Developing a new step reduction algorithm to improve the efficiency of large-scale electrical and electronic circuit simulation: Mixed Balanced Short Cutting and Riccati-Lyapunov Mixed Balanced Short Cutting
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https://doi.org/10.54939/1859-1043.j.mst.101.2025.13-22Keywords:
Model reduction; Balanced truncation; Orthogonal balanced truncation; Mixed balanced truncation; Mixed Riccati-Lyapunov balancing truncation; Large circuits.Abstract
This paper studies model reduction methods (MOR) in simulation of large-scale electrical and electronic systems, in order to reduce computational costs and optimize performance while maintaining important physical properties. In particular, two new reduction algorithms, Mixed Balanced Truncation (MBT) and Mixed Riccati-Lyapunov Balanced Truncation (MRLBT), were developed to improve efficiency compared to Balanced Truncation (BT) and Positive True Balance Truncation (PRBT) methods. Both the MBT and MRLBT algorithms preserve the stability and passivity of the original system. The paper describes in detail the steps to implement the algorithms, compares their efficiency on the RLC power network, through simulations that conduct error analysis and simulate responses on the time and frequency domains. The results show that MBT achieves a balance between accuracy and computational cost, the deceleration error is between BT and PRBT, while MRLBT has the best performance and meets the deceleration requirements among the four algorithms considered.
References
[1]. P. Benner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W. Schilders, and L. M. Silveira, “System-and data-driven methods and algorithms”. De Gruyter, (2021). DOI: https://doi.org/10.1515/9783110498967
[2]. R. W. Freund, “Electronic Circuit Simulation and the Development of New Krylov-Subspace Methods,” in Novel Mathematics Inspired by Industrial Challenges, Springer, pp. 29–55, (2022). DOI: https://doi.org/10.1007/978-3-030-96173-2_2
[3]. S.-M. Liu, L.-J. Jiang, and P. Li, “A Fast AWE-Augmented Wideband Discontinuous Galerkin Frequency-Domain Method in Solving Electromagnetic Wave Equations,” in 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), IEEE, pp. 1352–1353, (2022). DOI: https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9886356
[4]. A. Bhattacharya, P. Nandy, P. P. Nath, and H. Sahu, “On Krylov complexity in open systems: an approach via bi-Lanczos algorithm,” Journal of High Energy Physics, vol. 2023, no. 12, pp. 1–30, (2023). DOI: https://doi.org/10.1007/JHEP12(2023)066
[5]. A. K. Prajapati and R. Prasad, “Model reduction using the balanced truncation method and the Padé approximation method,” IETE Technical Review, vol. 39, no. 2, pp. 257–269, (2022). DOI: https://doi.org/10.1080/02564602.2020.1842257
[6]. M. A. Khattak, D. Romano, G. Antonini, and F. Ferranti, “Efficient Frequency and Time-Domain Simulations of Delayed PEEC Models With Proper Orthogonal Decomposition Techniques,” IEEE Access, (2023). DOI: https://doi.org/10.1109/ACCESS.2023.3347193
[7]. G. Wang, J. Yang, and J. Jiao, “Voltage correlation-based principal component analysis method for short circuit fault diagnosis of series battery pack,” IEEE Transactions on Industrial Electronics, vol. 70, no. 9, pp. 9025–9034, (2022). DOI: https://doi.org/10.1109/TIE.2022.3210588
[8]. H. R. Ali, L. P. Kunjumuhammed, B. C. Pal, A. G. Adamczyk, and K. Vershinin, “Model order reduction of wind farms: Linear approach,” IEEE Trans Sustain Energy, vol. 10, no. 3, pp. 1194–1205, (2018). DOI: https://doi.org/10.1109/TSTE.2018.2863569
[9]. P. Vuillemin, A. Maillard, and C. Poussot-Vassal, “Optimal modal truncation,” Syst Control Lett, vol. 156, p. 105011, (2021). DOI: https://doi.org/10.1016/j.sysconle.2021.105011
[10]. F. D. Freitas, J. Rommes, and N. Martins, “Developments in the Computation of Reduced Order Models with the Use of Dominant Spectral Zeros,” in Realization and Model Reduction of Dynamical Systems: A Festschrift in Honor of the 70th Birthday of Thanos Antoulas, Springer, pp. 215–233, (2022). DOI: https://doi.org/10.1007/978-3-030-95157-3_12
[11]. B. Moore, “Principal component analysis in linear systems: Controllability, observability, and model reduction,” IEEE Trans Automat Contr, vol. 26, no. 1, pp. 17–32, (1981). DOI: https://doi.org/10.1109/TAC.1981.1102568
[12]. A. C. Antoulas, Approximation of large-scale dynamical systems. SIAM, (2005). DOI: https://doi.org/10.1137/1.9780898718713
[13]. S. K. Suman and A. Kumar, “Linear system of order reduction using a modified balanced truncation method,” Circuits Syst Signal Process, vol. 40, pp. 2741–2762, (2021). DOI: https://doi.org/10.1007/s00034-020-01596-3
[14]. S. Tan and L. He, “Advanced model order reduction techniques in VLSI design”. Cambridge University Press, (2007). DOI: https://doi.org/10.1017/CBO9780511541117
[15]. T. Reis and T. Stykel, “Positive real and bounded real balancing for model reduction of descriptor systems,” Int J Control, vol. 83, no. 1, pp. 74–88, (2010). DOI: https://doi.org/10.1080/00207170903100214
[16]. P. Benner and T. Stykel, “Model order reduction for differential-algebraic equations: a survey”. Springer, (2017). DOI: https://doi.org/10.1007/978-3-319-46618-7_3
[17]. Z. Salehi, P. Karimaghaee, and M.-H. Khooban, “A new passivity preserving model order reduction method: conic positive real balanced truncation method,” IEEE Trans Syst Man Cybern Syst, vol. 52, no. 5, pp. 2945–2953, (2021). DOI: https://doi.org/10.1109/TSMC.2021.3057957
[18]. K. Unneland, P. Van Dooren, and O. Egeland, “A novel scheme for positive real balanced truncation,” in 2007 American Control Conference, IEEE, pp. 947–952, (2007).
[19]. K. Unneland, P. Van Dooren, and O. Egeland, “New schemes for positive real truncation”, (2007). DOI: https://doi.org/10.1109/ACC.2007.4282863
[20]. J. Phillips, L. Daniel, and L. M. Silveira, “Guaranteed passive balancing transformations for model order reduction,” in Proceedings of the 39th Annual Design Automation Conference, pp. 52–57, (2002). DOI: https://doi.org/10.1145/513918.513933
[21]. U. Zulfiqar, W. Tariq, L. Li, and M. Liaquat, “A passivity-preserving frequency-weighted model order reduction technique,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 64, no. 11, pp. 1327–1331, (2017). DOI: https://doi.org/10.1109/TCSII.2017.2685440
[22]. A. C. Antoulas et al., “Model order reduction: methods, concepts and properties,” Coupled multiscale simulation and optimization in nanoelectronics, pp. 159–265, (2015). DOI: https://doi.org/10.1007/978-3-662-46672-8_4