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Decision tree based validation of load model parameters

机译:基于决策树的负荷模型参数验证

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Load modeling is an important but complicated task for power system analysis and simulation. In previous research, much work has been done on load model parameter identification. However, the effectiveness of the identified load model parameters still need further validation. In practical situation, validation of load model parameters is commonly based on Post-Disturbance Simulation method, which has some inadequacies actually. Learning from the idea of load model parameter identification, a decision tree based method is proposed to validate the load model parameters in this paper. The structure and parameters of load model are selected first. Then, the parameters are discretized and represented as different classes so that the iteration of shapelet searching is carried on until all the samples are classified correctly. Finally, the decision trees are built using C4.5. Extensive simulation results in the CEPRI 36 bus system have demonstrated its feasibility and validity.
机译:负载建模是电力系统分析和仿真的重要但复杂的任务。在先前的研究中,已经在负载模型参数识别方面做了很多工作。但是,所识别的负载模型参数的有效性仍需要进一步验证。在实际情况下,负荷模型参数的验证通常基于扰动后仿真方法,但实际上存在一些不足。本文从负荷模型参数辨识的思想出发,提出了一种基于决策树的负荷模型参数验证方法。首先选择载荷模型的结构和参数。然后,将参数离散化并表示为不同的类,以便进行小波搜索的迭代,直到正确采样所有样本为止。最后,决策树是使用C4.5构建的。 CEPRI 36总线系统的大量仿真结果证明了其可行性和有效性。

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