Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm
To simulate the behaviors of photovoltaic (PV) systems properly, the best values of the uncertain parameters of the PV models must be identified. Therefore, this paper proposes a novel optimization framework for estimating the parameters of the triple-diode model (TDM) of PV units with different tec...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/9/995 |
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| author | Mohamed Abdel-Basset Reda Mohamed Attia El-Fergany Mohamed Abouhawwash S. S. Askar |
| author_facet | Mohamed Abdel-Basset Reda Mohamed Attia El-Fergany Mohamed Abouhawwash S. S. Askar |
| author_sort | Mohamed Abdel-Basset |
| collection | DOAJ |
| description | To simulate the behaviors of photovoltaic (PV) systems properly, the best values of the uncertain parameters of the PV models must be identified. Therefore, this paper proposes a novel optimization framework for estimating the parameters of the triple-diode model (TDM) of PV units with different technologies. The proposed methodology is based on the generalized normal distribution optimization (GNDO) with two novel strategies: (i) a premature convergence method (PCM), and (ii) a ranking-based updating method (RUM) to accelerate the convergence by utilizing each individual in the population as much as possible. This improved version of GNDO is called ranking-based generalized normal distribution optimization (RGNDO). RGNDO is experimentally investigated on three commercial PV modules (Kyocera KC200GT, Ultra 85-P and STP 6-120/36) and a solar unit (RTC Si solar cell France), and its extracted parameters are validated based on the measured dataset points extracted at generalized operating conditions. It can be reported here that the best scores of the objective function are equal to 0.750839 mA, 28.212810 mA, 2.417084 mA, and 13.798273 mA for RTC cell, KC200GT, Ultra 85-P, and STP 6-120/36; respectively. Additionally, the principal performance of this methodology is evaluated under various statistical tests and for convergence speed, and is compared with a number of the well-known recent state-of-the-art algorithms. RGNDO is shown to outperform the other algorithms in terms of all the statistical metrics as well as convergence speed. Finally, the performance of the RGNDO is validated in various operating conditions under varied temperatures and sun irradiance levels. |
| first_indexed | 2024-03-10T11:53:35Z |
| format | Article |
| id | doaj.art-f947bdd8f7344b2997de35151ab036e7 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T11:53:35Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-f947bdd8f7344b2997de35151ab036e72023-11-21T17:32:34ZengMDPI AGMathematics2227-73902021-04-019999510.3390/math9090995Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution AlgorithmMohamed Abdel-Basset0Reda Mohamed1Attia El-Fergany2Mohamed Abouhawwash3S. S. Askar4Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, EgyptDepartment of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, EgyptElectrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig 44519, EgyptDepartment of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, EgyptDepartment of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, EgyptTo simulate the behaviors of photovoltaic (PV) systems properly, the best values of the uncertain parameters of the PV models must be identified. Therefore, this paper proposes a novel optimization framework for estimating the parameters of the triple-diode model (TDM) of PV units with different technologies. The proposed methodology is based on the generalized normal distribution optimization (GNDO) with two novel strategies: (i) a premature convergence method (PCM), and (ii) a ranking-based updating method (RUM) to accelerate the convergence by utilizing each individual in the population as much as possible. This improved version of GNDO is called ranking-based generalized normal distribution optimization (RGNDO). RGNDO is experimentally investigated on three commercial PV modules (Kyocera KC200GT, Ultra 85-P and STP 6-120/36) and a solar unit (RTC Si solar cell France), and its extracted parameters are validated based on the measured dataset points extracted at generalized operating conditions. It can be reported here that the best scores of the objective function are equal to 0.750839 mA, 28.212810 mA, 2.417084 mA, and 13.798273 mA for RTC cell, KC200GT, Ultra 85-P, and STP 6-120/36; respectively. Additionally, the principal performance of this methodology is evaluated under various statistical tests and for convergence speed, and is compared with a number of the well-known recent state-of-the-art algorithms. RGNDO is shown to outperform the other algorithms in terms of all the statistical metrics as well as convergence speed. Finally, the performance of the RGNDO is validated in various operating conditions under varied temperatures and sun irradiance levels.https://www.mdpi.com/2227-7390/9/9/995renewable energyPV triple-diode modelparameter extractionoptimization methodspremature convergenceranking method |
| spellingShingle | Mohamed Abdel-Basset Reda Mohamed Attia El-Fergany Mohamed Abouhawwash S. S. Askar Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm Mathematics renewable energy PV triple-diode model parameter extraction optimization methods premature convergence ranking method |
| title | Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm |
| title_full | Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm |
| title_fullStr | Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm |
| title_full_unstemmed | Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm |
| title_short | Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm |
| title_sort | parameters identification of pv triple diode model using improved generalized normal distribution algorithm |
| topic | renewable energy PV triple-diode model parameter extraction optimization methods premature convergence ranking method |
| url | https://www.mdpi.com/2227-7390/9/9/995 |
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