditions,” in Energy Procedia, Elsevier Ltd, Mar. 2017, pp. 924–933. doi: 10.1016/j.egypro.2017.03.255.
[18] J. M. Lopez-Guede, J. Ramos-Hernanz, N. Altın, S. Ozdemir, E. Kurt, and G. Azkune, “Neural Modeling of Fuzzy Controllers for Maximum Power Point Tracking in Photovoltaic Energy Systems,” J Electron Mater, vol. 47, no. 8, pp. 4519–4532, Aug. 2018, doi: 10.1007/s11664-018-6407-2.
[19] A. Al-Hmouz, J. Shen, R. Al-Hmouz, and J. Yan, “Modeling and simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for mobile learning,” IEEE Transactions on Learning Technologies, vol. 5, no. 3, pp. 226–237, 2012, doi: 10.1109/TLT.2011.36.
[20] M. K. Islam, M. A. Mannan and M. R. Hazari, “Fault Ride-Through and Transient Stability Augmentation of Grid-Connected PV Station Using Virtual Synchronous Generator” AIUB Journal of Science and Engineering (AJSE), Vol. 20, No. 4, pp. 118-126, Dec. 2021.
[21] K. Premkumar and B. V. Manikandan, “Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor,” Neurocomputing, vol. 138, pp. 260–270, Aug. 2014, doi: 10.1016/j.neucom.2014.01.038.
[22] M. K. J. Rathi and Ali, “NFC Design using ANFIS for Power Electronics Circuits,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 4, pp. 2321–5526, 2016, doi: 10.17148/IJIREEICE.2016.4303.
[23] K. Premkumar and B. V. Manikandan, “Stability and Performance Analysis of ANFIS Tuned PID Based Speed Controller for Brushless DC Motor,” Curr Signal Transduct Ther, vol. 13, no. 1, pp. 19–30, Feb. 2018, doi: 10.2174/1574362413666180226105809.