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FURTHER RESEARCH ON LOAD MODELING AND PARAMETER IDENTIFICATION BASED ON ONLINE MEASURED DATA

机译:基于在线测量数据的载荷建模和参数识别进一步研究

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In this paper, load modeling using asymmetric disturbance data has been proposed and the load model is practically simplified, meanwhile, an improved immune algorithm, namely B-cell group evolvement based immune algorithm(BGEIA), is used in load modeling to improve the operation speed and the reliability of parameter convergence. The proposed method is applied firstly in Guangdong power grid, Southern China. And the feasibility is verified by the results of the mentioned project above. Furthermore, this paper verified the accuracy of the model by means of fault recurrence. Finally, composite load model and original ZIP load model are compared in the stability analysis on section of Guangdong power grid, and the influence and mechanism to the section stability under composite load model are studied in aspects of power-angle, frequency, voltage and power. These positive results can provide the accurate instruction to grid operators.
机译:本文已经提出了使用非对称扰动数据的负荷建模,并且载荷模型实际上简化,同时,使用改进的免疫算法,即B细胞组演进的免疫算法(BGGeia),用于载荷建模以改善操作速度和参数收敛可靠性。该方法首先在中国南方广东电网施用。通过上述项目的结果验证了可行性。此外,本文通过故障复发验证了模型的准确性。最后,在广东电网截面稳定性分析中比较了复合载荷模型和原始拉链载荷模型,以及复合载荷模型中稳定性的影响和机制在电力角,频率,电压和功率方面进行了研究。这些阳性结果可以为电网运营商提供准确的指导。

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