Abstract
The integration of photovoltaic systems (PVSs) in future power systems grows into a more attractive choice. Thus, the studies related to PVSs operation have gained immense interest. Particularly, research in identifying PV cell model parameters remains an agile field because of the non-linearity of PV cell characteristics and its wide dependency on meteorological conditions of irradiation level and temperature. This paper proposes an Opposition-based Learning Modified Salp Swarm Algorithm (OLMSSA) for accurate identification of the two-diode model parameters of the electrical equivalent circuit of the PV cell/module. Six metaheuristic algorithms, including the recently released basic algorithm SSA, used with the benchmark test PV model of the double diode, and a practical PV module, are employed to assess the performance of OLMSSA. The experimental results and the in-depth comparative study clearly demonstrate that OLMSSA is highly competitive and even significantly better than the reported results of the majority of recently-developed parameter identification methods.
Original language | English |
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Article number | 117333 |
Journal | Energy |
Volume | 198 |
DOIs | |
Publication status | Published - 1 May 2020 |
Externally published | Yes |
Keywords
- I–V characteristics
- Metaheuristic optimizer
- Parameters extraction
- Photovoltaic panels
- Salp swarm algorithm
- Two-diode model
ASJC Scopus subject areas
- Civil and Structural Engineering
- Building and Construction
- Pollution
- Mechanical Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering