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- W4303987533 abstract "The effective online monitoring of thermal performance is the principal criterion that determines the Loss-of-Life (LoL), ageing rate, and loading capability of the transformer. To assess transformer thermal performance and life expectancy, Top-oil Temperature (TOT) and Hot spot Temperature (HST) should be accurately estimated. In general, the first-principle approach with a thermal-electrical analogy is used for the first-order dynamical model based on the Resistance-Capacitance (RC) circuit structure to approximate the evolution of thermal performance. Traditionally, the TOT model parameters are identified by looking for the input-output data that minimize the error between estimated and actual values. The Gradient Estimator (GE) based on the least-square minimization principle ensures the parameter convergence to their actual value and the parametric estimation error to zero only when the regressor (information vector) signals fulfill the stringent Persistence of Excitation (PE) condition. As the choice of input-output data plays crucial role in parameter estimation, the Design of Experiment (DoE) is generally conducted in the laboratory to satisfy the PE condition. Therefore, the TOT model parameter estimation problem is reformulated from a system identification perspective by exploring the finite-time estimators (FTEs) that accurately identify and estimate the TOT model parameters for the streaming data from real-time working transformers without any laboratory experiments and DoE. The experimental analysis on the sufficiency of data for parameter convergence is carried out on MATLAB to demonstrate the identified PE problem, the effect of DoE, and the performance of finite time estimators for real-time scenario based non-PE data in thermal modeling of the transformer. • A system identification perspective is explored for the analysis of the problem of Persistence of Excitation, input design, and parameter uncertainties in thermal modeling of transformer. • A mathematical analysis with theorem-proof for data richness and PE on LRE of Top-oil-Temperature are proved for constant loading and flat data. • The effects of insufficient data and generated PE data on Gradient Estimator are shown. • The DREM and MREM for TOT model parameter estimates outperform GE and is applied without PE." @default.
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- W4303987533 date "2023-02-01" @default.
- W4303987533 modified "2023-10-06" @default.
- W4303987533 title "Finite-time parameter estimation for an online monitoring of transformer: A system identification perspective" @default.
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- W4303987533 doi "https://doi.org/10.1016/j.ijepes.2022.108639" @default.
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