Open AccessData‐driven lumped dynamic modelling of wind farm frequency regulation characteristics
Author(s) -
Li Shaolin,
Lu Jianmou,
Qin Shiyao,
Hu Yang,
Fang Fang
Publication year - 2022
Publication title -
iet cyber‐physical systems: theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 7
ISSN - 2398-3396
DOI - 10.1049/cps2.12031
Subject(s) - frequency response , transfer function , autoregressive model , computer science , wind power , electric power system , control theory (sociology) , power (physics) , engineering , mathematics , econometrics , artificial intelligence , physics , control (management) , quantum mechanics , electrical engineering
High proportion of wind power in the power grid leads to the problem of power system frequency instability, which requires the wind farm itself to have the ability of frequency adjustment; therefore, it is particularly important to conduct modelling of wind farm frequency regulation (WFFR) response characteristics. During the modelling process, it is generally necessary to establish a model for each working condition separately, which will bring huge workload. In addition, the accuracy of the model decreases when the frequency response is non‐linear. Therefore, this paper investigates the modelling of WFFR response characteristics in different working conditions. A data preprocessing method based on WFFR strategy and modelling methods is introduced. Then, data‐based transfer function models of WFFR response characteristics for different working conditions are constructed. After that, the gaps between different models are measured using a gap metric technique to analyse dynamic similarity between models. Finally, in order to make up for the defect of transfer function models, a non‐linear autoregressive with exogenous input neural networks (NARXNN) model of WFFR response characteristics is constructed utilising lumped data of all working conditions; then, the trained model is tested by the data of each working condition to verify the accuracy and universality.