Identifiability Analysis for Power Plant Parameter Calibration in the Presence of Collinear Parameters


IEEE Transactions on Power Systems, vol.37, no.4, pp.2988-2997, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.1109/tpwrs.2021.3130076
  • Journal Name: IEEE Transactions on Power Systems
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.2988-2997
  • Keywords: Power generation, Mathematical models, Estimation, Calibration, Analytical models, Voltage measurement, Sensitivity analysis, Power plant model validation, power plant parameter calibration, sensitivity analysis, dynamic parameter estimation, collinearity, identifiability, DYNAMIC STATE ESTIMATION, EXTENDED KALMAN FILTER, OBSERVABILITY ANALYSIS
  • Middle East Technical University Affiliated: Yes


IEEEA good quality stability model is a key factor for accurate power system operations. Inaccurate parameters of the stability models affect the decision making which paves the way for serious consequences. Thus, it is necessary to calibrate the stability model parameters in a regular manner. There are several calibration methods in the literature which are based on simultaneous estimation of the parameters and states. However, not all of the model parameters are well estimable simultaneously. Simultaneous estimation of parameters with high collinearity may result in biased calibration results. In this paper, the trajectory sensitivity method is used to detect the sensitive parameters and construct the sensitivity matrix. Then, parameters with high linear dependency are identified using the sensitivity matrix. It is shown that, despite the high sensitivity of a parameter, its estimability degrades as the collinearity with other parameters increase. In this paper an identifiability analysis that detects the collinearity among the sensitive parameters is proposed. The proposed method is validated using WSCC 9-Bus System.