Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis
Load modeling plays an important role in accessing and enhancing the dynamic stability of power systems. Though the Synthesis Load Model Considering Voltage Regulation of Distribution Network (<inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formu...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/81fc8e91cbc54776bc9bf5bb9d715312 |
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| Sumario: | Load modeling plays an important role in accessing and enhancing the dynamic stability of power systems. Though the Synthesis Load Model Considering Voltage Regulation of Distribution Network (<inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> model) has high accuracy, its parameters are too many. In order to improve the identification efficiency and reduce the difficulty of identification, a simplified model identification strategy based on parameter sensitivity analysis is proposed. Firstly, based on the global sensitivity analysis, the sensitivity analysis of the model parameters is carried out to obtain the First Order Sensitivity Indices (<italic>FSI</italic>)and Total Sensitivity Indices (<italic>TSI</italic>). Secondly, the <italic>FSI</italic> and <italic>TSI</italic> of each parameter are analyzed, and the effect on the output of model of each parameter is determined by <italic>FSI</italic>. For less influential parameters, whether the parameter should be fixed as constant is determined by the value of <italic>TSI</italic>. The parameter whose <italic>TSI</italic> equal or approximately equal to zero should be fixed as a constant. Finally, the improved genetic algorithm is used to identify the parameter-simplified model, and the effectiveness of the simplified identification strategy is verified by comparing the fitting effects with the measured curve and the residual and the integrated parameter models. |
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