The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems
This paper presents a new heuristic method for maximum power point tracking (MPPT) in PV systems under normal and shadowing situations. The proposed method is a modification of the original queen honey bee migration (QHBM) to shorten the computation time for the maximum power point (MPP) in PV syste...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2022/1996410 |
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| author | Aripriharta Aripriharta Kusmayanto Hadi Wibowo Irham Fadlika Muladi Muladi Nandang Mufti Markus Diantoro Gwo-Jiun Horng |
| author_facet | Aripriharta Aripriharta Kusmayanto Hadi Wibowo Irham Fadlika Muladi Muladi Nandang Mufti Markus Diantoro Gwo-Jiun Horng |
| author_sort | Aripriharta Aripriharta |
| collection | DOAJ |
| description | This paper presents a new heuristic method for maximum power point tracking (MPPT) in PV systems under normal and shadowing situations. The proposed method is a modification of the original queen honey bee migration (QHBM) to shorten the computation time for the maximum power point (MPP) in PV systems. QHBM initially uses random target locations to search for targets, in this case, MPP. So, we adjusted it to be able to do MPP point quests quickly. We accelerated the mQHBM learning process from the original randomly. We had fairly compared the mQHBM with several heuristics. Simulations were carried out with 2 scenarios to test the mQHBM. Based on the simulation results, it was found that mQHBM was able to exceed the capabilities of other methods such as original QHBM, particle swarm optimization (PSO) and perturb and observe (P&O), ANN, gray wolf (GWO), and cuckoo search (CS) in terms of MPPT speed and overshoot. However, the accuracy of mQHBM cannot exceed QHBM, ANN, and GWO. But still, mQHBM is better than PSO and P&O by about 15% and 18%, respectively. This experiment resulted in a gap of about 2% faster in speed, 0.34 seconds better in convergence time, and 0.2 fewer accuracies. |
| format | Article |
| id | doaj-art-d00dc12ae31244f7a6df33fd3993db42 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-d00dc12ae31244f7a6df33fd3993db422025-02-03T06:13:03ZengWileyApplied Computational Intelligence and Soft Computing1687-97322022-01-01202210.1155/2022/1996410The Performance of a New Heuristic Approach for Tracking Maximum Power of PV SystemsAripriharta Aripriharta0Kusmayanto Hadi Wibowo1Irham Fadlika2Muladi Muladi3Nandang Mufti4Markus Diantoro5Gwo-Jiun Horng6Department of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical EngineeringDepartment of Electrical EngineeringCenter of Advanced Materials for Renewable EnergyCenter of Advanced Materials for Renewable EnergyDepartment of Computer Science and Information EngineeringThis paper presents a new heuristic method for maximum power point tracking (MPPT) in PV systems under normal and shadowing situations. The proposed method is a modification of the original queen honey bee migration (QHBM) to shorten the computation time for the maximum power point (MPP) in PV systems. QHBM initially uses random target locations to search for targets, in this case, MPP. So, we adjusted it to be able to do MPP point quests quickly. We accelerated the mQHBM learning process from the original randomly. We had fairly compared the mQHBM with several heuristics. Simulations were carried out with 2 scenarios to test the mQHBM. Based on the simulation results, it was found that mQHBM was able to exceed the capabilities of other methods such as original QHBM, particle swarm optimization (PSO) and perturb and observe (P&O), ANN, gray wolf (GWO), and cuckoo search (CS) in terms of MPPT speed and overshoot. However, the accuracy of mQHBM cannot exceed QHBM, ANN, and GWO. But still, mQHBM is better than PSO and P&O by about 15% and 18%, respectively. This experiment resulted in a gap of about 2% faster in speed, 0.34 seconds better in convergence time, and 0.2 fewer accuracies.http://dx.doi.org/10.1155/2022/1996410 |
| spellingShingle | Aripriharta Aripriharta Kusmayanto Hadi Wibowo Irham Fadlika Muladi Muladi Nandang Mufti Markus Diantoro Gwo-Jiun Horng The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems Applied Computational Intelligence and Soft Computing |
| title | The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems |
| title_full | The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems |
| title_fullStr | The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems |
| title_full_unstemmed | The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems |
| title_short | The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems |
| title_sort | performance of a new heuristic approach for tracking maximum power of pv systems |
| url | http://dx.doi.org/10.1155/2022/1996410 |
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