Super‐evolutionary mechanism and Nelder‐Mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimation
Abstract In the pursuit of enhancing the efficiency of solar cells, accurate estimation of unspecified parameters in the solar photovoltaic (PV) cell model is imperative. An advanced salp swarm algorithm called the Super‐Evolutionary Nelder‐Mead Salp Swarm Algorithm (SENMSSA) is proposed to achieve...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-10-01
|
| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12973 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841546481953144832 |
|---|---|
| author | Huangying Wu Yi Chen Zhennao Cai Ali Asghar Heidari Huiling Chen Yudong Zhang |
| author_facet | Huangying Wu Yi Chen Zhennao Cai Ali Asghar Heidari Huiling Chen Yudong Zhang |
| author_sort | Huangying Wu |
| collection | DOAJ |
| description | Abstract In the pursuit of enhancing the efficiency of solar cells, accurate estimation of unspecified parameters in the solar photovoltaic (PV) cell model is imperative. An advanced salp swarm algorithm called the Super‐Evolutionary Nelder‐Mead Salp Swarm Algorithm (SENMSSA) is proposed to achieve this objective. The proposed SENMSSA addresses the limitations of SSA by incorporating a super‐evolutionary mechanism based on a Gaussian‐Cauchy mutation and a vertical and horizontal crossover mechanism. This mechanism enhances both the global optimization capabilities and the local search performance and convergence speed of the algorithm. It enables a secondary refinement of the global optimum, unlocking untapped potential in the solution space near the global optimum and elevating the algorithm's precision and exploitation capabilities to higher levels. The Nelder‐Mead simplex method is further introduced to enhance local search capabilities and convergence accuracy. The Nelder‐Mead simplex method is a versatile optimization algorithm that improves local search by iteratively adjusting a geometric shape (simplex) of points. It operates without needing derivatives, making it suitable for non‐smooth or complex objective functions. To assess the efficacy of SENMSSA, a comparative analysis is conducted against other available algorithms, namely SSA, IWOA, SCADE, LWOA, CBA, and RCBA, using the CEC2014 benchmark function set. Subsequently, the algorithm was employed to determine the unknown PV parameters under fixed conditions for three different diode models. Additionally, SENMSSA is utilized to estimate PV parameters for three commercially available PV models (ST40, SM55, KC200GT) under varying conditions. The experimental results indicate that the SENMSSA proposed in this study displays a remarkably competitive performance in all test cases compared to other algorithms. As such, we consider that the SENMSSA algorithm constitutes a reliable and efficient solution for the challenge of PV parameter estimation. |
| format | Article |
| id | doaj-art-081704d8c50148fd8184fb66af45ddfc |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-081704d8c50148fd8184fb66af45ddfc2025-01-10T17:41:03ZengWileyIET Renewable Power Generation1752-14161752-14242024-10-0118142209223710.1049/rpg2.12973Super‐evolutionary mechanism and Nelder‐Mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimationHuangying Wu0Yi Chen1Zhennao Cai2Ali Asghar Heidari3Huiling Chen4Yudong Zhang5Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University Wenzhou ChinaKey Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University Wenzhou ChinaKey Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University Wenzhou ChinaSchool of Surveying and Geospatial Engineering College of Engineering University of Tehran Tehran IranKey Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University Wenzhou ChinaSchool of Computing and Mathematical Sciences University of Leicester Leicester UKAbstract In the pursuit of enhancing the efficiency of solar cells, accurate estimation of unspecified parameters in the solar photovoltaic (PV) cell model is imperative. An advanced salp swarm algorithm called the Super‐Evolutionary Nelder‐Mead Salp Swarm Algorithm (SENMSSA) is proposed to achieve this objective. The proposed SENMSSA addresses the limitations of SSA by incorporating a super‐evolutionary mechanism based on a Gaussian‐Cauchy mutation and a vertical and horizontal crossover mechanism. This mechanism enhances both the global optimization capabilities and the local search performance and convergence speed of the algorithm. It enables a secondary refinement of the global optimum, unlocking untapped potential in the solution space near the global optimum and elevating the algorithm's precision and exploitation capabilities to higher levels. The Nelder‐Mead simplex method is further introduced to enhance local search capabilities and convergence accuracy. The Nelder‐Mead simplex method is a versatile optimization algorithm that improves local search by iteratively adjusting a geometric shape (simplex) of points. It operates without needing derivatives, making it suitable for non‐smooth or complex objective functions. To assess the efficacy of SENMSSA, a comparative analysis is conducted against other available algorithms, namely SSA, IWOA, SCADE, LWOA, CBA, and RCBA, using the CEC2014 benchmark function set. Subsequently, the algorithm was employed to determine the unknown PV parameters under fixed conditions for three different diode models. Additionally, SENMSSA is utilized to estimate PV parameters for three commercially available PV models (ST40, SM55, KC200GT) under varying conditions. The experimental results indicate that the SENMSSA proposed in this study displays a remarkably competitive performance in all test cases compared to other algorithms. As such, we consider that the SENMSSA algorithm constitutes a reliable and efficient solution for the challenge of PV parameter estimation.https://doi.org/10.1049/rpg2.12973artificial intelligencelearning (artificial intelligence) |
| spellingShingle | Huangying Wu Yi Chen Zhennao Cai Ali Asghar Heidari Huiling Chen Yudong Zhang Super‐evolutionary mechanism and Nelder‐Mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimation IET Renewable Power Generation artificial intelligence learning (artificial intelligence) |
| title | Super‐evolutionary mechanism and Nelder‐Mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimation |
| title_full | Super‐evolutionary mechanism and Nelder‐Mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimation |
| title_fullStr | Super‐evolutionary mechanism and Nelder‐Mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimation |
| title_full_unstemmed | Super‐evolutionary mechanism and Nelder‐Mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimation |
| title_short | Super‐evolutionary mechanism and Nelder‐Mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimation |
| title_sort | super evolutionary mechanism and nelder mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimation |
| topic | artificial intelligence learning (artificial intelligence) |
| url | https://doi.org/10.1049/rpg2.12973 |
| work_keys_str_mv | AT huangyingwu superevolutionarymechanismandneldermeadsimplexenhancedsalpswarmalgorithmforphotovoltaicmodelparameterestimation AT yichen superevolutionarymechanismandneldermeadsimplexenhancedsalpswarmalgorithmforphotovoltaicmodelparameterestimation AT zhennaocai superevolutionarymechanismandneldermeadsimplexenhancedsalpswarmalgorithmforphotovoltaicmodelparameterestimation AT aliasgharheidari superevolutionarymechanismandneldermeadsimplexenhancedsalpswarmalgorithmforphotovoltaicmodelparameterestimation AT huilingchen superevolutionarymechanismandneldermeadsimplexenhancedsalpswarmalgorithmforphotovoltaicmodelparameterestimation AT yudongzhang superevolutionarymechanismandneldermeadsimplexenhancedsalpswarmalgorithmforphotovoltaicmodelparameterestimation |