Weijun Wang^1,*, Min Chen¹, Hui Yin¹, Yuan Li²

Energy Engineering, Vol.120, No.10, pp. 2433-2448, 2023, DOI:10.32604/ee.2023.028620 - 28 September 2023

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… More >

Lei Wang¹, Tiecheng Li¹, Hui Fan², Xuekai Hu¹, Lin Yang³, Xiaomei Yang^3,*

Energy Engineering, Vol.120, No.4, pp. 851-867, 2023, DOI:10.32604/ee.2023.025383 - 13 February 2023

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 More >

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 >