An Adaptive Integral Backstepping SMC and Robust Functional Expanded Multikernel BLS Based MPPT Control in PV-Battery DC Microgrid System
In this paper, a robust functional expanded multikernel broad learning system (RFEMBLS) is proposed to compute the complex nonlinear solar photovoltaic (PV) reference voltage more accurately by importing the irradiance and temperature in different uncertainty conditions. The novel droop control mech...
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Published in | IEEE transactions on power electronics Vol. 39; no. 3; pp. 1 - 13 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Abstract | In this paper, a robust functional expanded multikernel broad learning system (RFEMBLS) is proposed to compute the complex nonlinear solar photovoltaic (PV) reference voltage more accurately by importing the irradiance and temperature in different uncertainty conditions. The novel droop control mechanism is introduced to obtain reference current to reduce dependence on different connected renewable energy resources. An adaptive integral backstepping sliding mode controller (AIBSMC) is designed to control the DC bus voltage under different abnormal scenarios for the proposed DC microgrid. An asymptotical stability analysis is developed using Lyapunov theory for the PV-battery DC microgrid. A new adoption rule is proposed for the estimation of both references of PV voltage and battery current. Furthermore, the system steady-state error and tracking convergence of the error are improved by adding an integral action. The backstepping method based on the DC bus voltage feedback results in a faster response and negligible chattering. A sliding mode controller is proposed to improve the control precision and robustness. Finally, the proposed RFEMBLS-AIBSMC method is tested using dSPACE platform in the scale-down lab environment to verify the robustness, practicability, feasibility, and efficacy of the proposed system in the real-time scenario. |
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AbstractList | In this paper, a robust functional expanded multikernel broad learning system (RFEMBLS) is proposed to compute the complex nonlinear solar photovoltaic (PV) reference voltage more accurately by importing the irradiance and temperature in different uncertainty conditions. The novel droop control mechanism is introduced to obtain reference current to reduce dependence on different connected renewable energy resources. An adaptive integral backstepping sliding mode controller (AIBSMC) is designed to control the DC bus voltage under different abnormal scenarios for the proposed DC microgrid. An asymptotical stability analysis is developed using Lyapunov theory for the PV-battery DC microgrid. A new adoption rule is proposed for the estimation of both references of PV voltage and battery current. Furthermore, the system steady-state error and tracking convergence of the error are improved by adding an integral action. The backstepping method based on the DC bus voltage feedback results in a faster response and negligible chattering. A sliding mode controller is proposed to improve the control precision and robustness. Finally, the proposed RFEMBLS-AIBSMC method is tested using dSPACE platform in the scale-down lab environment to verify the robustness, practicability, feasibility, and efficacy of the proposed system in the real-time scenario. In this article, a robust functional expanded multikernel broad learning system (RFEMBLS) is proposed to compute the complex nonlinear solar photovoltaic (PV) reference voltage more accurately by importing the irradiance and temperature in different uncertainty conditions. The novel droop control mechanism is introduced to obtain reference current to reduce dependence on different connected renewable energy resources. An adaptive integral backstepping sliding mode controller (AIBSMC) is designed to control the dc bus voltage under different abnormal scenarios for the proposed dc microgrid. An asymptotical stability analysis is developed using Lyapunov theory for the PV-battery dc microgrid. A new adoption rule is proposed for the estimation of both references of PV voltage and battery current. Furthermore, the system steady-state error and tracking convergence of the error are improved by adding an integral action. The backstepping method based on the dc bus voltage feedback results in a faster response and negligible chattering. A sliding mode controller is proposed to improve the control precision and robustness. Finally, the proposed RFEMBLS-AIBSMC method is tested using dSPACE platform in the scale-down lab environment to verify the robustness, practicability, feasibility, and efficacy of the proposed system in the real-time scenario. |
Author | Panda, Sanjib Kumar Biswal, Baladev Dash, Pradipta Kishore Sahani, Mrutyunjaya Prasad, Eluri NVDV |
Author_xml | – sequence: 1 givenname: Mrutyunjaya orcidid: 0000-0003-0276-8366 surname: Sahani fullname: Sahani, Mrutyunjaya organization: Department of Electrical and Computer Engineering, National University of Singapore, Singapore – sequence: 2 givenname: Baladev surname: Biswal fullname: Biswal, Baladev organization: Multidisciplinary Research Cell, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, India – sequence: 3 givenname: Eluri NVDV surname: Prasad fullname: Prasad, Eluri NVDV organization: Department of Electrical Engineering, Sri vasavi engineering autonomous college, Tadepalligudem, India – sequence: 4 givenname: Pradipta Kishore orcidid: 0000-0002-8950-7136 surname: Dash fullname: Dash, Pradipta Kishore organization: Multidisciplinary Research Cell, Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, India – sequence: 5 givenname: Sanjib Kumar orcidid: 0000-0002-9476-9755 surname: Panda fullname: Panda, Sanjib Kumar organization: Department of Electrical and Computer Engineering, National University of Singapore, Singapore |
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Snippet | In this paper, a robust functional expanded multikernel broad learning system (RFEMBLS) is proposed to compute the complex nonlinear solar photovoltaic (PV)... In this article, a robust functional expanded multikernel broad learning system (RFEMBLS) is proposed to compute the complex nonlinear solar photovoltaic (PV)... |
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SubjectTerms | Adaptive control Adaptive integral backstepping sliding mode control Adaptive systems Backstepping Batteries Control systems design Controllers Data buses Distributed generation dSPACE hardware in loop Electric potential Energy sources Functional expanded broad learning system Irradiance Kernel learning Lyapunov criteria Maximum power point tracking Microgrids Photovoltaic Photovoltaic cells Robust control Robustness Sliding mode control Stability analysis Steady-state Voltage Voltage control |
Title | An Adaptive Integral Backstepping SMC and Robust Functional Expanded Multikernel BLS Based MPPT Control in PV-Battery DC Microgrid System |
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