A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions
In order to achieve maximum efficiency a photovoltaic (PV) arrays should operate at their maximum power point (MPP). Therefore, an MPP tracking (MPPT) scheme is implemented between the PV system and the load to obtain maximum power. When the irradiance distribution on the PV arrays is uniform, many...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/106330 http://hdl.handle.net/10220/17678 |
| _version_ | 1824454565498454016 |
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| author | Jiang, Lian Lian. Maskell, Douglas L. Patra, Jagdish C. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Jiang, Lian Lian. Maskell, Douglas L. Patra, Jagdish C. |
| author_sort | Jiang, Lian Lian. |
| collection | NTU |
| description | In order to achieve maximum efficiency a photovoltaic (PV) arrays should operate at their maximum power point (MPP). Therefore, an MPP tracking (MPPT) scheme is implemented between the PV system and the load to obtain maximum power. When the irradiance distribution on the PV arrays is uniform, many traditional MPPT techniques can track the MPP effectively. However, when the PV arrays are partially shaded, multiple MPPs show up, which usually results in the failure of finding the global MPP. Some researchers have reported this problem and tried to solve it, but most of the MPP control schemes are relatively complicated or fail to guarantee the MPP under all shading circumstances. In order to overcome this difficulty, this paper presents a novel ant colony optimization (ACO)-based MPPT scheme for PV systems. A new control scheme is also introduced based on the proposed MPPT method. This heuristic algorithm based technique not only ensures the ability to find the global MPP, but also gives a simpler control scheme and lower system cost. The feasibility of this proposed method is verified with the irradiance of various shading patterns by simulation. In addition, the performance comparison with other traditional MPPT techniques, such as: constant voltage tracking (CVT), perturb and observe (P&O), particle swarm optimization (PSO), is also presented. The results show that the proposed algorithm can track the global MPP effectively, and is robust to various shading patterns. |
| first_indexed | 2025-02-19T03:24:20Z |
| format | Journal Article |
| id | ntu-10356/106330 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:24:20Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1063302020-05-28T07:18:19Z A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions Jiang, Lian Lian. Maskell, Douglas L. Patra, Jagdish C. School of Computer Engineering DRNTU::Science::Mathematics::Applied mathematics::Optimization In order to achieve maximum efficiency a photovoltaic (PV) arrays should operate at their maximum power point (MPP). Therefore, an MPP tracking (MPPT) scheme is implemented between the PV system and the load to obtain maximum power. When the irradiance distribution on the PV arrays is uniform, many traditional MPPT techniques can track the MPP effectively. However, when the PV arrays are partially shaded, multiple MPPs show up, which usually results in the failure of finding the global MPP. Some researchers have reported this problem and tried to solve it, but most of the MPP control schemes are relatively complicated or fail to guarantee the MPP under all shading circumstances. In order to overcome this difficulty, this paper presents a novel ant colony optimization (ACO)-based MPPT scheme for PV systems. A new control scheme is also introduced based on the proposed MPPT method. This heuristic algorithm based technique not only ensures the ability to find the global MPP, but also gives a simpler control scheme and lower system cost. The feasibility of this proposed method is verified with the irradiance of various shading patterns by simulation. In addition, the performance comparison with other traditional MPPT techniques, such as: constant voltage tracking (CVT), perturb and observe (P&O), particle swarm optimization (PSO), is also presented. The results show that the proposed algorithm can track the global MPP effectively, and is robust to various shading patterns. 2013-11-15T05:58:59Z 2019-12-06T22:09:17Z 2013-11-15T05:58:59Z 2019-12-06T22:09:17Z 2012 2012 Journal Article Jiang, L. L., Maskell, D. L., & Patra, J. C. (2013). A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions. Energy and buildings, 58, 227-236. 0378-7788 https://hdl.handle.net/10356/106330 http://hdl.handle.net/10220/17678 10.1016/j.enbuild.2012.12.001 en Energy and buildings |
| spellingShingle | DRNTU::Science::Mathematics::Applied mathematics::Optimization Jiang, Lian Lian. Maskell, Douglas L. Patra, Jagdish C. A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions |
| title | A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions |
| title_full | A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions |
| title_fullStr | A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions |
| title_full_unstemmed | A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions |
| title_short | A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions |
| title_sort | novel ant colony optimization based maximum power point tracking for photovoltaic systems under partially shaded conditions |
| topic | DRNTU::Science::Mathematics::Applied mathematics::Optimization |
| url | https://hdl.handle.net/10356/106330 http://hdl.handle.net/10220/17678 |
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