Zhang, J, Wang, Y, Zhou, G et al. (3 more authors) (2024) Integrating physical and data-driven system frequency response modelling for wind-PV-thermal power systems. IEEE Transactions on Power Systems, 39 (1). pp. 217-228. ISSN 0885-8950
Abstract
This paper presents an integrated system frequency response (SFR) modelling method for wind-PV-thermal power systems (WPTPSs) by combining both physical model-based and data-driven modelling methods. The SFR physical model is built and simplified by the balanced truncation (BT) method. Based on the physical model, an improved radial basis function neural networks (RBFNNs) is then employed to establish an off-line SFR model using source data. Following the transfer learning principle, the transferred data from the source data set is determined by the maximum mean discrepancy (MMD) criterion. The RBFNN-based SFR model is then fine-tuned using both the transferred source data and target data. Finally, the fine-tuned RBFNNs is applied to investigate real-time SFR of WPTPSs. Simulation results confirm the effectiveness of the proposed SFR modelling strategy with an illustrative WPTPS.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Data-driven modelling; neural network; physical model; primary frequency control; renewable energy; system frequency response; transfer learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Feb 2023 13:52 |
Last Modified: | 23 May 2024 20:41 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/tpwrs.2023.3242832 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:196267 |