Enhancing Efficiency in Hybrid Solar–Wind–Battery Systems Using an Adaptive MPPT Controller Based on Shadow Motion Prediction
Source abstract
Abstract
Renewable energy sources are particularly significant in global energy production, with wind and solar being the most prevalent sources. Managing the simultaneous connection of wind and solar energy generators to the smart grid as distributed generators involves complex control and stabilization due to their inherent uncertainties, making their management more intricate than traditional power plants. This study focuses on enhancing the speed and efficiency of the maximum power point tracking (MPPT) system in a solar power plant. A hybrid network is modeled, comprising a wind turbine with a doubly-fed induction generator (DFIG), a solar power plant with photovoltaic (PV) cells, an MPPT system, a Z-source converter, and a storage system. The proposed approach employs a motion detection-based method, utilizing image-processing techniques to optimize the MPPT of PV cells based on shadow movement patterns within the solar power plant area. This method significantly reduces the time required to reach the maximum power point (MPP), lowers the computational load of the control system by predicting shadow movements, and enhances the MPPT speed while maintaining system stability. The approach, which is suitable for relatively large solar farms, is implemented without the need for any additional sensors and relies on the system’s history. The simulation results show that the proposed approach improves the MPPT system’s efficiency and reduces the pressure on the control circuits by more than 70% in a 150,000 m2 solar farm under shaded conditions.
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Research context
OpenAlex topics: Photovoltaic System Optimization Techniques · Power Systems and Renewable Energy · Microgrid Control and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal
Department of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood 36199-43189, Iran
Department of Electrical and Computer Engineering, Shahrood Branch, Islamic Azad University, Shahrood 36199-43189, Iran
Instituto de Ciência e Inovaç ao em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal
References (72)
Show source-provided references
1. Ai, C., Zhang, L., Gao, W., Yang, G., Wu, D., Chen, L., Chen, W., and Plummer, A. (2022). A review of energy storage technologies in hydraulic wind turbines. Energy Convers. Manag., 264.10.1016/j.enconman.2022.115584 ↗
2. Barthelmie, R.J., and Pryor, S.C. (2021). Climate change mitigation potential of wind energy. Climate, 9.10.3390/cli9090136 ↗
3. Roga, S., Bardhan, S., Kumar, Y., and Dubey, S.K. (2022). Recent technology and challenges of wind energy generation: A review. Sustain. Energy Technol. Assess., 52.10.1016/j.seta.2022.102239 ↗
4. Fault diagnosis for induction generator-based wind turbine using ensemble deep learning techniques10.1016/j.egyr.2022.09.139 ↗
5. Bebars, A.D., Eladl, A.A., Abdulsalam, G.M., and Badran, E.A. (2022). Internal electrical fault detection techniques in DFIG-based wind turbines: A review. Prot. Control Mod. Power Syst., 7.10.1186/s41601-022-00236-z ↗
6. Permanent Magnet Synchronous Generator design optimization for wind energy conversion system: A review10.1016/j.egyr.2022.10.239 ↗
7. Touati, Z., Pereira, M., Araújo, R.E., and Khedher, A. (2022). Integration of switched reluctance generator in a wind energy conversion system: An overview of the state of the art and challenges. Energies, 15.10.3390/en15134743 ↗
8. Review on phase change materials for solar energy storage applications10.1007/s11356-021-17152-8 ↗
9. A review of water electrolysis–based systems for hydrogen production using hybrid/solar/wind energy systems10.1007/s11356-022-23323-y ↗
10. Wang, B., Yu, X., Chang, J., Huang, R., Li, Z., and Wang, H. (2022). Techno-economic analysis and optimization of a novel hybrid solar-wind-bioethanol hydrogen production system via membrane reactor. Energy Convers. Manag., 252.10.1016/j.enconman.2021.115088 ↗
11. Rahman, A., Farrok, O., and Haque, M.M. (2022). Environmental impact of renewable energy source based electrical power plants: Solar, wind, hydroelectric, biomass, geothermal, tidal, ocean, and osmotic. Renew. Sustain. Energy Rev., 161.10.1016/j.rser.2022.112279 ↗
12. Implications of the development and evolution of global wind power industry for China—An empirical analysis is based on public policy10.1016/j.egyr.2022.01.115 ↗
13. FEM and ANN approaches to wind turbine gearbox monitoring and diagnosis: A mini review10.1007/s40860-022-00183-4 ↗
14. Improving the efficiency of Darier rotor by controlling the aerodynamic design of blades10.1016/j.egyr.2022.10.162 ↗
15. Ganthia, B.P., Mohanty, M., and Maherchandani, J.K. (2022). Power analysis using various types of wind turbines. Modeling and Control of Static Converters for Hybrid Storage Systems, IGI Global.10.4018/978-1-7998-7447-8.ch010 ↗
16. Design of a superconducting synchronous generator with LTS field windings for 12 MW offshore direct-drive wind turbines10.1109/TIE.2015.2415758 ↗
17. Babaghorbani, B., Beheshti, M.T., and Talebi, H.A. (2021). A Lyapunov-based model predictive control strategy in a permanent magnet synchronous generator wind turbine. Int. J. Electr. Power Energy Syst., 130.10.1016/j.ijepes.2021.106972 ↗
18. Improving the transition capability of the low-voltage wind turbine in the sub-synchronous state using a fuzzy controller10.1093/ce/zkac033 ↗
19. Black-start capability of DFIG wind turbines through a grid-forming control based on the rotor flux orientation10.1109/ACCESS.2021.3120478 ↗
20. Wankhede, A.K., Sharma, A., and Fernandes, B. (2022). Simulation and Analysis of Medium-Voltage Low-Speed Cyclo-Converter Synchronous Motor Drive and Issues with on-Load Speed Trimming. Smart Technologies for Power and Green Energy: Proceedings of STPGE 2022, Springer.10.1007/978-981-19-2764-5_19 ↗
21. Ortatepe, Z., and Karaarslan, A. (2020). Robust predictive sensorless control method for doubly fed induction generator controlled by matrix converter. Int. Trans. Electr. Energy Syst., 30.10.1002/2050-7038.12650 ↗
22. Control and dynamic response analysis of full converter wind turbines with squirrel cage induction generators considering pitch control and drive train dynamics10.1016/j.ijepes.2019.01.018 ↗
23. Talpone, J.I., Puleston, P.F., Cendoya, M.G., and Barrado-Rodrigo, J.A. (2019). A dual-stator winding induction generator based wind-turbine controlled via super-twisting sliding mode. Energies, 12.10.3390/en12234478 ↗
24. Analysis of two-speed wind farm operation from grid-side measurements10.1109/TSTE.2013.2243176 ↗
25. Performance Optimization of a DFIG-based Variable Speed Wind Turbines by IVC-ANFIS Controller
26. Zeeshan, A., and Srivastava, S. (2024, January 23–24). Fuzzy/ANFIS control of DFIG based wind energy conversion system under the condition of voltage sag on grid in one phase. Proceedings of the 2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC), Mathura, India.10.1109/PARC59193.2024.10486500 ↗
27. Palanivel, M., Kaithamalai, U., and Parthsarathi, P. (2020, January 5–7). Performance assessment of IC and ANFIS based MPPT for PV System using Super Lift Boost Converter. Proceedings of the 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.10.1109/ICECA49313.2020.9297426 ↗
28. ANFIS current–voltage controlled MPPT algorithm for solar powered brushless DC motor based water pump10.1007/s00202-019-00885-8 ↗
29. Anbarasu, E., and Basha, A.R. (2020). An improved power conditioning system for grid integration of solar power using ANFIS based FOPID controller. Microprocess. Microsyst., 74.10.1016/j.micpro.2020.103030 ↗
30. Mahdi, A., Mahamad, A., Saon, S., Tuwoso, T., Elmunsyah, H., and Mudjanarko, S. (2020). Maximum power point tracking using perturb and observe, fuzzy logic and ANFIS. SN Appl. Sci., 2.10.1007/s42452-019-1886-1 ↗
