Enhanced Photovoltaic Systems Performance: Anti-Windup PI Controller in ANN-Based ARV MPPT Method
Photovoltaic (PV) panels exhibit a non-linear current-voltage characteristic with a Maximum Power Point (MPP) that varies due to environmental factors such as solar radiation and ambient temperature. In this study, an Artificial Neural Network (ANN)-based MPPT method, called the ANN-based Adaptive R...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10167656/ |
| _version_ | 1797733229296353280 |
|---|---|
| author | Musa Yilmaz Resat Celikel Ahmet Gundogdu |
| author_facet | Musa Yilmaz Resat Celikel Ahmet Gundogdu |
| author_sort | Musa Yilmaz |
| collection | DOAJ |
| description | Photovoltaic (PV) panels exhibit a non-linear current-voltage characteristic with a Maximum Power Point (MPP) that varies due to environmental factors such as solar radiation and ambient temperature. In this study, an Artificial Neural Network (ANN)-based MPPT method, called the ANN-based Adaptive Reference Voltage (ARV) method, is proposed to determine the optimal operating point of the PV panel. The ANN-based ARV method is a voltage-controlled approach that can adapt to changing atmospheric conditions. The performance of the proposed method is evaluated using both a normal Proportional-Integral (PI) controller and an anti-windup PI controller. Comparative analysis is conducted with the widely used Perturb and Observe (P&O) and Incremental Conductance (INC) methods in the MATLAB/Simulink environment, considering three different atmospheric scenarios with varying radiation levels according to EN50530 standards. The proposed method demonstrates superior efficiency with overall results of 99.4%, 95.9%, and 96% in scenario 1, scenario 2, and scenario 3, respectively. Particularly, the proposed method exhibits notable superiority in rapidly changing atmospheric conditions. |
| first_indexed | 2024-03-12T12:25:07Z |
| format | Article |
| id | doaj.art-d1632738ab5a4937a60fa20b63fef68a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T12:25:07Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d1632738ab5a4937a60fa20b63fef68a2023-08-29T23:00:36ZengIEEEIEEE Access2169-35362023-01-0111904989050910.1109/ACCESS.2023.329031610167656Enhanced Photovoltaic Systems Performance: Anti-Windup PI Controller in ANN-Based ARV MPPT MethodMusa Yilmaz0https://orcid.org/0000-0002-2306-6008Resat Celikel1https://orcid.org/0000-0002-9169-6466Ahmet Gundogdu2https://orcid.org/0000-0002-8333-3083Bourns College of Engineering, Center for Environmental Research and Technology, University of California at Riverside, Riverside, CA, USADepartment of Mechatronics, Faculty of Technology, Firat University, Elazig, TurkeyDepartment of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Batman University, Batman, TurkeyPhotovoltaic (PV) panels exhibit a non-linear current-voltage characteristic with a Maximum Power Point (MPP) that varies due to environmental factors such as solar radiation and ambient temperature. In this study, an Artificial Neural Network (ANN)-based MPPT method, called the ANN-based Adaptive Reference Voltage (ARV) method, is proposed to determine the optimal operating point of the PV panel. The ANN-based ARV method is a voltage-controlled approach that can adapt to changing atmospheric conditions. The performance of the proposed method is evaluated using both a normal Proportional-Integral (PI) controller and an anti-windup PI controller. Comparative analysis is conducted with the widely used Perturb and Observe (P&O) and Incremental Conductance (INC) methods in the MATLAB/Simulink environment, considering three different atmospheric scenarios with varying radiation levels according to EN50530 standards. The proposed method demonstrates superior efficiency with overall results of 99.4%, 95.9%, and 96% in scenario 1, scenario 2, and scenario 3, respectively. Particularly, the proposed method exhibits notable superiority in rapidly changing atmospheric conditions.https://ieeexplore.ieee.org/document/10167656/PV systemanti-windup PIartificial neural networkMPPTadaptive reference voltage |
| spellingShingle | Musa Yilmaz Resat Celikel Ahmet Gundogdu Enhanced Photovoltaic Systems Performance: Anti-Windup PI Controller in ANN-Based ARV MPPT Method IEEE Access PV system anti-windup PI artificial neural network MPPT adaptive reference voltage |
| title | Enhanced Photovoltaic Systems Performance: Anti-Windup PI Controller in ANN-Based ARV MPPT Method |
| title_full | Enhanced Photovoltaic Systems Performance: Anti-Windup PI Controller in ANN-Based ARV MPPT Method |
| title_fullStr | Enhanced Photovoltaic Systems Performance: Anti-Windup PI Controller in ANN-Based ARV MPPT Method |
| title_full_unstemmed | Enhanced Photovoltaic Systems Performance: Anti-Windup PI Controller in ANN-Based ARV MPPT Method |
| title_short | Enhanced Photovoltaic Systems Performance: Anti-Windup PI Controller in ANN-Based ARV MPPT Method |
| title_sort | enhanced photovoltaic systems performance anti windup pi controller in ann based arv mppt method |
| topic | PV system anti-windup PI artificial neural network MPPT adaptive reference voltage |
| url | https://ieeexplore.ieee.org/document/10167656/ |
| work_keys_str_mv | AT musayilmaz enhancedphotovoltaicsystemsperformanceantiwinduppicontrollerinannbasedarvmpptmethod AT resatcelikel enhancedphotovoltaicsystemsperformanceantiwinduppicontrollerinannbasedarvmpptmethod AT ahmetgundogdu enhancedphotovoltaicsystemsperformanceantiwinduppicontrollerinannbasedarvmpptmethod |