A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading
Solar photovoltaic (PV) systems often encounter a problem called partial shading condition (PSC), which causes a significant decrease in the system’s output power. To address this issue, meta-heuristic algorithms (MHAs) can be used to perform maximum power point tracking (MPPT) on the system’s multi...
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| Format: | Article |
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MDPI AG
2023-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/15/5712 |
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| author | Zhi-Kai Fan Kuo-Lung Lian Jia-Fu Lin |
| author_facet | Zhi-Kai Fan Kuo-Lung Lian Jia-Fu Lin |
| author_sort | Zhi-Kai Fan |
| collection | DOAJ |
| description | Solar photovoltaic (PV) systems often encounter a problem called partial shading condition (PSC), which causes a significant decrease in the system’s output power. To address this issue, meta-heuristic algorithms (MHAs) can be used to perform maximum power point tracking (MPPT) on the system’s multiple-peak P-V curves due to PSCs. Particle swarm optimization was one of the first MHA methods to be implemented for MPPT. However, PSO has some drawbacks, including long settling time and sustained PV output power oscillations during tracking. Hence, some improved MHA methods have been proposed. One approach is to combine a MHA with a deterministic approach (DA) such as P & O method. However, such a hybrid method is more complex to implement. Also, the transition criteria from a DA to a MHA and vice versa is sometimes difficult to define. Another approach, as adapted in this paper is to modify the existing MHAs. This includes modifying the search operators or the parameter settings, to enhance exploration or exploitation capabilities of MHAs. This paper proposed to incorporate the stooping behaviour in the golden eagle optimization (GEO) algorithm. Stooping is in fact a hunting technique frequently employed by golden eagles. Inclusion of stooping in the GEO algorithm not only truly model golden eagles’ hunting behaviour but also yields great performance. Stooping behavior only requires one extra parameter. Nevertheless, on average, the proposed method can reduce tracking time by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>42.41</mn><mo>%</mo></mrow></semantics></math></inline-formula> and improve dynamic tracking accuracy by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.95</mn><mo>%</mo></mrow></semantics></math></inline-formula>, compared to GEO. Moreover, compared to PSO, GWO, and BA, the proposed method achieves an improvement of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.66</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.56</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.24</mn><mo>%</mo></mrow></semantics></math></inline-formula> in dynamic tracking accuracy, respectively. |
| first_indexed | 2024-03-11T00:28:44Z |
| format | Article |
| id | doaj.art-bfbf7462551c49d1a2b51cdc1a06cbbd |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T00:28:44Z |
| publishDate | 2023-07-01 |
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| series | Energies |
| spelling | doaj.art-bfbf7462551c49d1a2b51cdc1a06cbbd2023-11-18T22:51:51ZengMDPI AGEnergies1996-10732023-07-011615571210.3390/en16155712A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial ShadingZhi-Kai Fan0Kuo-Lung Lian1Jia-Fu Lin2Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Taipei City 106, TaiwanDepartment of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Taipei City 106, TaiwanTaiwan Power Company, No. 242, Section 3, Roosevelt Rd., Zhongzheng District, Taipei City 100208, TaiwanSolar photovoltaic (PV) systems often encounter a problem called partial shading condition (PSC), which causes a significant decrease in the system’s output power. To address this issue, meta-heuristic algorithms (MHAs) can be used to perform maximum power point tracking (MPPT) on the system’s multiple-peak P-V curves due to PSCs. Particle swarm optimization was one of the first MHA methods to be implemented for MPPT. However, PSO has some drawbacks, including long settling time and sustained PV output power oscillations during tracking. Hence, some improved MHA methods have been proposed. One approach is to combine a MHA with a deterministic approach (DA) such as P & O method. However, such a hybrid method is more complex to implement. Also, the transition criteria from a DA to a MHA and vice versa is sometimes difficult to define. Another approach, as adapted in this paper is to modify the existing MHAs. This includes modifying the search operators or the parameter settings, to enhance exploration or exploitation capabilities of MHAs. This paper proposed to incorporate the stooping behaviour in the golden eagle optimization (GEO) algorithm. Stooping is in fact a hunting technique frequently employed by golden eagles. Inclusion of stooping in the GEO algorithm not only truly model golden eagles’ hunting behaviour but also yields great performance. Stooping behavior only requires one extra parameter. Nevertheless, on average, the proposed method can reduce tracking time by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>42.41</mn><mo>%</mo></mrow></semantics></math></inline-formula> and improve dynamic tracking accuracy by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.95</mn><mo>%</mo></mrow></semantics></math></inline-formula>, compared to GEO. Moreover, compared to PSO, GWO, and BA, the proposed method achieves an improvement of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.66</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.56</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.24</mn><mo>%</mo></mrow></semantics></math></inline-formula> in dynamic tracking accuracy, respectively.https://www.mdpi.com/1996-1073/16/15/5712partial shading conditionmaximum power point trackingphotovoltaic systemgolden eagle optimizer |
| spellingShingle | Zhi-Kai Fan Kuo-Lung Lian Jia-Fu Lin A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading Energies partial shading condition maximum power point tracking photovoltaic system golden eagle optimizer |
| title | A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading |
| title_full | A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading |
| title_fullStr | A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading |
| title_full_unstemmed | A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading |
| title_short | A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading |
| title_sort | new golden eagle optimization with stooping behaviour for photovoltaic maximum power tracking under partial shading |
| topic | partial shading condition maximum power point tracking photovoltaic system golden eagle optimizer |
| url | https://www.mdpi.com/1996-1073/16/15/5712 |
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