Accurate identification of unknown parameters in PV models is of great significance to improve the power generation efficiency of PV systems and the fault diagnosis of PV modules. Due to the nonlinear and multi-peak characteristics of complex PV models, some meta-heuristic algorithms tend to converge prematurely and fall into local optimization, resulting in low precision and poor stability of optimization results. To solve this problem, an enhanced osprey optimization algorithm (EOOA) was proposed: in the initialization stage, Sine-Tent Cosine chaotic mapping was used to obtain the initial position of the population with more uniform distribution, so as to avoid premature convergence. The Gauss wandering strategy was introduced into the attack behavior of osprey, and a disturbance of both direction and appropriate amplitude was applied to the individual osprey, which was helpful to improve the exploration efficiency. The differential variation strategy was introduced to increase the diversity of the population and help the algorithm to escape the local optimal solution. Combined with the predator avoidance strategy of APO algorithm, the probability of finding the optimal solution can be increased. Finally, through simulation experiments, it is proved that EOOA can accurately extract the unknown parameters of complex PV models TDM and FDM, and has a certain stability.