期刊
IEEE ACCESS
卷 10, 期 -, 页码 77137-77146出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3192538
关键词
Reactive power; Parameter estimation; Voltage control; Trajectory; Power system stability; Transient analysis; Sensitivity analysis; SVG controller; parameter identification; nonlinear sensitivity; particle swarm optimization
资金
- Natural Science Foundation of Zhejiang Province [LQY19E070001]
- National Natural Science Foundation of China [U2166211]
- Information Engineering College, Hangzhou Dianzi University, through the Science Research Project [KYP012201]
This paper presents a multi-layer coarse-to-fine grid searching method for calibrating SVG dynamic model parameters using particle swarm optimization. By comparing actual measurement data with transient stability simulation results and performing nonlinear trajectory sensitivity analysis using segmented curves, potential bad model parameters are identified. Then, a multi-layer grid searching mechanism is used to narrow the parameter searching space before particle swarm algorithm optimization is applied for precise parameter identification. Experimental results show that the proposed method achieves higher accuracy and faster computation speed.
Accurate model parameters of Static Var Generator (SVG) play an essential role in regulating bus voltage profiles of power grid with increased penetration of renewable energy under various contingencies. Aiming at addressing the known issues of low identification accuracy and long computation time faced by the traditional SVG parameter identification methods, this paper presents a multi-layer coarse-to-fine grid searching approach for calibrating SVG dynamic model parameters using particle swarm optimization. First, actual measurement data is collected through SVG-RTDS testbeds under various conditions, which is compared with transient stability simulation results to check for model accuracy. Then, nonlinear trajectory sensitivity analysis is performed using segmented curves to identify potential bad model parameters. Next, a multi-layer coarse-to-fine grid searching mechanism is used to narrow the parameter searching space, before particle swarm algorithm optimization is used for more precise identification of parameters. By comparing the identification results obtained by the traditional identification methods and the proposed approach via comprehensive case studies, it is found that the proposed coarse-to-fine parameter identification method achieved higher accuracy and faster computational speed.
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