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Regular Paper | Open Access

Identifiability Analysis of Load Model Parameters by Estimating Confidential Intervals

Xinran Zhang1Chao Lu2()Ying Wang3Qiantu Ruan4Hongbo Ye4Weihong Wang4
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
State Key Laboratory of Control and Simulation of Power System and Generation Equipment, Tsinghua University, Beijing 100084, China
School of Technology, Beijing Forestry University, Beijing 100083, China
State Grid Shanghai Electric Power Company, Shanghai 200122, China
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Abstract

The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation, analysis and control. In practical situations, the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy, which leads to the problem of identifiability. In this paper, an identifiability analysis methodology of load model parameters, by estimating the confidential intervals (CIs) of the parameters, is proposed. The load model structure and the combined optimization and regression method to identify the parameters are first introduced. Then, the definition and analysis method of identifiability are discussed. The CIs of the parameters are estimated through the profile likelihood method, based on which a practical identifiability index (PⅡ) is defined to quantitatively evaluate identifiability. Finally, the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power grid. The results show that the impact of various disturbance magnitudes, measurement errors and data length can all be reflected by the proposed PⅡ. Furthermore, the proposed PⅡ can provide guidance in data length selection in practical load model identification situations.

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CSEE Journal of Power and Energy Systems
Pages 1666-1675
Cite this article:
Zhang X, Lu C, Wang Y, et al. Identifiability Analysis of Load Model Parameters by Estimating Confidential Intervals. CSEE Journal of Power and Energy Systems, 2023, 9(5): 1666-1675. https://doi.org/10.17775/CSEEJPES.2020.02780
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