Persistence of Excitation in an Online Monitoring of Transformer

For effective online monitoring to assess thermal performance and life expectancy, Top-oil temperature (TOT) and Hot spot Temperature (HST) should be accurately esti-mated. In general, the thermal-electrical analogy is used for the first-order model based on the Resistance-Capacitance (RC) circuit structure to approximate the evolution of thermal performance. Traditionally, the TOT model parameters are identified by minimizing the error between estimated and actual values using the input-output data. The Gradient-based estimation (Least-square minimization) guarantees the convergence of the parameter estimation error to zero only when the Persistence of Excitation (PE) condition holds for regressor signals. As the choice of input-output data used for parameter identification is crucial, the Design of Experiment (DoE) is generally performed in the laboratory to satisfy PE conditions. Therefore, the TOT model parameter estimation problem is reformulated from a system identification perspective by exploring the finite-time estimators (FTE) that accurately estimate the TOT model parameters for non-PE data from real-time operating trans-formers without DoE. The experimental analysis is carried out on MATLAB to demonstrate the identified PE problem, effect of DoE, and the performance of finite time estimators for real-time scenario-based non-PE data in thermal modeling of the transformer.