A novel levy flight trajectory-based salp swarm algorithm for photovoltaic parameters estimation
Résumé
Simulation and optimization of photovoltaic (PV) systems are recently urging as a hotspot in the field of renewable energy. For this, different models have been introduced to simulate the behavior of real PV cells and modules. An accurate estimation of unknown parameters of PV models is necessary to achieve a good performance of photovoltaic systems. This paper presents a new metaheuristic algorithm called Levy flight trajectory-based salp swarm algorithm (LSSA), which estimated single diode (SD), double diode (DD) and PV module models parameters. Compared with the original salp swarm algorithm (SSA), LSSA benefits from enhanced population diversity due to Levy flight trajectory characteristic of long jump steps. In order to validate its efficiency, the proposed technique was compared with other well-known meta-heuristics for estimating PV parameters in the SD, DD and PV modules. The experimental and comparative results demonstrated that LSSA was able to find extremely accurate solutions with high reliability and offers very competitive results for the problem of estimating PV parameters.