31. Hamouda, N., Babes, B., Kahla, S., Boutaghane, A., Beddar, A., and Aissa, O. (2020, January 25–27). ANFIS controller design using PSO algorithm for MPPT of solar PV system powered brushless DC motor based wire feeder unit. Proceedings of the 2020 International Conference on Electrical Engineering (ICEE), Istanbul, Turkey.10.1109/ICEE49691.2020.9249869 ↗
32. Farah, L., Haddouche, A., and Haddouche, A. (2020). Comparison between proposed fuzzy logic and ANFIS for MPPT control for photovoltaic system. Int. J. Power Electron. Drive Syst., 11.10.11591/ijpeds.v11.i2.pp1065-1073 ↗
33. Javed, M.R., Waleed, A., Virk, U.S., and ul Hassan, S.Z. (2020, January 5–7). Comparison of the adaptive neural-fuzzy interface system (ANFIS) based solar maximum power point tracking (MPPT) with other solar MPPT methods. Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan.10.1109/INMIC50486.2020.9318178 ↗
34. Moyo, R.T., Tabakov, P.Y., and Moyo, S. (2021). Design and modeling of the ANFIS-based MPPT controller for a solar photovoltaic system. J. Sol. Energy Eng., 143.10.1115/1.4048882 ↗
35. Maximum power point tracking using ANFIS for a reconfigurable PV-based battery charger under non-uniform operating conditions10.1109/ACCESS.2021.3103039 ↗
36. Pareek, S., and Kaur, T. (2021). Hybrid ANFIS-PID based MPPT controller for a solar PV system with electric vehicle load. Proc. IOP Conf. Ser. Mater. Sci. Eng., 1033.10.1088/1757-899X/1033/1/012012 ↗
37. Certain investigations of ANFIS assisted CPHO algorithm tuned MPPT controller for PV arrays under partial shading conditions10.1007/s12652-020-02738-w ↗
38. Bendary, A.F., Abdelaziz, A.Y., Ismail, M.M., Mahmoud, K., Lehtonen, M., and Darwish, M.M. (2021). Proposed ANFIS based approach for fault tracking, detection, clearing and rearrangement for photovoltaic system. Sensors, 21.10.3390/s21072269 ↗
39. Survey on adaptative neural fuzzy inference system (ANFIS) architecture applied to photovoltaic systems10.1007/s12667-022-00513-8 ↗
40. Ahmed, E.M., Norouzi, H., Alkhalaf, S., Ali, Z.M., Dadfar, S., and Furukawa, N. (2022). Enhancement of MPPT controller in PV-BES system using incremental conductance along with hybrid crow-pattern search approach based ANFIS under different environmental conditions. Sustain. Energy Technol. Assess., 50.10.1016/j.seta.2021.101812 ↗
41. Revathy, S., Kirubakaran, V., Rajeshwaran, M., Balasundaram, T., Sekar, V., Alghamdi, S., Rajab, B.S., Babalghith, A.O., and Anbese, E.M. (2022). Design and analysis of ANFIS–based MPPT method for solar photovoltaic applications. Int. J. Photoenergy, 2022.10.1155/2022/9625564 ↗
42. Subramaniam, U., Reddy, K.S., Kaliyaperumal, D., Sailaja, V., Bhargavi, P., and Likhith, S. (2023). A MIMO–ANFIS-controlled solar-fuel-cell-based switched capacitor Z-source converter for an off-board EV charger. Energies, 16.10.3390/en16041693 ↗
43. Rahman, A., Myo Aung, K., Ihsan, S., Raja Ahsan Shah, R.M., Al Qubeissi, M., and Aljarrah, M.T. (2023). Solar energy dependent supercapacitor system with ANFIS controller for auxiliary load of electric vehicles. Energies, 16.10.3390/en16062690 ↗
44. A new MPPT design using PV-BES system using modified sparrow search algorithm based ANFIS under partially shaded conditions10.1007/s00521-023-08453-9 ↗
45. Sultana, W., and Jebaseelan, S.S. (2024). ANFIS controller for photovoltaic inverter transient and voltage stability enhancement. Meas. Sens., 33.10.1016/j.measen.2024.101154 ↗
46. ANN and ANFIS Based Control Approaches for Enhanced Performance of Solar PV Driven Water Pumping Systems Employing Quasi Z-Source Converter10.1007/s42835-023-01778-4 ↗
47. Tehrani, K., Weber, M., and Rasoanarivo, I. (2021, January 6–10). Hybrid Power System Optimization for Microgrids. Proceedings of the 2021 23rd European Conference on Power Electronics and Applications (EPE’21 ECCE Europe), Virtual.10.23919/EPE21ECCEEurope50061.2021.9570407 ↗
48. Belgacem, M.B., Gassara, B., and Fakhfakh, A. (2019, January 24–26). Shared energy algorithm and parameters influence on multi-sources and multi-consumers smart microgrid. Proceedings of the 2019 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Sousse, Tunisia.
