For the purpose of achieving accurate on-line identification of all parameters for the electric load simulator

an improved recursive least squares (IFRLS) online parameter identification method is proposed by introducing a low-pass filter and covariance matrix readjustment to the traditional least squares algorithm. Firstly

the mathematical model of the electric load simulator was established

and the filtering method in identification process was designed. Then

based on the traditional least-squares algorithm with forgetting factor

covariance resetting was introduced to deal with the non-convergence problem. Finally

on-line parameter identification and verification experiments were conducted. The experimental results show that the non-convergence problem in the identification process was solved effectively by using the proposed improved recursive least squares algorithm

and online identification for all parameters of the electric load simulator was realized. The obtained identification model has a fit of 93.8% with the real model.