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  • Open Access


    Parameters Identification for Extended Debye Model of XLPE Cables Based on Sparsity-Promoting Dynamic Mode Decomposition Method

    Weijun Wang1,*, Min Chen1, Hui Yin1, Yuan Li2

    Energy Engineering, Vol.120, No.10, pp. 2433-2448, 2023, DOI:10.32604/ee.2023.028620

    Abstract To identify the parameters of the extended Debye model of XLPE cables, and therefore evaluate the insulation performance of the samples, the sparsity-promoting dynamic mode decomposition (SPDMD) method was introduced, as well the basics and processes of its application were explained. The amplitude vector based on polarization current was first calculated. Based on the non-zero elements of the vector, the number of branches and parameters including the coefficients and time constants of each branch of the extended Debye model were derived. Further research on parameter identification of XLPE cables at different aging stages based on the SPDMD method was carried… More >

  • Open Access


    Application of Hankel Dynamic Mode Decomposition for Wide Area Monitoring of Subsynchronous Resonance

    Lei Wang1, Tiecheng Li1, Hui Fan2, Xuekai Hu1, Lin Yang3, Xiaomei Yang3,*

    Energy Engineering, Vol.120, No.4, pp. 851-867, 2023, DOI:10.32604/ee.2023.025383

    Abstract In recent years, subsynchronous resonance (SSR) has frequently occurred in DFIG-connected series-compensated systems. For the analysis and prevention, it is of great importance to achieve wide area monitoring of the incident. This paper presents a Hankel dynamic mode decomposition (DMD) method to identify SSR parameters using synchrophasor data. The basic idea is to employ the DMD technique to explore the subspace of Hankel matrices constructed by synchrophasors. It is analytically demonstrated that the subspace of these Hankel matrices is a combination of fundamental and SSR modes. Therefore, the SSR parameters can be calculated once the modal parameter is extracted. Compared… More >

  • Open Access


    Kalman Filter Dynamic Mode Decomposition for Data Assimilation

    Taku Nonomura

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.21, No.4, pp. 73-74, 2019, DOI:10.32604/icces.2019.05266

    Abstract In this presentation, a family of Kalman filter dynamic mode decomposition, which consists of algorithms of the linear Kalman filter DMD method which identify the linear system and the extended Kalman filter DMD method which simultaneously identify the system and estimates state variable, is introduced. Then, the application of the extended Kalman filter DMD to data assimilation is discussed. More >

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