Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions
The application of small-scale electrical systems is widespread and the integration of Maximum Power Point Tracking (MPPT) control for Photovoltaic systems with battery applications further enhances the techno-economic feasibility of renewable systems. For this purpose, a novel MPPT control system u...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722019588 |
| _version_ | 1797901894810599424 |
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| author | Syed Kumayl Raza Moosavi Majad Mansoor Muhammad Hamza Zafar Noman Mujeeb Khan Adeel Feroz Mirza Naureen Akhtar |
| author_facet | Syed Kumayl Raza Moosavi Majad Mansoor Muhammad Hamza Zafar Noman Mujeeb Khan Adeel Feroz Mirza Naureen Akhtar |
| author_sort | Syed Kumayl Raza Moosavi |
| collection | DOAJ |
| description | The application of small-scale electrical systems is widespread and the integration of Maximum Power Point Tracking (MPPT) control for Photovoltaic systems with battery applications further enhances the techno-economic feasibility of renewable systems. For this purpose, a novel MPPT control system using Dynamic Group based cooperation optimization (DGBCO) algorithm is utilized for PV systems. The population in the DGBCO is divided into exploration and exploitation groups. Due to effective mathematical modeling, the drawbacks of existing MPPT control techniques are undertaken. The drawbacks of modern MPPT control become prominent under partial shading conditions (PSC) which give rise to power loss, random fluctuations, and slow control action. The DGBCO is implemented using a search and skip mechanism which significantly enhances the performance of the MPPT controller and improves the efficiency of PV systems. The results are compared with recently developed Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CS), DragonFly Optimizer (DFO), and Particle Swarm Optimization (PSO) techniques. The operating conditions case studies include fast varying irradiance and PS with skewed GM. The DGBCO based MPPT control technique is also validated by the experimental setup. The results are compared using statistical and analytical indices such as tracking time, settling time, power tracking efficiency, total energy, RMSE, MAE, and RE. The results show the superior performance of the proposed DGBCO. Relatively, 2%–8% higher energy harvest, and up to 60% faster tracking time helps to achieve up to 99.86% power tracking efficiency in both transient and steady-state control operation. Lower values of statistical metrices i.e. RMSE, MAE, and SR indicate the robustness and effective mathematic modeling of DGBCO for effective MPPT of PV systems under PS conditions. |
| first_indexed | 2024-04-10T09:09:11Z |
| format | Article |
| id | doaj.art-c3b9b989e26e4bcf9d0a7319bf97be77 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:09:11Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-c3b9b989e26e4bcf9d0a7319bf97be772023-02-21T05:14:03ZengElsevierEnergy Reports2352-48472022-11-0181352913543Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditionsSyed Kumayl Raza Moosavi0Majad Mansoor1Muhammad Hamza Zafar2Noman Mujeeb Khan3Adeel Feroz Mirza4Naureen Akhtar5Department of Mechatronics, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Automation, University of Science and Technology of China, Hefei 230027, ChinaDepartment of Electrical, Capital University of Science and Technology, Islamabad 44000, PakistanDepartment of Electrical, Capital University of Science and Technology, Islamabad 44000, PakistanDepartment of Power Electronics and Electrical Machines, Gdansk University of Technology, PolandDepartment of Engineering Sciences, University of Agder, Grimstad, Norway; Corresponding author.The application of small-scale electrical systems is widespread and the integration of Maximum Power Point Tracking (MPPT) control for Photovoltaic systems with battery applications further enhances the techno-economic feasibility of renewable systems. For this purpose, a novel MPPT control system using Dynamic Group based cooperation optimization (DGBCO) algorithm is utilized for PV systems. The population in the DGBCO is divided into exploration and exploitation groups. Due to effective mathematical modeling, the drawbacks of existing MPPT control techniques are undertaken. The drawbacks of modern MPPT control become prominent under partial shading conditions (PSC) which give rise to power loss, random fluctuations, and slow control action. The DGBCO is implemented using a search and skip mechanism which significantly enhances the performance of the MPPT controller and improves the efficiency of PV systems. The results are compared with recently developed Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CS), DragonFly Optimizer (DFO), and Particle Swarm Optimization (PSO) techniques. The operating conditions case studies include fast varying irradiance and PS with skewed GM. The DGBCO based MPPT control technique is also validated by the experimental setup. The results are compared using statistical and analytical indices such as tracking time, settling time, power tracking efficiency, total energy, RMSE, MAE, and RE. The results show the superior performance of the proposed DGBCO. Relatively, 2%–8% higher energy harvest, and up to 60% faster tracking time helps to achieve up to 99.86% power tracking efficiency in both transient and steady-state control operation. Lower values of statistical metrices i.e. RMSE, MAE, and SR indicate the robustness and effective mathematic modeling of DGBCO for effective MPPT of PV systems under PS conditions.http://www.sciencedirect.com/science/article/pii/S2352484722019588Partial shading (PS)Particle swarm optimization (PSO)Maximum power point tracking (MPPT)Swarm intelligence (SI)Dynamic group-based cooperative optimization (DGBCO)Relative error (RE) |
| spellingShingle | Syed Kumayl Raza Moosavi Majad Mansoor Muhammad Hamza Zafar Noman Mujeeb Khan Adeel Feroz Mirza Naureen Akhtar Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions Energy Reports Partial shading (PS) Particle swarm optimization (PSO) Maximum power point tracking (MPPT) Swarm intelligence (SI) Dynamic group-based cooperative optimization (DGBCO) Relative error (RE) |
| title | Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions |
| title_full | Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions |
| title_fullStr | Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions |
| title_full_unstemmed | Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions |
| title_short | Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions |
| title_sort | highly efficient maximum power point tracking control technique for pv system under dynamic operating conditions |
| topic | Partial shading (PS) Particle swarm optimization (PSO) Maximum power point tracking (MPPT) Swarm intelligence (SI) Dynamic group-based cooperative optimization (DGBCO) Relative error (RE) |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722019588 |
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