An Estimation and Correction Combined Method for HVDC Model Parameters Identification

• Published in: IEEE access Vol. 9; pp. 51020 - 51028
• Main Authors: Li, Feng, Wang, Qi, Hu, Jian-Xiong, Tang, Yi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Correlation coefficient; Correlation coefficients; Direct current; gradient decent; high voltage direct current; Identification methods; Indexes; Mathematical models; Parameter estimation; Parameter identification; Parameter sensitivity; Pattern matching; Sensitivity analysis; Trajectory
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3070081

Identifying correct model parameters is important for actual power system operation and control. Though existing gradient decent method shows good timeliness, it would converge to wrong results because of inevitable linearization process when applied for strongly nonlinear models. To make up this shortcoming, an estimation and correction combined method is proposed in this paper, by which the gradient method is expected to have better initial values for avoiding the local optimum trap. In the estimation process, pattern matching is utilized based on the constructed post-disturbance trajectory based typical parameters matching database. To construct the typical parameters matching database, correlation coefficient based forward and backward cluster method is applied, with which the typical parameters matching database can be updated conveniently and quickly. In the correction process, a novel comprehensive evaluation index is put forward for gradient decent method to evaluate parameter identification effects reasonably. Finally, the proposed combined parameter identification method is verified with standard high voltage direct current (HVDC) models together with parameter sensitivity analysis, and results show effectiveness.