49. Qin, B., Li, H., Zhou, X., Li, J., and Liu, W. (2020). Low-voltage ride-through techniques in DFIG-based wind turbines: A review. Appl. Sci., 10.10.3390/app10062154 ↗
50. Feleke, S., Satish, R., Pydi, B., Anteneh, D., Abdelaziz, A.Y., and El-Shahat, A. (2023). Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization. Sustainability, 15.10.3390/su15064751 ↗
51. Benbouhenni, H., Bizon, N., Mosaad, M.I., Colak, I., Djilali, A., and Gasmi, H. (2023). Enhancement of the power quality of DFIG-based dual-rotor wind turbine systems using fractional order fuzzy controller. Expert Syst. Appl., 238.10.1016/j.eswa.2023.121695 ↗
52. Recent trends of control strategies for doubly fed induction generator based wind turbine systems: A comparative review10.1007/s11831-019-09367-3 ↗
53. A novel supervised control strategy for interconnected DFIG-based wind turbine systems: MiL validations10.1109/TETCI.2020.3010060 ↗
54. Alzubaidi, O.H.A.A., and Dawood, A.Q. (2022). Design and Simulation of Wind Farm Model Using Doubly-Fed Induction Generator Techniques. Proceedings of the International Conference on Emerging Technologies and Intelligent Systems: ICETIS 2021 (Volume 1), Springer.10.1007/978-3-030-82616-1_7 ↗
55. Kumar, V., Pandey, A.S., and Sinha, S.K. (2020). Stability improvement of DFIG-based wind farm integrated power system using ANFIS controlled STATCOM. Energies, 13.10.3390/en13184707 ↗
56. ANFIS controller design of DFIG under distorted grid voltage situations
57. DFIG control scheme of wind power using ANFIS method in electrical power grid system
58. Gagnon, R. (2006). Detailed Model of a Doubly-Fed Induction Generator (DFIG) Driven by a Wind Turbine, The MathWork.
59. Steady output and fast tracking MPPT (SOFT-MPPT) for P&O and InC algorithms10.1109/TSTE.2020.2991768 ↗
60. Kishor, S., Rajesh, K., Rajendrn, S., Ramkumar, A., and Arunkumar, T. (2021, January 17–18). A Comparative Analysis of Maximum Power Point Tracking Algorithms Applied to Hybrid Wind and Solar System. Proceedings of the 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India.10.1109/ICAC3N53548.2021.9725451 ↗
61. Horrillo-Quintero, P., García-Trivi no, P., Sarrias-Mena, R., García-Vázquez, C.A., and Fernández-Ramírez, L.M. (2023). Model predictive control of a microgrid with energy-stored quasi-Z-source cascaded H-bridge multilevel inverter and PV systems. Appl. Energy, 346.10.1016/j.apenergy.2023.121390 ↗
62. An analysis on the main formulas of Z-source inverter
63. Z-source inverter10.1109/TIA.2003.808920 ↗
64. Quasi-Z-source inverter-based photovoltaic generation system with maximum power tracking control using ANFIS
65. General type-2 fuzzy logic systems made simple: A tutorial10.1109/TFUZZ.2013.2286414 ↗
66. Benbouhenni, H., and Bizon, N. (2021). Advanced direct vector control method for optimizing the operation of a double-powered induction generator-based dual-rotor wind turbine system. Mathematics, 9.10.3390/math9192403 ↗
67. Direct vector control using feedback PI controllers of a DPAG supplied by a two-level PWM inverter for a multi-rotor wind turbine system10.1007/s13369-023-08035-w ↗
68. Parivar, H., Shivaie, M., Darahi, A., and Ansari, M. (2021, January 2–5). An efficient direct torque control strategy for a doubly fed induction generator (DFIG) in wind energy conversation systems. Proceedings of the 2021 IEEE Texas Power and Energy Conference (TPEC), Virtually.10.1109/TPEC51183.2021.9384993 ↗
69. A New Design of Fuzzy Affine Model-Based Output Feedback Control for Discrete-Time Nonlinear Systems10.1109/TFUZZ.2022.3202360 ↗
70. Singh, S.K., and Haque, A. (2015, January 17–20). Performance evaluation of MPPT using boost converters for solar photovoltaic system. Proceedings of the 2015 Annual IEEE India Conference (INDICON), New Delhi, India.10.1109/INDICON.2015.7443516 ↗
71. Mohammed, S.S., and Devaraj, D. (2015, January 5–7). Simulation of Incremental Conductance MPPT based two phase interleaved boost converter using MATLAB/Simulink. Proceedings of the 2015 IEEE International Conference on Electrical, Computer and communication Technologies (ICECCT), Coimbatore, India.10.1109/ICECCT.2015.7225987 ↗
72. Critical performance comparison between single-stage and two-stage incremental conductance MPPT algorithms for DC/DC boost-converter applied in PV systems10.1080/15325008.2022.2136286 ↗