The accuracy of PV array model is very important for grid connected operation and scheduling of large scale PV system. Based on the mechanism model of PV array, the hybrid artificial fish swarm and frog leaping algorithm is adopted to identify the unknown parameters in the model. The identification results of hybrid algorithm and the results of individual identification of artificial fish swarm algorithm (AFSA) and shuffled frog leaping algorithm(SFLA) are compared and analyzed which proves that the hybrid algorithm has the advantages of the two algorithms combined and can effectively overcome the shortcomings of the two algorithms.The superiority and effectiveness of the hybrid algorithm are verified. In order to make the output of the PV array model fit well with the measured curve of any day of PV power station, the parameters of the model under different scenarios are identified by the hybrid algorithm. The adaptability of the identification results is verified by the measured data for any two days. The accuracy of the identification results is proved and the validity and practicability of the hybrid algorithm are further verified.%光伏阵列模型的准确性对大规模光伏发电系统的并网运行与调度至关重要.在建立光伏阵列机理模型的基础上,采用人工鱼群和蛙跳混合算法对模型中的未知参数进行辨识,并将辨识结果与人工鱼群算法和蛙跳算法单独辨识的结果进行了对比分析,证明了混合算法兼具两种算法的优点,并能有效克服两种算法的不足,验证了其优越性和有效性.为使光伏阵列模型的输出与实际光伏电站任意1d的实测曲线均能很好拟合,采用混合算法对不同场景下的参数进行了辨识,并采用任意2d的实测数据对辨识结果进行了适应性验证,证明了辨识结果的准确性,进一步验证了混合算法的有效性和实用性.
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