A Variable Step Size LMS Adaptive Filtering Algorithm Based on Maximum Correntropy Criterion for Identification of Low Frequency Oscillation Modes

摘要

In this paper, the adaptive filtering based on LMS algorithm is applied to the recognition of low-frequency oscillation mode. The step factor is improved based on Sinh function to accelerate the convergence speed of the algorithm while ensuring a low steady-state error, and the robustness of the algorithm in identification is improved by combining the maximum correlation entropy rule. The effectiveness of the algorithm for low-frequency oscillation mode identification is verified by simulation of the 10-machine 39-node New-England power system.