Global MPPT optimization for partially shaded photovoltaic systems
Abstract The global demand for electrical energy has witnessed a substantial increase, presenting a challenge for power systems worldwide. In addition to technical considerations, the escalating issue of global warming has become a paramount concern in the planning studies of various sectors. The fo...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89694-7 |
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| author | T. Nagadurga V. Dhana Raju Abdulwasa Bakr Barnawi Javed Khan Bhutto Abdul Razak Anteneh Wogasso Wodajo |
| author_facet | T. Nagadurga V. Dhana Raju Abdulwasa Bakr Barnawi Javed Khan Bhutto Abdul Razak Anteneh Wogasso Wodajo |
| author_sort | T. Nagadurga |
| collection | DOAJ |
| description | Abstract The global demand for electrical energy has witnessed a substantial increase, presenting a challenge for power systems worldwide. In addition to technical considerations, the escalating issue of global warming has become a paramount concern in the planning studies of various sectors. The formulation and resolution of a single-objective non-linear optimization problem are carried out, considering different operational scenarios. Recent heuristic algorithms, including Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimization (GWO) and Chimp Optimization algorithm (ChOA) are employed to address the complexities associated with maximizing power output under partial shading conditions in solar PV systems. The inherent challenges of achieving MPPT under such conditions make conventional analytic approaches computationally intensive. Hence, this study leverages heuristic algorithms to optimize solar PV system performance, providing efficient solutions to the associated optimization problems. The current research work was performed on a test system using a MATLAB/SIMULINK environment and the results are presented and discussed. From the simulation results, it was found that ChOA have shown higher conversion efficiency of 99.63% with maximum power output of 525.13 W when compared to other optimization algorithms for the given shading pattern condition. Further, ChOA offers easy implementation and faster convergence, outperforming established methods in GMPP search by reducing power oscillations and achieving precise MPP convergence. |
| format | Article |
| id | doaj-art-8bd9f697a6b448d1b3a7baeffa8165f1 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-8bd9f697a6b448d1b3a7baeffa8165f12025-08-20T02:49:30ZengNature PortfolioScientific Reports2045-23222025-03-0115113010.1038/s41598-025-89694-7Global MPPT optimization for partially shaded photovoltaic systemsT. Nagadurga0V. Dhana Raju1Abdulwasa Bakr Barnawi2Javed Khan Bhutto3Abdul Razak4Anteneh Wogasso Wodajo5Department of Electrical and Electronics Engineering, Malla Reddy Engineering CollegeDepartment of Mechanical Engineering, Lakireddy Bali Reddy College of EngineeringDepartment of Electrical Engineering, College of Engineering, King Khalid UniversityDepartment of Electrical Engineering, College of Engineering, King Khalid UniversityDepartment of Mechanical Engineering, P. A. College of Engineering (Affiliated to Visvesvaraya Technological University, Belagavi)Department of Automotive Engineering, College of Engineering and Technology, Dilla UniversityAbstract The global demand for electrical energy has witnessed a substantial increase, presenting a challenge for power systems worldwide. In addition to technical considerations, the escalating issue of global warming has become a paramount concern in the planning studies of various sectors. The formulation and resolution of a single-objective non-linear optimization problem are carried out, considering different operational scenarios. Recent heuristic algorithms, including Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimization (GWO) and Chimp Optimization algorithm (ChOA) are employed to address the complexities associated with maximizing power output under partial shading conditions in solar PV systems. The inherent challenges of achieving MPPT under such conditions make conventional analytic approaches computationally intensive. Hence, this study leverages heuristic algorithms to optimize solar PV system performance, providing efficient solutions to the associated optimization problems. The current research work was performed on a test system using a MATLAB/SIMULINK environment and the results are presented and discussed. From the simulation results, it was found that ChOA have shown higher conversion efficiency of 99.63% with maximum power output of 525.13 W when compared to other optimization algorithms for the given shading pattern condition. Further, ChOA offers easy implementation and faster convergence, outperforming established methods in GMPP search by reducing power oscillations and achieving precise MPP convergence.https://doi.org/10.1038/s41598-025-89694-7Solar PV systemsParticle swarm optimization (PSO)Cat Swarm Optimization (CSO)Grey Wolf optimization (GWO)Teaching learning based optimization (TLBO)Chimp optimization algorithm (ChOA). |
| spellingShingle | T. Nagadurga V. Dhana Raju Abdulwasa Bakr Barnawi Javed Khan Bhutto Abdul Razak Anteneh Wogasso Wodajo Global MPPT optimization for partially shaded photovoltaic systems Scientific Reports Solar PV systems Particle swarm optimization (PSO) Cat Swarm Optimization (CSO) Grey Wolf optimization (GWO) Teaching learning based optimization (TLBO) Chimp optimization algorithm (ChOA). |
| title | Global MPPT optimization for partially shaded photovoltaic systems |
| title_full | Global MPPT optimization for partially shaded photovoltaic systems |
| title_fullStr | Global MPPT optimization for partially shaded photovoltaic systems |
| title_full_unstemmed | Global MPPT optimization for partially shaded photovoltaic systems |
| title_short | Global MPPT optimization for partially shaded photovoltaic systems |
| title_sort | global mppt optimization for partially shaded photovoltaic systems |
| topic | Solar PV systems Particle swarm optimization (PSO) Cat Swarm Optimization (CSO) Grey Wolf optimization (GWO) Teaching learning based optimization (TLBO) Chimp optimization algorithm (ChOA). |
| url | https://doi.org/10.1038/s41598-025-89694-7 |
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