Comparative Study of Global Sensitivity Analysis and Local Sensitivity Analysis in Power System Parameter Identification

Chuan Qin, Yuqing Jin (), Meng Tian, Ping Ju and Shun Zhou
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Chuan Qin: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Yuqing Jin: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Meng Tian: State Grid Yancheng Power Supply Company, Yancheng 224008, China
Ping Ju: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Shun Zhou: State Grid Yancheng Power Supply Company, Yancheng 224008, China

Energies, 2023, vol. 16, issue 16, 1-21

Abstract: In the process of parameter identification, sensitivity analysis is mainly used to determine key parameters with high sensitivity in the model. Sensitivity analysis methods include local sensitivity analysis (LSA) and global sensitivity analysis (GSA). The LSA method has been widely used for power system parameter identification for a long time, while the GSA has started to be used in recent years. However, there is no clear conclusion on the impact of different sensitivity analysis methods on parameter identification results. Therefore, this paper compares and studies the roles that LSA and GSA can play in different parameter identification methods, providing clear guidance for the selection of sensitivity analysis methods and parameter identification methods. The conclusion is as follows. If the identification strategy that only identifies key parameters with high sensitivity is adopted, we recommend still using the existing LSA method. If using a groupwise alternating identification strategy (GAIS) for high- and low-sensitivity parameters, either LSA or GSA can be used. To improve the identification accuracy, it is more important to improve the identification strategy than to change the sensitivity analysis method. When the accuracy of the non-key parameters with low sensitivity cannot be confirmed, using the GAIS is an effective method for ensuring identification accuracy. In addition, it should be noted that the high sensitivity of a parameter does not necessarily mean that the parameter is identifiable, which is revealed by the examples used in this paper.

Keywords: global sensitivity analysis; local sensitivity analysis; parameter identification; power system modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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