A parallel power system linear model reduction method based on extended Krylov subspace
•Extend the balanced truncation method to the unstable system for power system model reduction.•Enhance the convergence of the extended Krylov subspace method by adjusting the input matrix.•Boost the computational efficiency by improved Bartels-Stewart method and parallelism techniques.•Reduce the o...
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Published in | International journal of electrical power & energy systems Vol. 160; p. 110072 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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01.09.2024
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Abstract | •Extend the balanced truncation method to the unstable system for power system model reduction.•Enhance the convergence of the extended Krylov subspace method by adjusting the input matrix.•Boost the computational efficiency by improved Bartels-Stewart method and parallelism techniques.•Reduce the overall time consumption of the balanced truncation method, while the reduced-order models still retain the dynamic characteristics of the original high-dimensional power systems.
With the ever-increasing scale of power systems, stability analysis and control usually bear heavy storage and massive calculation burdens. In view of this, the model order reduction technique proves valuable by constructing a low-dimensional approximate model of the original system, which is crucial for efficiently handling large-scale systems. Balanced truncation (BT), a famous model reduction method, confronts practical limitations as it requires the systems to be stable and cannot deal with unstable models. Therefore, a parallel linear balanced truncation method for power systems based on extended Krylov subspace (EKS) is proposed in this work. Besides extending the BT method to unstable systems by α-shift, the key contribution also lies in strategies to enhance the convergence of the EKS method, whereupon the algorithm improvements include effective and efficient techniques for solving dual Lyapunov equations, and parallel acceleration of the singular value decomposition. The results of the simulation case verify that the proposed method can effectively improve the convergence of the EKS method by increasing α-shift, and the improvement work in this paper reduces the total time consumption of the BT method by about 26 %–33 % of the original, exhibiting better calculation efficiency. In addition, the case studies show that the simplified model still retains the time-domain and frequency-domain response characteristics of the original high-dimensional model. |
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AbstractList | •Extend the balanced truncation method to the unstable system for power system model reduction.•Enhance the convergence of the extended Krylov subspace method by adjusting the input matrix.•Boost the computational efficiency by improved Bartels-Stewart method and parallelism techniques.•Reduce the overall time consumption of the balanced truncation method, while the reduced-order models still retain the dynamic characteristics of the original high-dimensional power systems.
With the ever-increasing scale of power systems, stability analysis and control usually bear heavy storage and massive calculation burdens. In view of this, the model order reduction technique proves valuable by constructing a low-dimensional approximate model of the original system, which is crucial for efficiently handling large-scale systems. Balanced truncation (BT), a famous model reduction method, confronts practical limitations as it requires the systems to be stable and cannot deal with unstable models. Therefore, a parallel linear balanced truncation method for power systems based on extended Krylov subspace (EKS) is proposed in this work. Besides extending the BT method to unstable systems by α-shift, the key contribution also lies in strategies to enhance the convergence of the EKS method, whereupon the algorithm improvements include effective and efficient techniques for solving dual Lyapunov equations, and parallel acceleration of the singular value decomposition. The results of the simulation case verify that the proposed method can effectively improve the convergence of the EKS method by increasing α-shift, and the improvement work in this paper reduces the total time consumption of the BT method by about 26 %–33 % of the original, exhibiting better calculation efficiency. In addition, the case studies show that the simplified model still retains the time-domain and frequency-domain response characteristics of the original high-dimensional model. With the ever-increasing scale of power systems, stability analysis and control usually bear heavy storage and massive calculation burdens. In view of this, the model order reduction technique proves valuable by constructing a low-dimensional approximate model of the original system, which is crucial for efficiently handling large-scale systems. Balanced truncation (BT), a famous model reduction method, confronts practical limitations as it requires the systems to be stable and cannot deal with unstable models. Therefore, a parallel linear balanced truncation method for power systems based on extended Krylov subspace (EKS) is proposed in this work. Besides extending the BT method to unstable systems by α-shift, the key contribution also lies in strategies to enhance the convergence of the EKS method, whereupon the algorithm improvements include effective and efficient techniques for solving dual Lyapunov equations, and parallel acceleration of the singular value decomposition. The results of the simulation case verify that the proposed method can effectively improve the convergence of the EKS method by increasing α-shift, and the improvement work in this paper reduces the total time consumption of the BT method by about 26 %–33 % of the original, exhibiting better calculation efficiency. In addition, the case studies show that the simplified model still retains the time-domain and frequency-domain response characteristics of the original high-dimensional model. |
ArticleNumber | 110072 |
Author | Chen, Zhiying Du, Zhaobin Zhou, Ziqin Zhou, Weixian Chen, Baixi |
Author_xml | – sequence: 1 givenname: Zhaobin surname: Du fullname: Du, Zhaobin organization: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China – sequence: 2 givenname: Weixian orcidid: 0000-0001-5927-3861 surname: Zhou fullname: Zhou, Weixian email: epzhouwx@mail.scut.edu.cn organization: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China – sequence: 3 givenname: Zhiying surname: Chen fullname: Chen, Zhiying organization: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China – sequence: 4 givenname: Ziqin surname: Zhou fullname: Zhou, Ziqin organization: Jinan University – University of Birmingham Joint Institute at Jinan University (J-BJI), Jinan University, Guangzhou 511400, China – sequence: 5 givenname: Baixi surname: Chen fullname: Chen, Baixi organization: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China |
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Keywords | Balanced truncation Parallel singular value decomposition Extended Krylov subspace Power system model reduction |
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Snippet | •Extend the balanced truncation method to the unstable system for power system model reduction.•Enhance the convergence of the extended Krylov subspace method... With the ever-increasing scale of power systems, stability analysis and control usually bear heavy storage and massive calculation burdens. In view of this,... |
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SubjectTerms | Balanced truncation Extended Krylov subspace Parallel singular value decomposition Power system model reduction |
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Title | A parallel power system linear model reduction method based on extended Krylov subspace |
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