Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm
Identifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges of the simulation and design of photovoltaic systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control p...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/23/4617 |
| _version_ | 1797462700650921984 |
|---|---|
| author | Mohana Alanazi Abdulaziz Alanazi Ahmad Almadhor Hafiz Tayyab Rauf |
| author_facet | Mohana Alanazi Abdulaziz Alanazi Ahmad Almadhor Hafiz Tayyab Rauf |
| author_sort | Mohana Alanazi |
| collection | DOAJ |
| description | Identifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges of the simulation and design of photovoltaic systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control parameters must be adjusted with many existing algorithms, making them difficult to use. In real-world problems, many of these algorithms must be combined or hybridized, which results in more complex and time-consuming algorithms. This paper presents a new artificial parameter-less optimization algorithm (APLO) for parameter estimation of PV models. New mutation operators are designed in the proposed algorithm. APLO’s exploitation phase is enhanced by each individual searching for the best solution in this updating operator. Moreover, the current best, the old best, and the individual’s current position are utilized in the differential term of the mutation operator to assist the exploration phase and control the convergence speed. The algorithm uses a random step length based on a normal distribution to ensure population diversity. We present the results of a comparative study using APLO and well-known existing parameter-less meta-heuristic algorithms such as grey wolf optimization, the salp swarm algorithm, JAYA, teaching-learning based optimization, colliding body optimization, as well as three major parameter-based algorithms such as differential evolution, genetic algorithm, and particle swarm optimization to estimate the parameters of PV the modules. The results revealed that the proposed algorithm could provide excellent exploration–exploitation balance and consistency during the iterations. Furthermore, the APLO algorithm shows high reliability and accuracy in identifying the parameters of PV cell models. |
| first_indexed | 2024-03-09T17:40:22Z |
| format | Article |
| id | doaj.art-14b480ea3f944729b4630d2bc0cc2005 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T17:40:22Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-14b480ea3f944729b4630d2bc0cc20052023-11-24T11:36:36ZengMDPI AGMathematics2227-73902022-12-011023461710.3390/math10234617Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization AlgorithmMohana Alanazi0Abdulaziz Alanazi1Ahmad Almadhor2Hafiz Tayyab Rauf3Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Northern Border University, Ar’Ar 73222, Saudi ArabiaDepartment of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi ArabiaCentre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UKIdentifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges of the simulation and design of photovoltaic systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control parameters must be adjusted with many existing algorithms, making them difficult to use. In real-world problems, many of these algorithms must be combined or hybridized, which results in more complex and time-consuming algorithms. This paper presents a new artificial parameter-less optimization algorithm (APLO) for parameter estimation of PV models. New mutation operators are designed in the proposed algorithm. APLO’s exploitation phase is enhanced by each individual searching for the best solution in this updating operator. Moreover, the current best, the old best, and the individual’s current position are utilized in the differential term of the mutation operator to assist the exploration phase and control the convergence speed. The algorithm uses a random step length based on a normal distribution to ensure population diversity. We present the results of a comparative study using APLO and well-known existing parameter-less meta-heuristic algorithms such as grey wolf optimization, the salp swarm algorithm, JAYA, teaching-learning based optimization, colliding body optimization, as well as three major parameter-based algorithms such as differential evolution, genetic algorithm, and particle swarm optimization to estimate the parameters of PV the modules. The results revealed that the proposed algorithm could provide excellent exploration–exploitation balance and consistency during the iterations. Furthermore, the APLO algorithm shows high reliability and accuracy in identifying the parameters of PV cell models.https://www.mdpi.com/2227-7390/10/23/4617solar cellsphotovoltaic modelingmetaheuristic algorithmglobal optimizationpower system managementrenewable energy |
| spellingShingle | Mohana Alanazi Abdulaziz Alanazi Ahmad Almadhor Hafiz Tayyab Rauf Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm Mathematics solar cells photovoltaic modeling metaheuristic algorithm global optimization power system management renewable energy |
| title | Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm |
| title_full | Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm |
| title_fullStr | Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm |
| title_full_unstemmed | Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm |
| title_short | Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm |
| title_sort | photovoltaic models parameter extraction using new artificial parameterless optimization algorithm |
| topic | solar cells photovoltaic modeling metaheuristic algorithm global optimization power system management renewable energy |
| url | https://www.mdpi.com/2227-7390/10/23/4617 |
| work_keys_str_mv | AT mohanaalanazi photovoltaicmodelsparameterextractionusingnewartificialparameterlessoptimizationalgorithm AT abdulazizalanazi photovoltaicmodelsparameterextractionusingnewartificialparameterlessoptimizationalgorithm AT ahmadalmadhor photovoltaicmodelsparameterextractionusingnewartificialparameterlessoptimizationalgorithm AT hafiztayyabrauf photovoltaicmodelsparameterextractionusingnewartificialparameterlessoptimizationalgorithm |