Boosted backtracking search optimization with information exchange for photovoltaic system evaluation

The determination of photovoltaic (PV) parameters is great importance for the reliability solar system operation, continuity load power consumption, and control management energy source. Therefore, this study proposes an advanced backtracking search optimization algorithm (BSA) equipped with teaching learning-based (TLBO), named TLBOBSA, to accurately simulate PV model. During evaluation proposed algorithm, concept from TLBO introduced into BSA guide optimal individuals, thus improving convergence rate algorithm. learning behavior among individuals in student phase facilitates interindividual provides beneficial information its evolution, which ensure diversity population. comprehensive test results different module models environmental conditions show that more advantageous parameter extraction than other existing algorithms. This can be seen simulation experiments two commercial models, where simulated current consistent measured at each voltage. demonstrates TLBOBSA accurate reliable tool evaluating unknown models.