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

نویسندگان

چکیده

To identify the parameters of extended Debye model XLPE cables, and therefore evaluate insulation performance samples, sparsity-promoting dynamic mode decomposition (SPDMD) method was introduced, as well basics processes its application were explained. The amplitude vector based on polarization current first calculated. Based non-zero elements vector, number branches including coefficients time constants each branch derived. Further research parameter identification cables at different aging stages SPDMD carried out to verify practicability method. Compared with traditional differential method, simulation experiment indicated that can effectively avoid problems such relaxation peak being unobvious, possessing more accuracy during identification. And due less affected by measurement noise than depolarization current, results spectral line proved be better reflecting response characteristics dielectric. In addition, domain test converted into frequency domain, then used obtain dielectric loss factor spectrum insulation. integral a condition cable.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Sparsity-promoting dynamic mode decomposition

Sparsity-promoting dynamic mode decomposition Mihailo R. Jovanović,1,a) Peter J. Schmid,2,b) and Joseph W. Nichols3,c) 1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA 2Laboratoire d’Hydrodynamique (LadHyX), Ecole Polytechnique, 91128 Palaiseau cedex, France 3Department of Aerospace Engineering and Mechanics, University of Minnesota,...

متن کامل

Modal parameters identification of power transformer winding based on improved Empirical Mode Decomposition method

Modal parameters of power transformer winding are closely related to transformer manufacturing and detection technology of winding deformation based on vibration analysis method. Aimed at identifying the modal parameters of transformer winding accurately, a modal experiment is designed and made on a real 10 kV power transformer. An improved Empirical Mode Decomposition (EMD) algorithm is propos...

متن کامل

Dynamic mode decomposition for compressive system identification

Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonlinear dynamical systems via the Koopman operator. In this work, we integrate and unify two recent innovations that extend DMD to systems with actuation [56] and systems with heavily subsampled measurements [17]. When ...

متن کامل

application of upfc based on svpwm for power quality improvement

در سالهای اخیر،اختلالات کیفیت توان مهمترین موضوع می باشد که محققان زیادی را برای پیدا کردن راه حلی برای حل آن علاقه مند ساخته است.امروزه کیفیت توان در سیستم قدرت برای مراکز صنعتی،تجاری وکاربردهای بیمارستانی مسئله مهمی می باشد.مشکل ولتاژمثل شرایط افت ولتاژواضافه جریان ناشی از اتصال کوتاه مدار یا وقوع خطا در سیستم بیشتر مورد توجه می باشد. برای مطالعه افت ولتاژ واضافه جریان،محققان زیادی کار کرده ...

15 صفحه اول

A Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition

In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Energy Engineering

سال: 2023

ISSN: ['0199-8595', '1546-0118']

DOI: https://doi.org/10.32604/ee.2023.028620