Parameter Calibration of Wind Farm With Error Tracing Technique and Correlated Parameter Identification

With the increasing penetrations of wind power in power systems, periodical calibration of the large-scale Wind Farm (WF) model is crucial to maintaining high-quality model performance for stability investigations. However, the existing calibration methods mainly focus on correcting synchronous generators, not the more dynamical WF model. Challenges in tracing errors of inaccurate models and identifying the correlated parameters would further restrain the calibrating performance. To address such limitations, a novel parameter calibration approach for the WF model is developed explicitly in this article. With the development of a trajectory sensitivity based dominant parameters selection strategy, the parameters which significantly impact the model responses can be precisely located. Then, for discriminating the ill-conditioned parameters (deviating from their actual values), an improved error tracing method incorporating Hybrid Dynamic Simulation (HDS) and Hilbert-Huang Transform (HHT) is proposed. Lastly, to enhance the model performance, the ill-conditioned parameters are iteratively tuned by a well-designed multistage identification approach, with the consideration of parameter correlations. Additionally, theoretical proof of the error tracing technique is provided to guarantee the proposed method's validity mathematically. With realistic WF and a modified IEEE 39-bus system, the proposed method demonstrated its excellent performance in tracing and tuning inaccurate parameters.