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

Author

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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)

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.

Suggested Citation

  • Chuan Qin & Yuqing Jin & Meng Tian & Ping Ju & Shun Zhou, 2023. "Comparative Study of Global Sensitivity Analysis and Local Sensitivity Analysis in Power System Parameter Identification," Energies, MDPI, vol. 16(16), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5915-:d:1214363
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    References listed on IDEAS

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    1. Soha Saad & Florence Ossart & Jean Bigeon & Etienne Sourdille & Harold Gance, 2021. "Global Sensitivity Analysis Applied to Train Traffic Rescheduling: A Comparative Study," Energies, MDPI, vol. 14(19), pages 1-29, October.
    2. Luo, Yan & Wang, Zhiyuan & Zhu, Jiamin & Lu, Tao & Xiao, Gang & Chu, Fengming & Wang, Ruixing, 2022. "Multi-objective robust optimization of a solar power tower plant under uncertainty," Energy, Elsevier, vol. 238(PA).
    3. Abbassi, Rabeh & Abbassi, Abdelkader & Jemli, Mohamed & Chebbi, Souad, 2018. "Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 453-474.
    4. Wagener, Thorsten & Pianosi, Francesca, 2019. "What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling," Earth Arxiv g9ma5, Center for Open Science.
    5. Perz, Stephen G. & Muñoz-Carpena, Rafael & Kiker, Gregory & Holt, Robert D., 2013. "Evaluating ecological resilience with global sensitivity and uncertainty analysis," Ecological Modelling, Elsevier, vol. 263(C), pages 174-186.
    6. Jian Zhang & Mingjian Cui & Yigang He, 2020. "Parameters Identification of Equivalent Model of Permanent Magnet Synchronous Generator (PMSG) Wind Farm Based on Analysis of Trajectory Sensitivity," Energies, MDPI, vol. 13(18), pages 1-18, September.
    7. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    8. Carta, José A. & Díaz, Santiago & Castañeda, Alberto, 2020. "A global sensitivity analysis method applied to wind farm power output estimation models," Applied Energy, Elsevier, vol. 280(C).
    9. Mirko Ginocchi & Ferdinanda Ponci & Antonello Monti, 2021. "Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started," Energies, MDPI, vol. 14(24), pages 1-59, December.
    10. Zhang, Feng & Wang, Xinhe & Hou, Xinting & Han, Cheng & Wu, Mingying & Liu, Zhongbing, 2022. "Variance-based global sensitivity analysis of a hybrid thermoelectric generator fuzzy system," Applied Energy, Elsevier, vol. 307(C).
    11. Thomas Guewouo & Lingai Luo & Dominique Tarlet & Mohand Tazerout, 2019. "Identification of Optimal Parameters for a Small-Scale Compressed-Air Energy Storage System Using Real Coded Genetic Algorithm," Energies, MDPI, vol. 12(3), pages 1-32, January.
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