Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm
Abstract Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondire...
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| Format: | Article |
| Language: | English |
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Wiley
2024-04-01
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| Series: | Energy Science & Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ese3.1673 |
| _version_ | 1827279029820981248 |
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| author | Ting‐ting Zhou Chao Shang |
| author_facet | Ting‐ting Zhou Chao Shang |
| author_sort | Ting‐ting Zhou |
| collection | DOAJ |
| description | Abstract Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells. |
| first_indexed | 2024-04-24T08:11:09Z |
| format | Article |
| id | doaj.art-f4c464bf0c7d47e4a15660f06ab75c78 |
| institution | Directory Open Access Journal |
| issn | 2050-0505 |
| language | English |
| last_indexed | 2024-04-24T08:11:09Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj.art-f4c464bf0c7d47e4a15660f06ab75c782024-04-17T05:33:22ZengWileyEnergy Science & Engineering2050-05052024-04-011241422144510.1002/ese3.1673Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithmTing‐ting Zhou0Chao Shang1Architectural Engineering Institute Maanshan University Maanshan ChinaPujiang Institute Nanjing Tech University Nanjing ChinaAbstract Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells.https://doi.org/10.1002/ese3.1673mutation mechanismparameter identificationphotovoltaic cellssine–cosine algorithm |
| spellingShingle | Ting‐ting Zhou Chao Shang Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm Energy Science & Engineering mutation mechanism parameter identification photovoltaic cells sine–cosine algorithm |
| title | Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm |
| title_full | Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm |
| title_fullStr | Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm |
| title_full_unstemmed | Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm |
| title_short | Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm |
| title_sort | parameter identification of solar photovoltaic models by multi strategy sine cosine algorithm |
| topic | mutation mechanism parameter identification photovoltaic cells sine–cosine algorithm |
| url | https://doi.org/10.1002/ese3.1673 |
| work_keys_str_mv | AT tingtingzhou parameteridentificationofsolarphotovoltaicmodelsbymultistrategysinecosinealgorithm AT chaoshang parameteridentificationofsolarphotovoltaicmodelsbymultistrategysinecosinealgorithm |