Hybrid Neural Network and Adaptive Terminal Sliding Mode MPPT Controller for Partially Shaded Standalone PV Systems

被引:3
|
作者
Baraean, Abdullah [1 ]
Kassas, Mahmoud [1 ,2 ]
Alam, Md Shafiul [3 ]
Abido, Mohamed A. [1 ,2 ,4 ]
机构
[1] King Fahd Univ Petr & Minerals, Coll Engendering & Phys, Dept Elect Engn, Dhahran 31261, Saudi Arabia
[2] King Fahd Univ Petr & Minerals, Res Inst, Interdisciplinary Res Ctr Renewable Energy & Power, Dhahran 31261, Saudi Arabia
[3] King Fahd Univ Petr & Minerals, Res Inst, Appl Res Ctr Environm & Marine Studies, Dhahran 31261, Saudi Arabia
[4] King Fahd Univ Petr & Minerals, KA CARE Energy Res & Innovat Ctr, Dhahran 31261, Saudi Arabia
关键词
Adaptive terminal sliding mode controller; Artificial neural networks; Maximum power point tracking (MPPT); PV system control; Renewable energy integration; Sliding mode control; MAXIMUM POWER POINT; PHOTOVOLTAIC SYSTEMS; TRACKING ALGORITHM; ENERGY;
D O I
10.1007/s13369-023-08179-9
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
This research presents a new method for controlling the maximum power point tracking (MPPT) of solar photovoltaic (PV) systems that are partially shaded. The proposed approach uses a neural network and an adaptive terminal sliding mode controller (NN-ATSMC) to ensure that the PV system operates at optimal performance under uncertain conditions. The NN-ATSMC controller is applied to a DC/DC boost converter to drive the system to the maximum power point (MPP). This method ensures that the error will converge in finite time and the chattering effect will be minimized without losing robustness under various disturbances and load conditions. Simulation results show that the proposed NN-ATSMC controller performs better than other types of controllers existing in the literature, such as a sliding mode controller (SMC) and a conventional proportional-integral controller (CPI). For the validation of the proposed controller, control hardware-in-the-loop (C-HIL) experimental implementation has been carried out through Texas Instruments digital signal processor C2000. The experimental results show the viability of real-time implementation and verify the effectiveness of the proposed method, which ensures the low cost and stability of the standalone PV systems.
引用
收藏
页码:15527 / 15539
页数:13
相关论文
共 50 条
  • [1] Hybrid Neural Network and Adaptive Terminal Sliding Mode MPPT Controller for Partially Shaded Standalone PV Systems
    Abdullah Baraean
    Mahmoud Kassas
    Md Shafiul Alam
    Mohamed A. Abido
    [J]. Arabian Journal for Science and Engineering, 2023, 48 : 15527 - 15539
  • [2] An Intelligent MPPT controller based on direct neural control for partially shaded PV system
    Kofinas, P.
    Dounis, Anastasios I.
    Papadakis, G.
    Assimakopoulos, M. N.
    [J]. ENERGY AND BUILDINGS, 2015, 90 : 51 - 64
  • [3] Simulated Annealing - MPPT in Partially Shaded PV Systems
    Chaves, E. N.
    Reis, J. H.
    Coelho, E. A. A.
    Freitas, L. C. G.
    Junior, J. B. V.
    Freitas, L. C.
    [J]. IEEE LATIN AMERICA TRANSACTIONS, 2016, 14 (01) : 235 - 241
  • [4] Backstepping Terminal Sliding Mode MPPT Controller for Photovoltaic Systems
    Behih, Khalissa
    Attoui, Hadjira
    [J]. ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH, 2021, 11 (02) : 7060 - 7067
  • [5] A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System
    Salah, Zahra Bel Hadj
    Krim, Saber
    Hajjaji, Mohamed Ali
    Alshammari, Badr M.
    Alqunun, Khalid
    Alzamil, Ahmed
    Guesmi, Tawfik
    [J]. SUSTAINABILITY, 2023, 15 (12)
  • [6] A novel implementation of MPPT sliding mode controller for PV generation systems
    Afghoul, H.
    Chikouche, D.
    Krim, F.
    Beddar, A.
    [J]. 2013 IEEE EUROCON, 2013, : 789 - 794
  • [7] Enhancing MPPT in partially shaded PV modules: a novel approach using adaptive reinforcement learning with neural network architecture
    Leelavathi, M.
    Kumar, V. Suresh
    [J]. BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES, 2024, 72 (04)
  • [8] RBF neural network based backstepping terminal sliding mode MPPT control technique for PV system
    Khan, Zain Ahmad
    Khan, Laiq
    Ahmad, Saghir
    Mumtaz, Sidra
    Jafar, Muhammad
    Khan, Qudrat
    [J]. PLOS ONE, 2021, 16 (04):
  • [9] Hybrid algorithm for MPPT tracking using a single current sensor for partially shaded PV systems
    Balaji, V
    Fathima, A. Peer
    [J]. SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2022, 53
  • [10] Design of an adaptive sliding mode controller for efficiency improvement of the MPPT for PV water pumping
    Miqoi, Sabah
    El Ougli, Abdelghani
    Tidhaf, Belkassem
    [J]. INTERNATIONAL JOURNAL OF INTELLIGENT ENGINEERING INFORMATICS, 2019, 7 (01) : 19 - 36