Abstract
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.
Highlights
INTRODUCTIONEffects of simulation models for theoretical research and actual operation are more and more emphasized [1], [2]
In modern power systems, effects of simulation models for theoretical research and actual operation are more and more emphasized [1], [2]
Considering requirements of the non-Euclidean distance scenario and computing efficiency, this paper proposes a correlation coefficient index based forward and backward cluster method
Summary
Effects of simulation models for theoretical research and actual operation are more and more emphasized [1], [2]. In [13], multi-stage GA is combined with sensitivity analysis for differentiated synchronous generator model parameters identification This kind of methods rely on iterative model simulations to correct model parameters, which inevitably cause large computation burden. Though artificial intelligence method computes fast, they require huge offline training time and could lose effect when operation scenario changes largely. An estimation and correction combined method is put forward for parameter identification of HVDC model, where gradient decent method is used for correction. An estimation and correction combined method for HVDC model online parameter identification is proposed, where pattern matching method is utilized to provide initial values for gradient decent method. Remaining of this paper is organized as follows: In section 2, pattern matching based model parameters estimation method is put forward together with typical parameters matching database construction method.
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