Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic Models
It is difficult to determine unknown solar cell and photovoltaic (PV) module parameters owing to the nonlinearity of the characteristic current–voltage (I-V) curve. Despite this, precise parameter estimation is necessary due to the substantial effect parameters have on the efficacy of the PV system...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/17/1/26 |
| _version_ | 1797340218376847360 |
|---|---|
| author | Amr A. Abd El-Mageed Ayoub Al-Hamadi Samy Bakheet Asmaa H. Abd El-Rahiem |
| author_facet | Amr A. Abd El-Mageed Ayoub Al-Hamadi Samy Bakheet Asmaa H. Abd El-Rahiem |
| author_sort | Amr A. Abd El-Mageed |
| collection | DOAJ |
| description | It is difficult to determine unknown solar cell and photovoltaic (PV) module parameters owing to the nonlinearity of the characteristic current–voltage (I-V) curve. Despite this, precise parameter estimation is necessary due to the substantial effect parameters have on the efficacy of the PV system with respect to current and energy results. The problem’s characteristics make the handling of algorithms susceptible to local optima and resource-intensive processing. To effectively extract PV model parameter values, an improved hybrid Sparrow Search Algorithm (SSA) with Exponential Distribution Optimization (EDO) based on the Differential Evolution (DE) technique and the bound-constraint modification procedure, called ISSAEDO, is presented in this article. The hybrid strategy utilizes EDO to improve global exploration and SSA to effectively explore the solution space, while DE facilitates local search to improve parameter estimations. The proposed method is compared to standard optimization methods using solar PV system data to demonstrate its effectiveness and speed in obtaining PV model parameters such as the single diode model (SDM) and the double diode model (DDM). The results indicate that the hybrid technique is a viable instrument for enhancing solar PV system design and performance analysis because it can predict PV model parameters accurately. |
| first_indexed | 2024-03-08T09:59:50Z |
| format | Article |
| id | doaj.art-6c26435947de404c84856018b52a378e |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-08T09:59:50Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-6c26435947de404c84856018b52a378e2024-01-29T13:41:27ZengMDPI AGAlgorithms1999-48932024-01-011712610.3390/a17010026Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic ModelsAmr A. Abd El-Mageed0Ayoub Al-Hamadi1Samy Bakheet2Asmaa H. Abd El-Rahiem3Department of Information Systems, Sohag University, Sohag 82524, EgyptInstitute for Information Technology and Communications (IIKT), Otto von Guericke University Magdeburg, 39106 Magdeburg, GermanyDepartment of Information Technology, Faculty of Computers and Information, Sohag University, Sohag 82524, EgyptDepartment of Mathematics, Faculty of Science, South Valley University, Qena 83511, EgyptIt is difficult to determine unknown solar cell and photovoltaic (PV) module parameters owing to the nonlinearity of the characteristic current–voltage (I-V) curve. Despite this, precise parameter estimation is necessary due to the substantial effect parameters have on the efficacy of the PV system with respect to current and energy results. The problem’s characteristics make the handling of algorithms susceptible to local optima and resource-intensive processing. To effectively extract PV model parameter values, an improved hybrid Sparrow Search Algorithm (SSA) with Exponential Distribution Optimization (EDO) based on the Differential Evolution (DE) technique and the bound-constraint modification procedure, called ISSAEDO, is presented in this article. The hybrid strategy utilizes EDO to improve global exploration and SSA to effectively explore the solution space, while DE facilitates local search to improve parameter estimations. The proposed method is compared to standard optimization methods using solar PV system data to demonstrate its effectiveness and speed in obtaining PV model parameters such as the single diode model (SDM) and the double diode model (DDM). The results indicate that the hybrid technique is a viable instrument for enhancing solar PV system design and performance analysis because it can predict PV model parameters accurately.https://www.mdpi.com/1999-4893/17/1/26photovoltaic (PV) modelssolar cellExponential Distribution Optimization (EDO)Sparrow Search Algorithm (SSA)differential evolution (DE) |
| spellingShingle | Amr A. Abd El-Mageed Ayoub Al-Hamadi Samy Bakheet Asmaa H. Abd El-Rahiem Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic Models Algorithms photovoltaic (PV) models solar cell Exponential Distribution Optimization (EDO) Sparrow Search Algorithm (SSA) differential evolution (DE) |
| title | Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic Models |
| title_full | Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic Models |
| title_fullStr | Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic Models |
| title_full_unstemmed | Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic Models |
| title_short | Hybrid Sparrow Search-Exponential Distribution Optimization with Differential Evolution for Parameter Prediction of Solar Photovoltaic Models |
| title_sort | hybrid sparrow search exponential distribution optimization with differential evolution for parameter prediction of solar photovoltaic models |
| topic | photovoltaic (PV) models solar cell Exponential Distribution Optimization (EDO) Sparrow Search Algorithm (SSA) differential evolution (DE) |
| url | https://www.mdpi.com/1999-4893/17/1/26 |
| work_keys_str_mv | AT amraabdelmageed hybridsparrowsearchexponentialdistributionoptimizationwithdifferentialevolutionforparameterpredictionofsolarphotovoltaicmodels AT ayoubalhamadi hybridsparrowsearchexponentialdistributionoptimizationwithdifferentialevolutionforparameterpredictionofsolarphotovoltaicmodels AT samybakheet hybridsparrowsearchexponentialdistributionoptimizationwithdifferentialevolutionforparameterpredictionofsolarphotovoltaicmodels AT asmaahabdelrahiem hybridsparrowsearchexponentialdistributionoptimizationwithdifferentialevolutionforparameterpredictionofsolarphotovoltaicmodels |