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The parameter identification of photovoltaic array model is of great significance in the simulation of photovoltaic power generation system, the output power prediction and maximum power point tracking of the photovoltaic array. Weighted least squares (WLS), as a commonly used parameter identification optimization function, has a good recognition effect when the measurement data only contains random errors. When there are gross errors present in the measurement data, the effect of parameter identification with WLS is poor. To solve this issue, a quasi-weighted least squares (QWLS) method is proposed in this paper to reduce the influence of gross errors by using the QWLS as the optimization function, Akaike information criterion is used to design the optimal parameter of QWLS, and the method is applied in the model of photovoltaic arrays to construct robust parameter estimation problem. Finally, WLS and QWLS are combined with Sequential Quadratic Programming (SQP) algorithm to carry out the simulation and experimental testing of parameter identification of photovoltaic array model. The results show that the QWLS based robust parameter identification can achieve more accurate estimation, which further verifies the robustness and effectiveness of the quasi-least squares method.